Shareholder Value in Banks

AARHUS UNIVERSITY, BUSINESS & SOCIAL SCIENCES

Shareholder Value in Banks

A MASTER THESIS BY

Kasper Wittrup, Msc. Finance [282606]

Jesper Agerholm Jensen, Msc. Finance [284012]

1st August, 2012

Supervisor: Christian Schmaltz

Department of Economics and Business

i

Abstract

The recent turmoil in the global banking system has illustrated that bank performance,

measured as total shareholder return, has a substantial impact on the overall economy.

Therefore, maximization of shareholder value is currently an important management

issue and should be moved to the top of the bank CEO’s agenda, not just for the bank

itself but also for the overall economy.

This thesis sets out to investigate whether drivers of value creation can be found and

whether they are implementable. It advances earlier studies of shareholder value creation

in banks on three areas. First, a comprehensive balanced panel data set for 132 banks

from 2001 – 2011 is constructed, including variables not tested among academia before.

Second, the use of listed banks makes it possible to apply total shareholder return as the

dependent variable and a measure for shareholder value creation. Finally, an operational

part, focusing on actual actions to be taken by bank managers, an area not studied by

academia, makes the thesis a valuable tool in the search for shareholder value

maximization.

The thesis suggests that by implementing a value-based management system and focus

on the most important value drivers such as ROA and revenue growth it will increase

shareholder value significantly. The findings confirm some of the findings in the key

valuation literature (Koller, Goedhart & Wessels 2010) but extents them to banks.

ii

Content

Part I – Introduction 1. Introduction.................................................................................................................... 1

1.1 Problem Statement ................................................................................................... 2

1.2 Thesis Structure and Research Design ...................................................................... 2

1.3 Delimitations ............................................................................................................. 5

1.4 Theory of Science ...................................................................................................... 5

1.5 Literature Review ..................................................................................................... 6

1.6 Data Sources ............................................................................................................. 7

2. The Importance of Shareholder Value in Banks ............................................................. 8

2.1 Measuring Shareholder Value ................................................................................... 9

2.2 Value-based Management ....................................................................................... 10

Part II – Identifying Value Drivers 3. The Business Model of Banks ....................................................................................... 14

3.1 The Balance Sheet of a Bank and Implications for Valuation ................................ 15

3.2 The Income Statement and Implications for Valuation .......................................... 16

3.3 The Regulatory Impact ........................................................................................... 17

4. The Principles of Bank Valuation ................................................................................ 19

4.1 Market-based Models .............................................................................................. 20

4.2 Equity-based Models ............................................................................................... 22

5. Value Drivers Discussed by Academia ......................................................................... 25

5.1 Profitability as the Main Value Driver ................................................................... 26

5.2 Risk and Cost-efficiency as the Main Value Drivers ............................................... 28

5.3 Bank Diversification as a Value Driver ................................................................... 29

6. The Value Drivers Discussed by Consulting Companies .............................................. 31

6.1 The Value Drivers Discussed by McKinsey ............................................................ 32

6.2 The Value Drivers Discussed by Boston Consulting Group (BCG) ........................ 34

6.3 The Value Drivers Discussed by PriceWaterhouseCoopers ..................................... 37

7. The Shareholder Value Banks ...................................................................................... 40

7.1 The Value Drivers of Value-based Management Banks .......................................... 41

8. Mapping the Value Drivers .......................................................................................... 43

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Part III – Analysis 9. The Data Set ................................................................................................................ 47

9.1 The Conditions ....................................................................................................... 49

9.2 Other Issues ............................................................................................................ 54

10. Analyses ...................................................................................................................... 55

10.1 The Academic Approach ....................................................................................... 55

10.2 The Applied Model ............................................................................................... 59

10.3 Robustness Check using Different Model Specification Methods .......................... 61

10.4 The Reduced Model Including Robustness Checks ............................................... 64

10.5 Correcting for Expectations .................................................................................. 65

11. Further Analyses ........................................................................................................ 67

11.1 Investigating TSR Performance of the VBM Banks ............................................. 67

11.2 Investigating the Top Performing Banks .............................................................. 68

11.3 Prioritizing Profitability and Growth.................................................................... 69

11.4 The Relative Importance of Value Drivers Before and During the Crisis ............. 70

11.5 Investigating Differences between US and Europe ................................................ 71

12 Constructions of the Value Driver Map ....................................................................... 73

Part IV – Operationalizing the Value Drivers 13. Operationalizing Profitability, Growth and Risk Control ........................................... 76

13.1 Value Driver 1 – ROA .......................................................................................... 77

13.2 Value Driver 2 – Revenue Growth ........................................................................ 81

13.3 Value Driver 3 – Risk Control .............................................................................. 85

13.4 Summarizing Part IV ............................................................................................ 89

Part V – Assessment and Conclusion 14. Critical Review of Results .......................................................................................... 90

14.1 Putting the Results into Perspective..................................................................... 91

15. Conclusion .................................................................................................................. 91

16. Bibliography ............................................................................................................... 94

17. Appendix .................................................................................................................. 101

iv

Exhibit list

Exhibit 1.2.1: Thesis Structure ................................................................................... 3

Exhibit 3.1.1: The Structure of Part II ..................................................................... 13

Exhibit 3.1.2: The Non-interest Income Share ......................................................... 14

Exhibit 3.1.3: Balance Sheet of Banks and Non-banks ............................................ 15

Exhibit 3.2.1: Income Statement of Banks and Non-banks ....................................... 17

Exhibit 4.1.1: The Correlation between ROE and P/B ............................................ 21

Exhibit 4.2.1: Value Drivers Extracted from Bank Valuation Models ...................... 24

Exhibit 5.1.1: The Value Drivers according to (Fiordelisi, Molyneux 2010) ............. 26

Exhibit 5.1.2: The Investigated Value Drivers .......................................................... 27

Exhibit 5.2.1: The Value Drivers Identified by (Gross 2006) .................................... 28



Exhibit 6.1.1: McKinsey’s View on Value Drivers in Banking .................................. 33

Exhibit 6.1.2: Performance and Confidence Indicators used by McKinsey ............... 34

Exhibit 6.2.1: The Relationship between CoE and RIR ............................................ 36

Exhibit 6.2.2: 40 Variables Considered by BCG ....................................................... 37

Exhibit 6.3.1: PWC’s Value Driver Categories ......................................................... 37

Exhibit 6.3.2: CoE and Sector Beta .......................................................................... 39

Exhibit 7.1.1: Selection Process ................................................................................. 40

Exhibit 7.1.2: Deutsche Bank Case Study................................................................. 42

Exhibit 7.1.3: The Most Commonly Used KPIs ........................................................ 43

Exhibit 8.1.1: Mapping the Value Drivers ............................................................... 45

Exhibit 9.1.1: Structure of Part III ........................................................................... 47

Exhibit 9.1.2: The conditions of the three estimation techniques ............................. 49

Exhibit 9.1.3: Testing for Endogeneity and Finding Suitable Instruments ............... 50

Exhibit 9.1.4: The Tests for Heteroskedasticity ........................................................ 51

Exhibit 9.1.5: The Durbin-Watson Test for Serial Correlation ................................. 52

Exhibit 9.1.6: The Standardized Residuals ............................................................... 53

Exhibit 10.1.1: The Eight Variable Categories.......................................................... 56

Exhibit 10.1.2: The Investigated Profitability Measures ........................................... 57

v

Exhibit 10.1.3: The Investigated Growth Measures .................................................. 58

Exhibit 10.2.1: The Applied Model ........................................................................... 60

Exhibit 10.3.1: The Chosen Variables from the Univariate Analysis ........................ 62

Exhibit 10.3.2: The Factor Analysis Categories ........................................................ 63

Exhibit 10.4.1: The Reduced Model with Robustness Checks ................................... 64

Exhibit 10.5.1: Controlling for Expectations ............................................................. 66

Exhibit 11.1.1: The Performance VBM Banks .......................................................... 67

Exhibit 11.2.1: Performance of Top, Medium and Low Performers .......................... 68

Exhibit 11.3.1: Prioritizing ROE and Growth........................................................... 69

Exhibit 11.3.2: Investigating Interaction Terms ...................................................... 70

Exhibit 11.4.1: Results from Pre-crisis and Crisis using FE and White Standard

Errors ........................................................................................................................ 71

Exhibit 11.5.1: 10 Year CAGR TSR across the Included Countries ......................... 72

Exhibit 11.5.2: Performance of North American and European Banks ..................... 72

Exhibit 11.5.3: Investigating the Europe Factor ....................................................... 73

Exhibit 12.1.1: The Value Driver Tree ..................................................................... 74

Exhibit 13.1.1: The Structure of Part IV .................................................................. 76

Exhibit 13.1.2: KPIs for Profitability Improvement .................................................. 78

Exhibit 13.1.3: Operational Effectiveness Measures .................................................. 79

Exhibit 13.2.1: KPIs for Achieving Revenue Growth ................................................ 81

Exhibit 13.3.1: KPIs for Enhanced Risk Control ...................................................... 85

Exhibit 13.3.2: 10 steps to cope with Regulatory Changes ....................................... 87

Exhibit 13.4.1: Operational Strategies to Improve Shareholder Value and Their KPIs

.................................................................................................................................. 89

1

Part I – Introduction

1. Introduction

“It’s only when the tide goes out that you learn who’s been swimming naked.”

- Warren Buffet

September 15th 2008 the tide went out and large banks lay naked on the beach. Lehman

Brothers filed for Chapter 11 bankruptcy, and this date marks the beginning of the end

of the glory years. The banks had been too leveraged, had filled the financial system with

risk and not considered whether return was captured based on strong business models or

high risk. A decade of deregulation had given the banks endless opportunities to seek

more value and to take on more risk. What followed was a financial crisis which likes

only had been seen during the Great Depression back in 1929 (French et al. 2011). As a

consequence, regulators have now started to tighten the requirements and therefore

totally reshaped the industry (Visali et al. 2011).

Dramatic changes need dramatic actions. In order to increase shareholder value, banks

need to adapt their business models to the changing environment. In the report “The

State of Global Banking – in Search of a Sustainable Business Model” the consulting firm

McKinsey highlights four major trends that will force European and North American

banks to change their business models:

• The new tight regulation carried forward by the Basel Comity is the single most

important factor

• The squeeze on capital funding leads to pressure on smaller banks and pressure on

deposit margins

• A huge gap between established markets and emerging markets opens up for

attractive geographic growth opportunities

• Changing consumer behavior increases the importance of delivering a unique

customer experience.

Even though the changes seem dramatic, history has shown that when a sector is

confronted with game-changing regulations there is a will and a necessity to work

through it. In the late 1990s the US government enforced similar game-changing rules in

the telecommunication industry. The changes lead to a total reshaping of their business

models, they reduced costs and staff numbers by 30-50% and improved productivity. The

banks need to be just as bold and find a way to reshape their business models in order to

adjust to “the new normal” where return on equity above 20% is no longer the industry

standard.


2

With shareholder value creation as the ultimate goal, the trade-off between risk and

return will be brought back on the CEO’s agenda. Banks need to question themselves,

what the key focus of the business models should be and how the entire organization can

be aligned to reach this goal. This is what this thesis seeks to find out.

1.1 Problem Statement

In order to get back on track, banks need to align their efforts on creating value and they

need to find out how this is done in the environment they face. Therefore, the key

research question under investigation is stated as follows:

KRQ: How should banks maximize shareholder value?

To answer the key research question the following questions need to be answered:

• RQ1: How is the value of a bank determined?

• RQ2: What are the potential value drivers of a bank?

• RQ3: Which value drivers generate most shareholder value?

• RQ4: Do banks with value-based management strategies manage what they

intend to manage?

• RQ5: How are the value drivers made manageable?

By answering the five research questions the reader will get an understanding of the full

picture of value creation in banks. Further, it will be possible to identify the value

drivers that banks need to prioritize in order to maximize shareholder value. The aim of

this study is not just to combine these two areas but to take it even further. To the

authors knowledge no academic study has yet been able to both combine theory and

practice with operational strategies on how to increase shareholder value on an

operational level.

1.2 Thesis Structure and Research Design

In order to answer the research questions, the overall structure is inspired by (Rappaport

1998) who recommends a framework that enables managers to monitor the overall value

creation and identify the activities they are to focus on. It follows four steps

1. Identify the value drivers that have the greatest impact on value.

2. Isolate the drivers that management can influence.


3

3. Develop a value driver map of the business

4. The value drivers should be disaggregated in order to operationalize the value

drivers and make it possible for all employees to understand how they create

value.

Exhibit 1.2.1: Thesis Structure

Source: Own contribution

Inspired by the thoughts of (Rappaport 1998) the thesis initially starts with discussing

the application of the shareholder value view, and the existing research on shareholder

value in banks.

Shareholder value management has for many years been a dominating management

concept and a performance indicator for companies all over the world. However, more

than twenty years after the groundbreaking book by (Copeland, Koller & Murrin 1991)

still only few articles have discussed shareholder value management in connection to

banks and none of these have had both an internal and an external view. To fill this gap

Part I will be a discussion regarding shareholder value and value-based management.

When applying this approach in banking, the measurement of shareholder value is

necessary and will therefore also be addressed in Part I.

In Part II the objective is to give an overview of the current thinking within shareholder

value in banking and identify the value drivers that are to be included in the regression

models in Part III. It will begin with an assessment of the preferred bank valuation

models. By finding the models that explain how to value banks the hypothesis is that the

Shareholder value

Consultants

Academia

Case study of VBM banks

Bank Valuation

Research question

Value-based management

Multiple analysis

Cash flow-oriented

Equity DCF

Dividend discount model

P/B

P/E

Part II

Company N

Company 1Concept

VD1

VDn

Article N

Article 1

Concept

VD

VDn

RQ1: How is the value of a bank determined?

RQ2: What are the potential value drivers of a bank?

Bank N

Bank 1 KPI1

KPIn

...

Analysis

Data selection

Value drivers from Part II

Part III

Methodology

Regression and test

Empirical findings

Study of case banks

Making KPIs operational

RQ3: Which value drivers generate most

shareholder value?RQ4: Do banks with

VBM strategies manage what they intend to manage?

RQ5: How are the value drivers made

manageable?

Identifying value drivers

Introduction

Shareholder value in banks

Why share-holder value?

Introduction Part I

Operational strategies Part IV

Research design

Concept

Residual income

VD

VD

VD

VD

VD

VD

VD

VD

...

...

... ...

...

VD


4

drivers behind these models will also be possible value drivers for banks. The models

discussed will be both peer group multiples and equity-based models. This chapter will

also provide an answer to RQ1 and knowledge regarding how the value of a bank is

determined.

Following the valuation chapter the focus turns towards identifying the value drivers

found relevant in academia and by practitioners. Starting with academia, a limited

number of articles1 have been written on the subject, the most well-known being

(Fiordelisi, Molyneux 2010) and none of them have used Total Shareholder Return

(TSR) as their dependent variable as done in this thesis. This is followed by an analysis

of the largest management consulting houses and their view towards shareholder value

creation among banks. Finally, 20 case banks who have implemented value-based

management are included in the analysis. The case banks are chosen trough an

investigation of the sample banks and the screening is conducted using some clearly

defined screening rules. Through case studies of shareholder-focused banks and a careful

study of the literature the main goal of Part II is to provide an answer for RQ2.

Part III of the thesis contains a regression analysis with the research objective of finding

the value drivers that maximize shareholder value and thereby answering RQ3. Such an

empirical study aims to give a better understanding of value creating drivers and thereby

provide valuable insights for bank managers on how to manage the bank. For this, a

balanced panel data set is used and through comprehensive analyses that considers and

corrects for elements such as heteroskedasticity, endogeneity etc. the final model will

yield insight into the value drivers that have a significant influence on the dependent

variable. Further, it is examined both whether value-based management banks

outperform non-value-based management banks based on TSR and whether top-

performing banks outperform the other banks on the key value drivers. The former is

done to investigate RQ 4. Next, differences between European and North American

banks yield information regarding any unobserved effects in the different regions. In order

to build further knowledge about the value drivers, additional analyses such as

robustness tests and expectation correction tests suggested by (Koller, Goedhart &

Wessels 2010) and (Jiang, Koller 2007) supports the construction of a comprehensive

value driver tree

In Part IV an ending discussion will provide the reader with some operational actions to

be taken in order to affect the main value drivers. This is done through a breakdown of

the value driver tree constructed in Part III, by analyzing identified top-performers and

carefully study the prevailing strategies within management consulting. Part V will

conclude upon the findings and address the KRQ.

1 A quite large number of German articles have focused on shareholder value in connection to banks, but this study have

only focused on those written in English


5

1.3 Delimitations

The thesis will limit itself to only focus on banks within the European and North

American area. This limitation is made due to the differences that are seen among

developed world banks and emerging market banks (Dayal et al. 2010). Further, it has

been necessary to make limitations on the banking type. Banks that secure less than 30%

of their revenues in the retail banking segment are not considered for this thesis which

allow drawing more consistent conclusions. Next, TSR is seen as the best measure for

shareholder value and therefore only listed banks are considered.

Furthermore, the regulatory requirements are expected to influence the stock price since

capital requirements and restrictions on the calculation of risk-weighted assets directly

influences the profitability of banks. However, since it is not possible for bank managers

to affect the regulatory requirements, it is not within the scope of this study.

Finally, the recent financial crisis has undoubtedly had a huge effect on the shareholder

value generation in the banks. The thesis will however not contain an extensive

discussion on the financial crisis since it has received a large amount of attention in

recent literature (French et al. 2011).

1.4 Theory of Science

Scientific studies and research is affected by the paradigm of the researcher and how the

researcher views the world. Since the paradigm will influence everything in a study from

problem statement to data collection and analysis it is important to clarify the paradigm

applied (Arbnor, Bjerke 1997).

The main paradigm of the thesis is one closely related to the analytical paradigm

described by (Arbnor, Bjerke 1997). This paradigm sees the world objectively and tries to

explain cause-and-effect relationships as they exist in the real world. However, also the

radical structural paradigm inspired by (Bell, Bryman 2003) is applied when analyzing

specific strategies followed by the case banks and in determining whether a bank uses

value-based management or not. This research obviously causes the interpretation to be

influenced by the authors’ paradigm.

Given the paradigm, the study will use econometric methods that give the best

approximation of the real world. The statistical analysis will show a cause-and-effect

relationship as it exists in the real world and the influence of the researchers is therefore

minimal, which is in accordance with the analytical paradigm.

The analysis of the case banks and their strategies relies heavily on qualitative methods.

The case banks are chosen based on a subjective evaluation of their management system

and here the radical structural paradigm comes into play (Bell, Bryman 2003). The

conclusions drawn from the analysis of the case banks relies on the view of the


6

researchers. Since the qualitative part is included in order to add another dimension to

the analyses it is believed that it will add value to the study.

The results from the quantitative and the qualitative part of the research will in total

lead to results that will shed light on how banks should prioritize their value drivers to

maximize shareholder value.

1.5 Literature Review

The main sources of inspiration are (Koller, Goedhart & Wessels 2010), (Dermine 2008)

and (Rappaport 1998) and their work on the importance of shareholder value and the

value creation process. The shareholder value view that dominates the thesis was

introduced by (Rappaport 1981) as he questioned whether accounting numbers was the

best measures for company performance.

The literature used for the valuation models in Part II is especially focused around

(Koller, Goedhart & Wessels 2010, Damodaran 2009, Dermine 2009) and (Gross 2006)

where the first two are considered gurus within corporate valuation. Turning towards

academia, the literature on shareholder value creation in banks is scarce since most is

still focusing on accounting numbers and banking profitability. Key literature on the

subject includes (Gross 2006), (Fiordelisi, Molyneux 2010) (Baele, De Jonghe & Vander

Vennet 2007) and (Rapp et al. 2011). As seen in Section 1.2 this thesis also takes an

internal view where key literature is found among the management consulting houses.

Especially (Visali et al. 2011, Dayal et al. 2010, Duthoit et al. 2011, Dayal et al. 2011,

Leichtfuss et al. 2010, Maguire et al. 2009) together with numerous annual reports from

the case banks have been applied.

In relation to the statistical analysis in Part III a wide range of key literature is used.

(Wooldridge 2002), the most extensive book on the subject of panel data analysis, has

together with (Baltagi 2001) been used as an important guideline for the statistical

analysis while (Wooldridge 2009) and (Verbeek 2009) have been indispensable in relation

to delivering practical and statistical guidance for the analysis.

Further, (Koller, Goedhart & Wessels 2010), which follows the ground breaking work of

(Copeland, Koller & Murrin 1991) has conducted key research on how value is created in

non-financial companies and is a major source of inspiration to some of the more

thorough analyses.

For Part IV, mainly articles from McKinsey & Company and BCG have been applied

since academia does not discuss their findings on such an operational level.


7

1.6 Data Sources

The thesis relies 100 percent on publicly available information about the banks in the

sample. The primary data sources are Bloomberg, Bank Scope, Datastream and annual

reports. The Bank Scope database was used in the selection of the 132 banks because of

its use of standardized variables but unfortunately the database almost contained no

data before 2005. Therefore the data was primarily drawn from Bloomberg which also

provides standardized data. Datastream was primarily used to get the macroeconomic

data for the analysis.

Also annual reports have been an important data source for several reasons. First of all,

the data provided by Bloomberg was not 100% sufficient. However, the effort put into

gathering data has resulted in a unique balanced panel data set containing data for all

included variables for all banks for the years 2001 to 2011. Secondly, the annual reports

were an important data source in the identification of the value-based management

banks. For a full view of the data selection process see Appendix 17.2.1.


8

2. The Importance of Shareholder Value in Banks

The origins of the shareholder value view can be dated all the way back to economist

Adam Smith. In The Wealth of Nations Adam Smith argued that the individuals pursue

of wealth will create a capitalistic society for everyone’s benefit. The argument went that

if everyone seeks to maximize his own benefits, capital will be distributed to the best

investments. Even though it may not be in the individuals’ interest to promote public

interest, he does so anyway through his allocation of capital (Smith 1776). However, the

shareholder value view started gaining significant support during the 1980’s and 1990’s

and is today considered the dominant corporate objective (Shukla 2009). According to

(Young, O'Byrne 2001) the following major developments have lead to the increasing use

of shareholder value:

• Globalization and deregulation

• The end of capital and exchange controls

• Advances in IT

• More liquid securities markets

• Improvements in capital market regulation

• Generational changes in attitudes towards savings and investments

• The expansion of institutional investments

As discussed in the introduction, the lack of focus on both profitability and the cost of

capital have been the key sources to the financial crisis. By introducing shareholder

value-based management, this problem will be avoided. (Koller, Goedhart & Wessels

2010)

Even though the principles of shareholder value have gained ground, some articles argue

that it comes at the cost of the stakeholders. However, in relation to the central

argument of Adam Smith, maximizing shareholder value does not only benefit the

investors, it also creates growth and wealth in the economy through more jobs, employee

benefits, more CSR spending, more focus on environmental issues etc. Hence shareholder

value and stakeholder value are not competing ideas as much of the literature suggests

(Rappaport 1981). Further, (Jensen 2001) is even more radical since it argues that the

whole idea of stakeholder maximization is flawed as it involves maximizing more than

one measure. According to (Jensen 2001) shareholder value maximization is the only

objective that makes sense since it involves maximizing only one measure. Other

complications regarding shareholder value creation have been suggested by (Kay 2010).

(Kay 2010) claims that a key deficiency of shareholder value management is the fact that

it causes managers only to focus on the next earnings announcement. (Koller, Goedhart


9

& Wessels 2010), however, stresses that the argument is flawed due to the fact that value

is determined by the long-run expectations and not short-term earnings.

In the discussion of shareholder value in banks, it is suggested by (Gross 2006) that even

though limited articles have focused on shareholder value in banks, the creation of it is

even more important for banks than for regular companies. Banks are required to secure

growth through equity which is easier gained if investor can see that they are rewarded

(Gross 2006). However, due to the important role of banks in the economy and the

complexity of bank valuation there is still a large skepticism when it comes to

shareholder value maximization in banks, skepticism that after the crisis only has

increased (Gross 2006). This is in line with (Porter, Kramer 2011) that proposes the

creation of shared value as the new main objective of the company. According to (Porter,

Kramer 2011) the central argument in shareholder value is outdated due to the narrow

focus.

Even though the authors acknowledge a few of the deficiencies presented in (Porter,

Kramer 2011) the arguments by (Rappaport 1981) and (Gross 2006) is supported more

strongly and shareholder value will still be the predominant methodology throughout this

thesis.

2.1 Measuring Shareholder Value

Having discussed the importance of shareholder value the next question is how to

determine it. Even though various measures are used in the shareholder value literature

the most direct measure is the TSR measure:

Total Shareholder Return�(TSR�) = Stock Price� + Dividend�Stock Price��

− 1

It is however discussed, that one major weakness of TSR as a shareholder value measure

is the stock market expectation affection and some practitioners are therefore reluctant to

use it (Gross 2006). Therefore (Koller, Goedhart & Wessels 2010) examines different

methods to correct for expectations and finds that they become insignificant over a

longer time span.

A greater weakness is however, that while the total shareholder return belongs to the

market measure category, a measure for unlisted companies needs to be found. For this,

the consultancy firms have developed a wide range of residual income measures (Young,

O'Byrne 2001). Economic Value Added (EVA) is such a residual value measure,

developed by the consulting company Stern Stewert & Co and among the most widely

used measures of shareholder value (Young, O'Byrne 2001). According to (Young,

O'Byrne 2001) the major benefit of EVA is that it can be calculated and implemented at

all levels of a company. EVA equals the spread between return on net assets and the cost

of capital, multiplied by invested capital:

EQ 1


10

EVA = (RONA – WACC)·Invested capital

Another popular measure is the economic profit measure put forward by (Koller,

Goedhart & Wessels 2010) which is very similar to the EVA model

EP = (ROIC - WACC)·Invested capital

This model has gained in popularity over the last years because of its close relation to

economic theory and a company’s competitive strategy (Koller, Goedhart & Wessels

2010). It highlights whether the company is capable of earning more or less than its cost

of capital. The key issue with the two models is however, that they fail to account for

synergies between the different business units of a company. They therefore have some

limitations when it comes to representing the total value generation in a company

(Young, O'Byrne 2001).

Several studies have found that residual income models are capable of explaining some of

the variance in TSR but the amount is limited. In the studies conducted by (Gross 2006)

Economic Profit is used as the preferred shareholder value measure, since (Gross 2006) is

working with non-listed banks. However, since total shareholder return is preferred and

the data is available, this thesis only uses the Economic Profit variable as an explanatory

variable.

2.2 Value-based Management

As has just been seen, shareholder value is focusing on how much value is generated to

the owners. Such an increasing focus on the owners has compelled bank managers to

adopt new management approaches that are designed to fit this change. Value-based

management is developed exactly with this purpose in mind since it focuses on

shareholder value creating activities throughout the whole organization (Ameels,

Bruggeman & Scheipers 2002). (Dermine 2008) defines value-based management (VBM)

as:

“Value-based management refers to the corporate objective of increasing the wealth of

shareholders of a corporation”

- (Dermine 2008)

In order to efficiently work on the ultimate goal of creating value for the shareholders a

value-based management system should use the most important value drivers as key

performance indicators (KPIs) and use operational KPIs that supports (Rappaport,

1998).

In accordance with (Rappaport, 1998), this thesis defines value drivers as the main

components of shareholder value and KPIs as the strategic and operational measures

implemented to support the VBM system in ensuring shareholder value maximization.

EQ 3

EQ 2


11

When using VBM, managers and employees are encouraged and measured on their

ability to create shareholder value through the use incentive systems and KPIs

(Rappaport 1998). The ideas behind the concept have risen from the agency theory which

focuses on the relationship between the agent and the principal. It is the agent’s

obligation to fulfill the principal’s objectives because of the economic relationship they

have. Since agents are moved by their self-interest it is necessary to align the two parties

interests in order to secure that they share a common goal. In theory there are two

problems that can disturb this alignment of interest, the agency problem and the

problem of risk sharing (Ameels, Bruggeman & Scheipers 2002). The first is based upon

the assumption that the agent and the principal’s interests and goals might conflict and

that it is difficult or too expensive for the principal to monitor the agent (Eisenhardt

1989). Secondly, risk sharing assumes that there is a difference between how the two

parties are affected by different risk structures and therefore differs in the way they act

(Shankman 1999). In summary both of these two issues between management and the

shareholders are the corollary of a lack of goal congruence between the objectives of the

agents and principals. By letting managers think like shareholders, Value-based

management reduces this lack of goal congruence. Therefore, VBM should be

implemented as an overall management tool that enables this. (Pitman 2003)

2.2.1 VBM Implementation

(Rappaport 1998) acknowledges the huge task at hand when a company starts

implementing a VBM system since it requires a complete corporate transformation.

Important implementation lessons can however, be learned by the study of Lloyds bank

who implemented VBM. The CEO, Brian Pitman, identified the following five steps in a

successful VBM implementation (Pitman 2003):

1. Define one objective: The first thing that needs to be done is to reach agreement

in the management of the bank. The management should agree that shareholder

value creation is the priority. A starting point that is also acknowledged by

(Young, O'Byrne 2001).

2. Establish stretch goals: It is important to be very ambitious when setting the

goals. Lloyds set out to be the best shareholder value generator in the financial

sector, and they reached the goal. After this they started benchmarking

themselves against the best shareholder value generator at the time, Coca Cola.

3. Align reward system behind goals: The use of incentive systems is crucial to a

successful VBM implementation. In connection to this (French et al. 2011)

highlights some important issues in relation to the systemic importance in banks.

It is recommended to use long-term reward systems to prohibit inappropriate

short-term maximization. The use of long-term reward systems is also supported

by (Young, O'Byrne 2001).


12

4. Cultivate learning and change minds: VBM needs to be a part of the culture at

the bank. This step is supported by both (Rappaport 1998) and (Young, O'Byrne

2001) who confirm the importance of shareholder value understanding and

commitment.

5. Make the difficult choices: If a business segment for several years has delivered

poor shareholder value results then the business segment should be shut down.

However, before a bank is ready for an implementation of VBM, the first task, according

to (Rappaport 1998), is to find those value drivers that have the greatest impact on

shareholder value. Part II will identify these value drivers and Part III will figure out

which of them to focus on.

13

Part II – Identifying Value Drivers

The purpose of Part II is to yield an understanding of the banking business model, define

the current thinking on bank valuation and identify the value drivers that are to be tested

in Part III. Exhibit 3.1.1 summarizes the structure of Part II.

Exhibit 3.1.1: The Structure of Part II


Focusing on the value drivers from an operational and strategic angle, the objective is to

permeate the bank with a strategy that is centered on shareholder value creation both in

terms of decision making and resource allocation (Gross 2006). However, before

identifying the value drivers of banks a detailed description of the banking business

model and the differences between banks and non-banks will be provided in Chapter 3.

Chapter 4 follows with a discussion regarding which bank valuation models external

analysts apply in the estimation of a fair value of the bank, and the value drivers that

can be derived from these models. Afterwards, a literature review of the academic articles

focusing on creation of shareholder value in banks will be given in Chapter 5. Those two

groups have an outside-in perspective while management consulting companies and

banks, discussed in Chapter 6 and 7 respectively, are able to make internal analyses.

Finally, Chapter 8 will end Part II by addressing whether there is a consensus across the

different types of literature and which value drivers that will be incorporated in Part III.

Identifying these value drivers is an importance step towards making managers capable

of managing the bank with shareholder value as a main goal (Rappaport 1998).

Chapter 3 The Business Model of Banks

Chapter 4 Bank Valuation

Chapter 5Academia

Chapter 6 Consulting

Chapter 7 VBM Banks

Chapter 8 Mapping the Value Drivers

Understanding Banks

External KPIs

Internal KPIs

Conclusion


14

3. The Business Model of Banks

Banks are very different from non-banks when it comes to the construction of their

business model (Gross 2006). Looking at the products, non-banks in general occurs risk

as a side-effect of doing business while banks has managing, incurring, structuring and

assessing financial market risk as one of their core business activities. By taking this risk,

they provide the service of storing value and extending credit, acting as intermediaries

between parties with funding surpluses and deficits (Koller, Goedhart & Wessels 2010).

This gives banks a very central function in the modern economy but it also makes them

heavily dependent of the overall economy.

However, in the later years this typical model has evolved and today’s universal banks

have a whole palette of activities in their portfolio (Koller, Goedhart & Wessels 2010).

Especially proprietary trading activities in well-known products such as equity stocks,

foreign exchange, bonds etc. or more exotic products such as credit default swaps or

asset-backed securities have become a larger part of large banks income (see Exhibit 3.1.2

interest income vs. non-interest income). Also non-banking activities are an increasing

part of banking income, a strategy that has evolved from their focus on increasing

number of customers and income per customer. By creating synergies between different

financial areas such as real estate, insurance, pension products etc. banks have been able

to increase total income. Even though this shifting in the bank’s business model has

increased its diversification it has also increased the volatility and cyclicality and gains

made over several years can be wiped out in a single year (Koller, Goedhart & Wessels

2010).

Exhibit 3.1.2: The Non-interest Income Share

Source: Bloomberg, own contribution

0

200

400

600

800

1000

1200

1400

1600

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

billion USD

Non interest income Net interest income


15

Despite these great uncertainties in the business model, factors like long-term contracts,

high percentage of existing lending relative to new lending and the law of large numbers

makes analysts capable of giving reliable estimates, and their valuation models an

indicator of which KPIs bank managers are to focus on. (Gross 2006)

3.1 The Balance Sheet of a Bank and Implications for Valuation

One of the core differences between bank and non-bank balance are their balance sheets.

Since the financial risks, that banks occur, affects both assets and liabilities it is

important for them to focus on both sides in order to run their business (Gross 2006).

Exhibit 3.1.3 outlines the key differences.

Exhibit 3.1.3: Balance Sheet of Banks and Non-banks

Assets Non-

banks Banks Liabilities

Non-

banks Banks

PP&E 25% 1% Equity capital and reserves 18% 4%

Investments 13% 2% Provisions 20% 1%

Inventories 23% N/A Liabilities 62% 91%

Receivables 33% 74% - Trade payables 12% N/A

- From customers 15% 49% - Liabilities, fin. institution 20% 29%

- From credit institutions N/A 25% - Liabilities, non-banks N/A 38%

- Other receivables 18% 0% - Securitised loans N/A 23%

Investment securities 3% 19% - Other liabilities 31% 5%

Cash and Cash Equiv. 4% 1%

Other assets 0% 2%

Source: (Gross 2006), own contribution

3.1.1 The Assets Side

Where some of the large entries in non-banks are property, plant & equipment and

inventories the asset side in banks are mainly dominated by receivables from either

customers or other credit institutions. Further, the tangible assets and capital

expenditures in banks are of minor importance since human capital and intangibles like

brand and employees are the main cost drivers and therefore booked in the income

statement (Gross 2006). One practical way to deal with such an issue is to estimate

investments in intangibles and then adjust the income statement and balance sheet by

capitalizing these investments (Damodaran 2009). For analysts to estimate working

capital they fall into another type of problem. When working capital is defined as the

difference between current assets and current liabilities a large proportion of the bank’s

balance sheet falls under one of these two categories. The changes are often both large

and volatile and may have no relationship to reinvestments. If it is not possible to

directly identify capital expenditures and changes in working capital it is not possible to

estimate cash flows either (Damodaran 2009). All in all, when not available, numbers


16

have to be estimated by the analyst, making estimated cash flow less reliable (Koller,

Goedhart & Wessels 2010).

Another area to focus on when valuing banks is the fact that assets are traded in the

market and often not held to maturity which is why the marked-to-market principle is

often applied (Damodaran 2009). Besides increasing volatility it creates at least one

problem. Comparison and interpretation of ratios (e.g. P/B or ROE) between banks and

non-banks is almost impossible. If ROE is taken as an example: In non-banks this would

be a measure of the return earned on equity invested in the company but that

information is missed due to the marked-to-market principle. In fact if the assets were

truly marked-to-market ROE would equal the cost of equity (Damodaran 2004).

3.1.2 The Liability Side

Opposite to most industrial companies, banks are capable of earning money on the

liability side as well as the asset side (Damodaran 2009). Normally only assets drive the

value but with the deposit franchise given by the government, banks can issue deposits at

costs lower than the cost of raising an equivalent amount of funds with equal risk in the

open market. This positive spread on the liability side creates value for the shareholders

and is the reason why liability management should be seen as part of a bank’s business

operations when valuing them. If it was purely financing, the bank would be paying

market rates for the deposits and only through the tax shield would shareholders gain

value (Copeland, Koller & Murrin 2000). This also explains the high leverage that banks

have, with equity capital only making up 4% of the total liability side. As a result of this

structure, banks can only be valued correctly if all financing activities are included in the

valuation model. From an outside-in perspective it is, however, difficult to determine the

function of debt (Gross 2006).

A further note on the equity is its role as liquidity which in banks is not just residuals of

the production process. For a bank, the liquidity kept on its books plays a key role as

input factor into its banking business, making cash flows very volatile and difficult to

forecast (Gross 2006).

3.2 The Income Statement and Implications for Valuation

From a valuation point of view, it is important to understand the main driver of revenue,

interest income. Most banks experience a maturity miss-match on their balance sheet as

a result of short term deposits and long term lending and therefore not all interest

income creates value (Koller, Goedhart & Wessels 2010). In a situation with maturity

miss-match the bank do not earn an excess interest spread due to their value adding

activities but due to being on different parts of the yield curve and therefore taking more

risk, see Appendix 17.1.1 for a more thorough discussion of the value creating income and

how it has evolved in the last decade. From Exhibit 3.2.1 the differences between

banking and non-banking income statements can be seen.


17

Exhibit 3.2.1: Income Statement of Banks and Non-banks

Revenues Non-

banks Banks Expenses

Non-

banks Banks

Sales 93% N/A Supplies expense 61% N/A

Change in inventories 1% N/A Staff expense 17% 10%

Interest income 1% 83% Other adm. expenses N/A 7%

Inc. from provisions N/A 7% Depreciation 4% 7%

Inc. from sec. & inv. N/A 4% -Fixed Assets & intangibles 4% 1%

Net inc. financing business N/A 1% -Provision on receiv. & sec. N/A 6%

Other income 5% 5% Interest expense 2% 71%

Tax charges 3% 1%

Other expenses 14% 4%


Another important income driver is the increased focus on proprietary trading which has

also increased the risk through increased volatility and made it challenging for external

analysts to estimate the internal positions. Finally, a typical banking income is fee and

commissions when the banks are either advising or servicing their customers. Services

that include deposits transport of money, exchange, provision of liquid funds etc. (Gross

2006).

On the expense side a structural characteristic of banks is their high share of fixed costs

due to storage of both human capital and advanced IT systems. Together with variable

costs like interest expenses (similar to COGS in non-banks), and provision for loan losses,

this is what mainly drives expenses (Gross 2006). Especially the loan loss should be

handled with care. The probability of default fluctuates significantly during a business

cycle. However, rather than writing of loans as they default, banks make provisions for

losses and average out the large changes (Damodaran 2009). This of course comes in

handy when making valuation but it is a subjective decisions whether banks are

conservative and set aside a large amount for loan losses or aggressive. In an estimation

of future loan losses there are three ways to go, either analysts use sector wide estimates,

the banks historical provisions (to see whether it is conservative or aggressive) or a

combination of both. (Damodaran 2009)

3.3 The Regulatory Impact

All over the world banks are regulated due to their important role in the economy and

their dependency on economic cycles (Visali et al. 2011). Although the regulations differ

from country to country there are some typical requirements that recur. The typical

forms of bank regulation covers capital requirements put forward by the Basel

Committee on Banking Regulations and Supervisory Practices. Capital ratios are

calculated based on the banks risk-weighted assets and should ensure that neither

claimholders nor depositors are at risk. Besides increasing costs, these capital


18

requirements also represent a bottleneck in the banks’ growth opportunities and therefore

have to be considered both in connection to valuation and in the testing of KPIs (Gross

2006). In the following sections, the two largest regulatory changes will be discussed. For

a more thorough discussion of the regulatory landscape, see Appendix 17.1.2.

3.3.1 The Impact on Liquidity and Capital Risk

After the crisis few industry experts would disagree that tighter capital requirements is

desirable in order to keep a more stable future for both the banking industry and the

overall economy (French et al. 2011). However, since it is a large driver for profitability

both analysts and banks will have to cope with these changes and academia has to

incorporate it in the studies. (Visali et al. 2011)

The impact from the new regulatory constraints on capital will have a triple impact on

the banks: An increase in the core tier 1 capital ratio, stricter rules on how to calculate

the core tier 1 capital ratio and changed weights in the calculation of RWA (Basel

Committee on Banking Supervision 2010). Especially two constraints, that affect RWA,

have been added after it was clear that the capital requirement on trading books where

not high enough to absorb all the trading losses from market fluctuations (Leichtfuss et

al. 2010). These constraints are counterparty- and market risk, and they impact RWA

through increased Value-at-Risk. From these changes the largest banks’ (263 of the

largest banks European and North American banks) capital ratios are, with no changes

on the balance sheet, expected to fall from 11.1% to 5.7% whereas RWA is expected to

increase by 23 % (Basel Committee on Banking Supervision 2010). Shifting towards a

less capital intensive business model is therefore key in avoiding the increased regulatory

constraints (Visali et al. 2011).

During the crisis, the inefficient allocation of liquidity costs highlighted how ineffective

many banks’ liquidity risk management systems were. This was mainly due to the low

focus on liquidity risk from a regulatory perspective (Leichtfuss et al. 2010). Basel III,

however, introduces from 2019 two new liquidity requirements that banks need to fulfill,

the liquidity coverage ratio (LCR) and net stable funding ratio (NSFR). (Basel

Committee on Banking Supervision 2010) estimates that global banks will need to raise

an additional 1.7 trillion EUR in liquid assets just to comply with the LCR. Looking at

NSFR, which are to limit the maturity mismatch discussed earlier, it will trigger even

larger fundamental changes. It is expected that global banks with no changes on their

balance sheet would have to add 2.9 trillion EUR2 of additional long term funding to

their balance sheet. In Europe alone the equivalent number is 1.8 trillion EUR due to

their high reliance on wholesale funding3 (Basel Committee on Banking Supervision

2010).

2 It should however be mentioned that the two numbers are not additive since a shortfall in one of them affects the other

and improvement will likewise affect both numbers 3 Wholesale funding increases the loans to deposit ratio


19

4. The Principles of Bank Valuation

Surprisingly few studies have focused on bank valuation and the determinants that drive

market value in banks. Even in one of the most accepted valuation books “Valuation –

measuring and managing the value of companies” by (Koller, Goedhart & Wessels 2010)

only one chapter is devoted to the subject and in the chapter intro it is written “Banks

are among the most complex businesses to value, especially from the outside in. Published

accounts give an overview of a bank’s performance, but the clarity of the picture they

present depends largely on accounting decisions made by management”(Koller, Goedhart

& Wessels 2010). Maybe because of this great complexity another author writes “There

is still a lack of comprehensive coverage of this subject [bank valuation]. Moreover, the

valuation literature that does focus on banks has rarely been practical and theoretical

satisfactory at the same time” (Gross 2006).

In the valuation of banks several factors need to be addressed which might be why

standard literature only focuses on valuating industrial companies (Gross 2006). Banks

have in the recent years started expanding their potential customer base and increased

their business scope. This has increased the complexity greatly, making it necessary to

use different valuation models for their different divisions (Koller, Goedhart & Wessels

2010). Yet, a detailed income and balance sheet that is divided into these different

business units is very rare and subjects such as transfer pricing, capital allocation and

synergies created through cross-selling are difficult to estimate (Koller, Goedhart &

Wessels 2010). Especially if some products are kept due to their synergies even though

income margins are low or negative. Further, due to a very non-transparent balance sheet

and income statement, as discussed in Chapter 3, it is difficult to define entries such as

debt, capital expenditures, working capital etc. which makes the estimation of cash flows

(the core in DCF-valuation) difficult (Damodaran 2004). Also, accounting rules that

apply to banks are often different from those that apply in non-financial companies which

yields the problem, also mentioned by (Koller, Goedhart & Wessels 2010), that

performance largely depends on accounting decisions made by management. Further,

“normal” bank products are often not patented and difficult to differentiate which makes

forecasting even more difficult than the above mentioned complications suggest. Finally,

banks are due to their economic importance often heavily regulated which is something

that must be incorporated in the models. (Koller, Goedhart & Wessels 2010)

Since the value found in valuation models can be seen as a proxy for the market value

and TSR (Koller, Goedhart & Wessels 2010) this chapter will discuss the value drivers

that affect the valuation models. In Part III it will be tested whether drivers of valuation

models also are capable of explaining TSR.

One of the valuation pioneers, Aswath Damodaran, suggests that banks are best valued

using equity valuation models rather than the usual enterprise valuation models applied

in non-banks valuation (Damodaran 2009). The following methods will be discussed in

order to identify factors that might drive TSR:


20

• Market-based models

• Equity based models

Some of the general assumptions behind almost all of the valuation models are “going

concern” and that they are valued on a stand-alone basis (without synergies in e.g. M&A

activities), these assumptions will also be applied here. (Koller, Goedhart & Wessels

2010)

4.1 Market-based Models

When using market-oriented approaches, analysts use stock market data to form peer

group multiple analysis in order to compare the value of two banks. The core

assumptions behind peer group valuation based on multiples are the similarity between

the compared banks both on size, risk, type etc. (Koller, Goedhart & Wessels 2010)

In the valuation of non-banks, value multiples such as EV/EBITDA or EV/EBIT are the

preferred multiples. However, these are not applicable in bank valuation due to the

difficulty in estimating both the enterprise value and the operating income. Enterprise

value is difficult to estimate due to the challenges in interpreting the debt while

estimating operating income cause problems in the separation of operating and financing

activities. (Koller, Goedhart & Wessels 2010)

Especially two models are preferred in the literature: P/E and P/B (Koller, Goedhart &

Wessels 2010, Damodaran 2009, Dermine 2009, Gross 2006)(Fernández 2001).

P/E is one of the most widely used multiples across several industries (Gross 2006) and is

defined as price per share over earnings. The level of P/E is a function of earnings

growth, the payout ratio and the cost of equity. As with non-banks, high growth banks

with high payouts and low cost of equity should trade at high P/E levels (Damodaran

2009).

There are however some disadvantages with this approach when it comes to banks. First

of all, the actual P/E multiple do not take a forward looking perspective, a problem that

to some extent can be taken care of by using forward earnings. A more important

drawback is its dependency of the underlying accounting variables and the subjective

accounting decisions made by the banks that tends to make it very volatile, see Appendix

17.1.3. (Damodaran 2009)

Due to this instability of P/E another equity ratio, the P/B ratio, is preferred. In this

ratio the market value is compared to the value of book assets. This multiple is driven by

earnings growth, payout ratios, cost of equity, business mix (Dermine 2009) and ROE,

where ROE is the variable that has the most impact on P/B (Damodaran 2009).

Mathematically the superiority of P/B over P/E can be seen from the following formula:


21

�� !" #"� $%&�"'(() *&+," #"� $%&�" = �� !" #"� $%&�" - .�/

'(() *&+," #"� $%&�" - .�/ =

Price per shareEPS ∙ EPS

Book value per share = PE ∙ ROE

Since P/B is constructed by P/E·ROE, it is preferred because it has all the benefits that

the P/E ratio provides but also the current level of ROE. The stock market often uses

ROE in the estimation of a fair price and the high correlation between ROE and P/B

makes it a preferred multiple (Damodaran 2009). This correlation can also be seen from

the data found for this thesis. Exhibit 4.1.1 shows both the ROE and PB of the average

bank in the sample and a correlation of 96% supports this close relationship. If the stock

market e.g. demands 7 % ROE and the banks are capable of delivering 14 % a rule of

thumb is that the bank will trade at P/B close to two. If the bank, however, only

delivers ROE of 3.5% (on a consistent basis) the stock market will trade it at P/B close

to 0.5. (Damodaran 2009)

Exhibit 4.1.1: The Correlation between ROE and P/B

Source: Bloomberg, Own Contribution

The relationship between P/B and ROE is stronger in banks than in non-banks. This is

not a surprise because the book value of equity is much more likely to track the market

value of equity invested in existing assets due to the marked-to-market principles

discussed in Chapter 3 (Damodaran 2009). One of the drawbacks by using the P/B

multiple is, however, that it does not include off balance sheet items such as interest rate

derivatives, loan commitments, loans without recourse where the bank has no

commitment etc. even though these items often account for a significant part of the

2.11.8

2.22.4

2.1 2.1

1.71.2 1.1 1.1

0.9

13.3%12.9%13.8%

14.7%15.3%15.3%

12.9%

2.7%

2.1%4.7% 0.8%

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

0.0

0.5

1.0

1.5

2.0

2.5

3.0

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

P/B ROE (right axis)

EQ 4

Correlation = 96%


22

business (Johnson 1996). Finally, P/B suffers from some of the same general drawbacks

as discussed in Appendix 17.1.3, regarding P/E.

4.1.1 Control Variables Derived from Multiples

In connection to this thesis, one general problem with multiples in regards to value-based

management is that management cannot directly influence the multiples since they are

not transparent to managers (Damodaran 2009). It does however, not mean that

multiples are useless because they can give an indication to managers of whether the

business environment is changing (Gross 2006). Further, when the thesis discusses the

different parameters to incorporate into the regression model in Part III, P/B will, due to

its superiority, be used as a control variable for the market expectations instead of P/E

suggested by (Koller, Goedhart & Wessels 2010).

4.2 Equity-based Models

The equity based models are based on discounting future cash flow to equity (CFE) by

the cost of equity in order to find the company value (Koller, Goedhart & Wessels

2010)(Damodaran 2009)(Dermine 2009). In the estimation of CFE it is however necessary

to look at the income statement and balance sheet of a bank. Since the simplest bank to

value is a pure retail bank, this banking type will work as an example.

Starting from the top of the income statement, the overall revenue driver in retail

banking is net interest income and is calculated as the difference between interest income

and interest expense. From this, operating expenses and taxes are subtracted in order to

arrive at the net income which is the amount left to equity holders when all expenses and

obligations to debt holders have been paid, for a detailed description of the income

statement and balance sheet see Appendix 17.1.4.

Even though net income is the earnings that theoretically are left to the equity holders, it

does not equal the CFE. As discussed in Chapter 3 banks need equity to grow if it is to

meet regulatory demands, increasing equity means less cash to equity holders. Finally,

other types of non-operational income or expenses also have to be included in the

calculation of CFE. Therefore, CFE is calculated as net income subtracted changes in

equity book value plus other comprehensive income after debt obligations have been met

which can be seen in the following formula (Koller, Goedhart & Wessels 2010).

CFEt = NIt - ∆Et +OCIt

The value of the company is then calculated as the value of future cash flows (CFE)

discounted with the proper cost of capital. Since WACC is not applicable cost of equity

is applied as the discount factor and the equity value of a bank is calculated as

V" = ∑ 89.:(�;)<):

∞�=� EQ 6

EQ 5


23

Another way to calculate CFE is by summing all expected cash paid to or received by

equity holders and discounting it at the cost of equity. Since both calculations will arrive

at the same number a thorough discussion of this method can be found in Appendix

17.1.5.

Turning towards the cost of equity, it is the return a firm theoretically pay investors to

compensate for the risk they undertake (Brealey, Myers & Marcus 2012). Quantification

of this tradeoff between risk and return is one of the important problems in modern

financial economics (Campbell, Lo & MacKinlay 1998). (Markowitz 1959) laid the

groundwork for this quantification and (Sharpe 1964) and (Lintner 1965) build upon it in

the development of the capital asset pricing model (CAPM) where the cost of equity is

determined by the following equation

CoE = Rf +βBank (Rm - Rf)

In this model it is assumed that investors can lend and borrow at the risk free rate Rf

and that the CoE is calculated as the risk free rate plus the banks beta multiplied with

the market return subtracted the risk free rate. This shows that taking on more risk

(increasing the beta) will increase the CoE.

The equity cash flow method is straightforward and theoretically correct, however, in

connection to value-based management it yields a serious pitfall because it is impossible

to track where and when profit is created. Therefore, some further analyses are required

in order to show investors and bank managers how and where value is created. Such an

analysis is the residual income model. (Dermine 2009)

4.2.1 Residual Income

By construction, residual income models are able to give a detailed description of the

value created by a company over a certain period because it calculates the difference

between return and the cost of capital of an activity. Since the methods are derived

directly from the DCF approach any valuation based on the residual income method will

be identical to the DCF approach (Koller, Goedhart & Wessels 2010). The general

residual income formula is stated as

Residual Income = Invested Capital · (ROIC - WACC)

However, since bank valuation differs from normal valuation by its focus on equity the

general residual income model for banks should be adjusted to the following (Damodaran

2009):

Residual income = Equity · (ROE - Cost of equity)

From the formula it can be seen, that it is a measurement tool that are able to determine

both whether the bank earns more than their cost of capital and how much the bank

EQ 7

EQ 8

EQ 9


24

earns more. It is applicable in each business unit and also takes into account the

corresponding risk of a given activity.

Besides being a multiyear VBM tool that focuses on shareholder maximization, Stern

Stewart also suggests residual income models as a tool to motivate managers because it

makes them think like owners (Stewart 1998), thereby avoiding some of the agency

theory problems discussed in Chapter 2.

4.2.2 The Value Drivers

To summarize the value drivers in the valuation models a value driver tree for a simple

retail bank has been constructed, see Exhibit 4.2.2. Following the tree’s branches it is

possible to analyze how value is increased in the valuation models.

Exhibit 4.2.1: Value Drivers Extracted from Bank Valuation Models

Source: (Koller, Goedhart & Wessels 2010)

It is clear from the model, that the cost-income ratio is an important driver and

calculated as operating expenses divided by net interest income. Further, subtracting

taxes, operating expenses and loan loss provisions from the net interest income makes it

possible to derive the net income and thereby calculate return on equity, which is, as

described in the residual income model, a key driver in bank valuation models. (Koller,

Goedhart & Wessels 2010)

1After taxes

Value

creation

Growth

Cost of

equity

Return on equity

Equity

Operating

expenses1

Net interest income

Additions to loan loss provisions1

Cost/income

Interest

rate assets1

Assets

3

Capital

ratio

4

5

6

7

1

2

3

4

5

6

7

Interest rates on products

Volumes: Book values of

assets and liabilities outstanding

Cost-to-income ratio:

Operating costs of business relative to net interest income

Capital ratio: Equity requirements for assets outstanding

COE: Cost of equity based on asset liability mix

Growth: Growth of assets

and liabilities

Loan losses: Expected future losses on loans outstanding

Interest rate

liabilities1

1

Liabilities

2

Value driver tree Value drivers


25

5. Value Drivers Discussed by Academia

Since the mid-2007 where the financial crisis started, it has been illustrated that bank

performance has a substantial influence on efficient capital allocation and the overall

economy (French et al. 2011). Academia has for some time had this in mind, and quite a

few papers have focused on how banks are able to increase their profitability. Studies like

(Athanasoglou, Brissimis & Delis 2008)(Brissimis, Delis & Papanikolaou 2008)(Lepetit et

al. 2008)(Berger et al. 2004)(Berger, Mester 2003)(Salas, Saurina 2003) are considering a

wide range of factors that they believe affect banking profitability. These factors include

bank specific, industry specific and macro specific variables. Most studies have some kind

of geographical area that they focus on. (Athanasoglou, Brissimis & Delis 2008) looks at

the Greek banking sector from 1985 to 2001 in order to test whether bank-specific,

industry-specific and sector-specific variables have an impact on profitability while

(Dietrich, Wanzenried 2011) focused on the same variables in the Swizz banking sector.

The study finds that all the bank specific factors (except for size) affect profitability.

Similar, (Brissimis, Delis & Papanikolaou 2008) examines the relationship between

individual bank performance with the banking sector reform-4, competition- and risk

variables. In their study, which they have conducted between 1994 and 2005, they find

that increased competition increases the efficiency and that increased risk has a negative

impact on the bank profitability. As discussed earlier, risk has always been an important

part of bank profitability. In connection to that (Lepetit et al. 2008) investigate the

relationship between risk and product diversification and find, that banks with a higher

non-interest income ratio display higher risk than pure retail banks. Other aspects of risk

are the degree of leverage and the impact on profit and efficiency. Both (Berger,

Bonaccorsi di Patti 2006) and (Margaritis, Psillaki 2010)(Cummins, Lewis & Wei 2006)

have analyzed this relationship and find that, controlled for everything else, higher

leverage (e.g. a lower tier 1 ratio) increases the bank efficiency and therefore bank

profitability. Further, in relation to changes in the leverage ratio, (Kwan, Eisenbeis 1997)

and (Demirgüç-Kunt, Huizinga 2004) recognize that a positive loan and deposit growth

affects profitability positively. Finally, studies have also focused on different source of

profits like e.g. (Cummins, Lewis & Wei 2006) or (Gillet, Hübner & Plunus 2010) who

both investigate the relationship between operational risk and the corresponding reaction

on the stock market.

As it has just been described, there is a large amount of literature focusing on how

profitability might be affected by various factors. However, the empirical literature on

how shareholder value can (Marshall 1891)be affected by various factors is somehow

limited. Only a few studies have tried to find a relationship between bank productivity,

bank efficiency and shareholder value but they generally come up with positive results.

(Fiordelisi 2007) defines a measure called shareholder value efficiency where banks

producing the highest economic value add are described as the value-efficient banks.

4 By using the European Bank for Reconstruction and Development Index of banking sector reforms


26

(Beccalli, Casu & Girardone 2006) tests the stock return on various efficiency measures

and generally find a positive correlation between the two. However, the studies are

limited to only focusing on the relationship between a single factor and shareholder value

creation instead of testing a larger set of factors that might affect shareholder value (like

cost of equity, growth, risk etc.). The articles discussed in Section 5.1, 5.2 and 5.3 are

considered the main literature within the area of shareholder value creation.

5.1 Profitability as the Main Value Driver

(Fiordelisi, Molyneux 2010) tries to cover some of the gaps within the “shareholder value

in banks” literature. First, a broad range of factors that impact shareholder value in

European banks are analyzed. This is followed by a causality test of the factors where

there is controlled for the trade-off between various value determinants (Fiordelisi,

Molyneux 2010).

The shareholder value creation measure used by (Fiordelisi, Molyneux 2010) is the

economic value added (EVA) which is defined as a bank’s net operating profits

subtracted its capital charge over the same period. In order to affect the EVA banks have

three bottoms to push: Net operating profit, opportunity cost of capital and invested

capital. The value drivers suggested by (Fiordelisi, Molyneux 2010) can be observed in

Exhibit 5.1.1.

Exhibit 5.1.1: The Value Drivers according to (Fiordelisi, Molyneux 2010)

Source: (Fiordelisi, Molyneux 2010), own contribution

EVA

Invested capital

Net operating

profit

Opportunity cost of capital

Cost structure

Income structure

Fee income

Interest income

Security investment

returns

Fee cost

Interest cost and exp.

credit loss

Capital losses

Operating costs

Risk exposure

Leverage

Level of risk management


27

From this value driver tree different KPIs are evaluated. (Fiordelisi, Molyneux 2010)

suggests that, based on earlier profitability literature such as (Berger, Mester 2003)

(Lepetit et al. 2008) and (Beccalli, Casu & Girardone 2006), cost efficiency, revenue

efficiency and income diversification is expected to have an impact on the income

structure of the bank. Also loan and deposits growth is considered valuable KPIs

(Demirgüç-Kunt, Huizinga 2004) and are seen as proxy for both bank performance and

soundness. Other authors such as (Stan Davis, Tom Albright 2004) also suggest it as a

proxy for customer satisfaction. Turning toward risk exposure and risk costs, a suggested

KPI is loan loss provisions to total loans (Brissimis, Delis & Papanikolaou

2008)(Athanasoglou, Brissimis & Delis 2008). The industry specific factors are testing

whether the market structure has any impact on the performance (Berger et al. 2004).

For this, the Herfindahl index is included since it measures the domestic banking

industry concentration. The last measure is the macroeconomic factor GDP per capita

which are included as a control variable according to (Salas, Saurina 2003) and

(Brissimis, Delis & Papanikolaou 2008).

Exhibit 5.1.2 illustrates the various factors and shows the results from the article. The

most important finding in the article is that cost-efficiency and revenue-efficiency are

confirmed to have a positive relationship on shareholder value (measured by the

shareholder value efficiency measure). Furthermore, the use of generalized method of

moments allows the test of lagged effects of the independent variables because it is

suggested that the total payoff might not come right away. Both cost and revenue-

efficiency is confirmed to have a lagged effect on the value generation. Finally, it is

emphasized that the value factors often have both positive and negative side effects and

it is a trade-off between those that measures the net effect.

Exhibit 5.1.2: The Investigated Value Drivers

Variable Result

Profita-

bility

"Cost-efficiency" Positive relationship and a lagged effect ***

"Revenue efficiency" Positive relationship and a lagged effect **

"Profit efficiency" Positive relationship and a lagged effect ***

Credit risk,

liquidity

and

leverage

Provision to loan-loss reserves Positive relationship **

Security investments/assets Negative

Avg. loans/Avg. deposits Negative

Liabilities/equity Positive

Control

variables

Net non-interest inc./Net oper. inc. Positive relationship ***

Adj. loan growth rate Positive

Adj. deposit growth rate Negative relationship *

Total assets Negative

Herfindahl index Negative

GDP pro capita Negative

* Significant at 10% ** Significant at 5% *** Significant at 1%

Source: (Fiordelisi, Molyneux 2010), own contribution


28

5.2 Risk and Cost-efficiency as the Main Value Drivers

(Gross 2006) states that shareholder value has become the pre-eminent performance

measure in many industrial companies and it has significantly affected how some banks

in recent years have tried to optimize their business. The objective of the paper by

(Gross 2006) is to find the metrics that are able to quantify the story behind shareholder

value and to understand the fundamental drivers of value. Also in this thesis it is

emphasized that not many articles have so far been written on the subject and it

therefore contributes to the mapping of the value drivers in banks. The analysis is based

upon a regression analysis of 139 retail banks in Germany in the period 1998-2003. Since

the analysis is based on non-traded banks it is not possible to get TSR data and a

residual income value is used to approximate it. Besides not being available, (Gross 2006)

believes that TSR is affected by short-term market reactions and this potential over- or

under valuation makes residual income more reliable. The rationale behind this decision

is to be found in earlier literature by (Stephen H Penman, Theodore Sougiannis 1998)

where evidence for residual income as a superior measure for shareholder value creation

in companies has been provided. Further, residual income is directly linked to the

operational value drivers that (Gross 2006) looks at. Finally residual income can easily be

estimated with the data required in a bank’s annual reports. The relationship between

residual income and the different drivers can be seen in Exhibit 5.2.1.

Exhibit 5.2.1: The Value Drivers Identified by (Gross 2006)


The drivers that are tested in the study and that can be seen in Exhibit 5.2.1 above are

the strategic and operational value drivers that affect the residual income on equity

(RIOE). The first driver is business mix, also suggested by (Baele, De Jonghe & Vander

Vennet 2007)(Dayal et al. 2011) and (Leichtfuss et al. 2010), where income diversification

is used as a measure. Low diversification is seen as a measure for the traditional lending-

oriented business model where a high measure characterizes the diversified banks and

those that are more depended on fees and advisory-based income. The second driver is

branch structure, measured as customer per branch. It has been widely argued whether

there is such a thing as an optimal branch structure but it is potentially a value driver.

Cost efficiency is the third driver that is tested and measured as total cost per employee.

Due to recently introduced cost programs it is expected to be a value driver that affects

Intrinsic value

Residual income

RIOE

Economic equityCost of Equity

RoE (After tax)

Cost/Equity

Income/Equity

LLP/Equity

Taxes/Equity

Financial indicators Operational value drivers

Business mix: Diversification of income

Risk capabilities: LLP/Interest income

Branch structure:Customers/Branch

Cost efficiency:Total cost/Employees


29

TSR, this is supported by (Dermine 2008) and (Fiordelisi, Molyneux 2010). Finally, risk

capabilities are included since it is expected to be still more important for banks in the

future. The LLP/Interest income provides important information regarding the risk

capabilities of a bank and is thus expected to influence bank value significantly.

The findings in (Gross 2006) suggest that only the cost efficiency and the risk capabilities

are relevant drivers for shareholder value in banks. Whereas, both the business mix and

the branch structure driver is difficult to make any reliable conclusions on, due to their

ambiguity. The regression results for the business mix suggest that an increased income

diversification is value destroying in the short-term. Results for the underlying income

cost and risk structure for the bank is somewhat controversial as well. Looking at the

branch structure there is no empirical evidence for the value impact of changes in the

branch structure and it is therefore concluded that it has no direct impact on value.

Potential value implications are instead driven by the interdependence of the branch

structure and the different value drivers (Gross 2006). Exhibit 5.2.2 summarizes the

different value drivers and the key results of the study.


Variable Category Result

Interest income / Total income "Business mix" No clear relationship

LLP / Interest income "Risk" Positive relationship

Customers / Branch "Branch structure" No clear relationship

Total cost / employee "Cost efficiency" Positive relationship

Interest rate "Control variable"

Ln(assets) "Control variable"

Universal bank dummy "Control variable"


5.3 Bank Diversification as a Value Driver

Following the second banking directive 1989, European banks has been allowed to pursue

functional diversification across different financial activities like investment banking,

commercial banking, insurance etc. This law has increased the diversification among

some banks (Baele, De Jonghe & Vander Vennet 2007). The aim of both (Walter 1997)

and (Baele, De Jonghe & Vander Vennet 2007) is to test whether these conglomerate

banks have a comparative advantage in terms of long-term shareholder value, compared

to the more specialized competitors. From a regulatory perspective, a bank is considered

a conglomerate by (Baele, De Jonghe & Vander Vennet 2007) when its product portfolio

consists of two out of three of the following: banking, insurance or securities-related

activities. In order to test for diversification in practice, researchers look toward non-

interest income in order to measure the diversification. Non-interest income effectively

captures all those income streams that are not bank related which makes it suitable for

these kinds of tests (Dermine 2009).


30

When a bank chooses to diversify itself it yields both some advantages and disadvantages

that affects the shareholder value (Baele, De Jonghe & Vander Vennet 2007). The

advantages are first of all created through increased cost effectiveness or increased

revenue effectiveness (synergies) of combining the different kind of financial activities.

Further, both the bank and e.g. the insurance part possess information from their

activities that facilitate the efficient provision of each other’s financial services. Hence, a

banking conglomerate is able to exploit the information it has collected on different

activities and thereby secure increased performance and market valuation. Finally, when

cross-activity is allowed, managers of the financial firms incur a higher degree of

monitoring by the acquiring market (Saunders 1994) which might decrease some of the

agency problems. Taking a look at the disadvantages, a more diversified company may

also mean a more complex business which decreases shareholder value. Further, agency

problems in the form of conflict of interest might be increased both between insiders and

outsider but also between each division. Also more person-specific disadvantages may

arise since managers can seek private benefits by a merger or by taking in a new business

unit even though it is at the cost of shareholder value. Finally, conglomerate discount is

a widely used term and also tested by (Schmid, Walter 2009) in terms of banking. It is

here found that banking conglomerates normally trade at a discount. Theoretically it is

unclear when dealing with non-banks and banks whether the advantages weigh stronger

than the disadvantages in terms of shareholder value. However, (DeLong 2001) have

obtained that shareholder value is created through M&A activities only when they are

based on activity and geography whereas more functional mergers are value destroying.

As it is clearly seen, the literature is divided in the question whether increased

diversification increases or decreases shareholder value. In the studies (Baele, De Jonghe

& Vander Vennet 2007) finds strongly positive correlations between shareholder value

and the degree of diversification, measured as non-interest income to total revenue,

meaning that the stock market anticipate the advantages to have a greater weight than

the disadvantages. In Exhibit 5.3.1 some of the other variables that (Baele, De Jonghe &

Vander Vennet 2007) tests in order to decide whether diversification affects performance

can be seen.


Variable Category Result

Revenue diversity "Business mix" Positive impact on value creation ***

Equity to assets "Leverage" Negative (Not significant)

Equity to assets squared "Leverage" Positive impact on value creation ***

Cost-income "Cost efficiency" Negative impact on value creation ***

Loan loss provisions "Risk" Positive (Not significant)

Ln (assets) "Size" Negative (Not significant)

* Significant at 10% ** Significant at 5% *** Significant at 1%

Source: (Baele, De Jonghe & Vander Vennet 2007), own contribution

Another way of looking at diversification is by discussing whether large banks outperform

small banks because only the very large banks have the capabilities of having several


31

financing activities (Walter 1997). An issue that has received a very large amount of

attention is the existence or lack of economies of scale and scope. Where economies of

scale refer to size being able to create a competitive advantage, economies of scope refer

to the fact that joint offerings create competitive advantages (Dermine 2008). (Walter

1997) addresses the problem through a discussion about the two phenomena. In an

intensive sector with high information, many distribution challenges and high fixed costs

it might be suggested that there is an economic rationale behind economies of scale.

Economies of scope are discussed by looking at supply and demand side. On the supply

side, scope economies are related to the sharing of costs through joint production of

similar products. However, banks might also be exposed to diseconomies of scope on the

supply side due to inertia and lack of responsiveness and creativity through increased

bureaucratization and increased complexity. Further, (Calomiris 1995) have found that

there was a negative supply side economy of scope among universal banks, meaning that

the more products the bank had in its portfolio the higher were the cost on each product

compared to the specialized companies. Turning towards the demand side, economies of

scope arises if the bank succeeds in lowering the total cost compared to if the customer

needs to buy each financial product at separate companies. Diseconomies of scope on the

demand side could be experienced through agency costs if the multibank employees act

against the interest of the customers in order to facilitate a sale to another part of the

bank. Even though (Walter 1997) only discusses the different issues without testing

them, they will be included as variables in the analysis in Part III.

6. The Value Drivers Discussed by Consulting Companies

Having discussed value drivers from an outside-in perspective the following chapters will

focus on the value drivers that are found through an internal view by consulting

companies and the banks. Consulting can deliver an internal view because they are hired

by the bank management to optimize the bank and therefore gain access to internal data.

This data is collected in information pools and used in their industry analyses. (Visali et

al. 2011, Duthoit et al. 2011)

Operational excellence is becoming a still increasing focus of bank managers, but

relatively few of them understand the steps they must take to raise their operational level

(Duthoit et al. 2011). According to the consulting industry, the maximization of

shareholder value is therefore a strong performance measure where managers are forced

to make value creating decisions. In the following part the results of a literature review of

the articles concerning the subject “shareholder value in banks” will be presented. The

objective of the part is to give both an overview of the different methodologies and a

more operational view than what is often seen within academia. Finally, the findings will

be applied as input factors in Part III.

Before discussing the findings from management consulting companies, it is important to

understand the major drawback in using consulting literature. The key focus in these


32

firms is to earn money meaning that the more companies implement shareholder value-

approaches the more training is needed, training that consulting companies offer (Young,

O'Byrne 2001).(Myers 1996) discusses this greed driven “war” between the consulting

companies. However, keeping this drawback in mind it can also be suggested that they

are forced to provide the readers with some strong analyses in order to secure their

business, and therefore lessons can still be learned from analyzing the findings of

consulting companies.

In the procedure of selecting the consultancy companies there was a requirement

regarding available reports on the subject. The chosen companies that lay ground to this

were Booz & Company, Bain & Company, Boston Consulting Group (BCG), McKinsey

& Company, Deloitte, KPMG, Ernst & Young and PriceWaterouseCoopers (PWC). Of

these companies only McKinsey & Company, BCG and PWC had available publications

on shareholder value in banks. Measured by the number of available articles BCG is the

consultancy company with most focus on the subject, however, McKinsey also have

contributed with a great deal of insight. A more extensive description of the consulting

companies and the selection process see Appendix 17.1.6. The following will focus on key

drivers discussed by the consulting firms while the more thorough operational changes

that need to be made in order to affect the drivers will be discussed in Part IV.

6.1 The Value Drivers Discussed by McKinsey

Each year McKinsey analyses the banking sector in the article series "The State of Global

Banking" where the core drivers for bank valuation are discussed. As is the case with

McKinsey’s general view on shareholder value creation, the core driver in banks is the

economic profit. However, the economic profit is calculated differently from banks to

non-banks, where ROE and cost of equity are the main drivers (Visali et al. 2011)5.

Exhibit 6.1.1 shows how McKinsey sees value creation in banks.

5 This follows what was discussed in the section regarding bank valuation models


33

Exhibit 6.1.1: McKinsey’s View on Value Drivers in Banking

Source: McKinsey: (Visali et al. 2011)


In order to have a successful VBM system, bank managers need to take into

consideration the future challenges that lie ahead of them (Visali et al. 2011). The world

is consistently evolving, and with it the environment that banks operate in. At the

moment, especially four trends will have an impact on the core value drivers going

forward.

First, the increasing regulation is the single most important driver when it comes to

profitability. Due to the heavy capital constraints, equity capital and funding costs will

increase which is expensive for banks as discussed in Chapter 3. These increased

regulatory constraints will impact the key drivers through a set of underlying value

drivers. ROE will be affected both in the numerator and the denominator. Return will be

negatively impacted due to the increasing cost of holding equity and higher operational

costs whereas the common equity in the denominator will increase. This expected

decrease in ROE is also the main reason for the negative TSR in the years after the crisis

(Daruvala, Malik & Nauck 2012). Looking at the other driver, cost of equity, it is

expected to decrease due to a more secure sector. However, the decrease is not expected

to make up for the lower ROE.

Second, the squeeze on capital and funding driven by new investment opportunities and

the increasing demand for capital in banks all over the world increases the funding costs.

An underlying value driver that is affected by this change is the net interest margin. This

development is especially seen in Spain where the increasing demand for capital have

more than doubled the interest rate on customer deposits compared to treasury bills, and

therefore puts pressure on the net interest margin. (Visali et al. 2011)

ROEIntangible

assets

Net income

Tangible common equity

Annual PLL

Revenues before PLL

Cost-income ratio

Other incl. taxes

RWA

TCE Ratio

Net interest margin

Fee Margin

Total Assets

RWA/Total assets


34

Third, revenue growth is still seen as a value driver by McKinsey. In the recent years it

has however been difficult to grow organically within the Western European and

American region. The arguments for this driver are therefore to be found in the emerging

markets. Banking TSR have been significantly higher in those countries that have

experienced growth compared to those where a flat development have been seen. This

indicates that those banks, capable of capturing revenue growth will be able to increase

shareholder value. (Visali et al. 2011)

Finally, the shift in consumer behaviour to a more technology-driven behaviour will

affect the cost driven value driver. By closing branches or making them smaller bank

managers will be able to decrease the cost-income ratio which is also expected to affect

shareholder value positively. However, as is always the case with change, only the banks

capable of adapting to the changing environment will benefit from it. Those banks not

capable of delivering superior customer experience to a new generation of self-helped

customers will have a hard time competing. (Visali et al. 2011)

6.1.2 Macro Variables

When testing the value drivers, bank managers are not solely responsible for the

development of a bank’s performance; some factors are to be controlled for in order to

isolate the internal value drivers. One of these is the domicile country’s credit rating. If

the country has a low credit rating it will affect the banks credit rating, higher the

lending costs and thereby make it difficult to compete across borders and be more

vulnerable to foreign competitors with higher credit ratings. A proxy for this credit rating

is the CDS spread which McKinsey uses to control for the different funding costs (Visali

et al. 2011). In Exhibit 6.1.2 all the value drivers that McKinsey focus on can be seen.

Exhibit 6.1.2: Performance and Confidence Indicators used by McKinsey

Performance indicators Confidence indicators

Financial depth Cross-border capital flows

Banking revenue growth Short-term cross-border loans

Net interest and fee margins LIBOR-OIS spreads

Annual provisions for loan losses Bank Credit Default Swap (CDS) spreads

Non-performing loans Bank market capitalization

Cost-income ratio Bank price-to-book multiples

Banking profit growth

ROE

Capital ratios

Loan-to-deposit ratio Source: (Visali et al. 2011), own contribution

6.2 The Value Drivers Discussed by Boston Consulting Group (BCG)

In order to explore what drives bank valuation and which value drivers to focus on, BCG

has conducted several analyses on the issue. The analyses are based on the largest banks


35

in the world where they compare top performers with the underperformers on some key

value drivers. Key findings from these studies are that even among large universal banks

there are several factors that can increase performance (Duthoit et al. 2011).

6.2.1 The Focus Areas

In general, BCG focuses on some operational drivers such as sales- and service

effectiveness, process- automation and industrialization in order to affect key value

drivers like CIR, ROE, ROA, revenue growth etc. Where McKinsey focused on four

trends that the banking business models need to adapt to, BCG see two major trends

that challenge these numbers. First of all, an intensifying competition due to

deregulation, that opens the international markets and increases the number of global

banks, the introduction of both direct and online banking and increasing customer

demands. This change in the competitive landscape is expected to put an increasing

pressure on the banks earnings margins. Secondly, BCG also emphasizes the importance

of adapting to the capital requirements and uses the Risk-to-income ratio as a measure

on how capable the bank is of controlling for the increasing risk (Dayal et al. 2011).

Starting with the competitive landscape, the question is now where the new margin

equilibrium on the cost-income ratio and ROA will be and how bank managers can

increase it to an absolute maximum. As stated BCG has looked at how some banks

historically have been able to outperform and are giving two suggestions on what bank

managers can do in the future and which value drivers to focus at. In order to become a

“winning” bank BCG suggest that bank managers focus on ROA and CIR since they are

the two accounting numbers that are able to grow value the most (Duthoit et al. 2011).

Further, revenue growth is also an important value driver and can be achieved either by

organic growth or through M&A activities. Like McKinsey, BCG supports their

arguments for revenue growth being an important value driver, based on the

development in emerging market. (Dayal et al. 2010)

Achieving revenue growth is however very difficult. The reason for this low growth in

developed economies is not due to a decreasing focus from the top management but more

because of the challenges in increasing revenue per customer. As can be seen in the next

section a strong customer focus is needed thereby building the growth upon a long lasting

customer relationship. (Dayal et al. 2010)


Even though it is suggested that revenue growth is a value driver, being large is not a

synonym for sector outperformance. BCG has, just as (Walter 1997) and (Baele, De

Jonghe & Vander Vennet 2007) looked at whether large banks outperform smaller banks.

In these studies they have spotted what they call “domestic champions” which are those

banks that have been able to focus on creating superior sales and service by knowing

their customers preferences. This focus on a small market might conflict with the revenue

growth value driver, but being really good in a small area can make up for the missing


36

size (Leichtfuss et al. 2010). In Appendix 17.1.7 an example of a domestic champion can

be found. However, BCG also highlights some advantages from being a large bank. Often

diversification is larger in global banks making them more resilient. Further, by

spreading portfolios across different regions the great dependency to the overall economy

is also diversified across several regions (Leichtfuss et al. 2010).

The second large factor that BCG sees as a KPI is the Risk-to-income ratio (RIR), a

ratio that helps the bank manager balance between risk and growth. The fact that some

banks, during the crisis, came close to failure while others collapsed or became dependent

of government support has highlighted the importance of strong risk management skills

(Leichtfuss et al. 2010). Further, risk cost has been the main driver of negative value

creation since the start of the crisis and is expected to remain high for the coming years

(Dayal et al. 2011). In Exhibit 6.2.1 it is clear to see how the RIR has evolved as an

effect of increasing cost of equity and provision for loan losses.

Exhibit 6.2.1: The Relationship between CoE and RIR

Source: Bloomberg, own analysis

Looking across the BCG studies included in this review a total of 40 variables affecting

the shareholder value has been identified, see Exhibit 6.2.2.

50 60 53 43 50 60 103

249

400

25819965 80 88 104 135 160

216

258

312

411

335

65% 66% 62%51%

60% 62%

82%

135%

136%

192%

145%

0%

50%

100%

150%

200%

250%

0

100

200

300

400

500

600

700

800

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Billion USD

Provision for loan loss Cost of equity RIR (Right axis)


37

Exhibit 6.2.2: 40 Variables Considered by BCG

Dayal et. al. (2010) Dayal et. al. (2011) Leichtfuss et. al. (2010) ROE Risk to income ratio (RIR) CIR

Trading revenue growth Operating costs ROA

Operating profit margin Risk weighted assets Revenue

Risk cost Loan to deposit ratio Cost

Tier 1 ratio Growth in Long term assets LLP

RWA growth Deposit growth Retail banking revenue

Economic recovery (macro) Cost to income ratio

Regulation criteria Refinancing costs

Duthoit, et. al. (2011) Olsen et. al. (2010) Cost-income-ratio Branch wait time Profit growth

Customer/FTE Call wait time Multiple change

Sales&Service FTE/Total FTE Call resolution time Dividend yield

New Accounts/ Sales FTE Time until new account ready to use Net debt charge

New current account/operations FTE Time from application to fund available Revenue growth

Existing curr. accounts/operations FTE Time from application to condition app. Sales growth

Sales conversion/inbound call % of FTE with Customer facing sales Margin change

Customer attrition rate Cycle times Profitable growth*

Level of automation (Q) Call centre metrics

Sources: Dayal et. al. (2010), Dayal et. al. (2011), Leichtfuss, et. al. (2010), Dutohoit et. al. (2010), Olsen et. al. (2010),

own contribution

6.3 The Value Drivers Discussed by PriceWaterhouseCoopers

Although PWC have not published many articles on how to create shareholder value in

banks, (Black, Wright & Bachman 1998) presents a set of drivers that PWC focus on

when they evaluate banks. Besides macro economic factors, they group banking value

drivers into three overall categories “growth”, “returns” and “risk”. These three overall

drivers are then broken down into ten value drivers (Black, Wright & Bachman 1998).

Again, residual income is used as the dependent variable. Exhibit 6.3.1 shows the value

drivers.

Exhibit 6.3.1: PWC’s Value Driver Categories

Category Value Driver

Growth Competitive advantage period

Capital expenditures

Growth in operating assets

Returns Net interest margin

Non-interest income growth

Cost-income ratio

Loan loss rate

Cash tax rate

Risk Regulatory requirements

Cost of equity

Source: (Black, Wright & Bachman 1998), own contribution


38

6.3.1 The Growth Drivers

The first driver that bank managers should focus on is increasing the banks growth by

gaining competitive advantages. In PWCs models, the competitive advantage period that

the bank has is measured as the period where the bank is capable of earning a higher

operating return than their cost of capital (positive residual income). In the calculation of

this period it is discussed that the sustainability of the banks competitive advantages

needs to be taken into account because most banking products are easy to duplicate.

This makes these periods with positive residual incomes short unless the bank is capable

of renewing their portfolio all the time. An example from the Danish market of this ease

of duplication was Danske Banks launch of an iPhone app. It only took the competitors

six month to invent their own app making the period of competitive advantages

extremely short. Further, although it was argued in an earlier chapter that capital

expenditures are a minor part of a bank’s balance sheet, PWC believes it is capable of

explaining how value is created. Finally, growth in operating assets which is made up of

loans and other earnings assets (short term assets, long term positions in investments,

loans to banks etc.) is the final growth driver. Even though the last driver does not

directly affect the cash flow it is assumed that every loan creates deposits and thereby

creates value (given that net interest margin is positive). (Black, Wright & Bachman

1998)

6.3.2 The Return Drivers

The first return driver that PWC focus on is the net interest margin, since it can be

approximated to work as a cash flow measure which is what creates value in their

banking model. Further, non-interest income, which is made up of fees, commissions and

trading income (this is other operating income in the data) is expected to drive value.

Having covered the two main income drivers PWC turn towards the cost side of the

return. Like many other studies it is again the cost-income ratio that is included since it

is expected to be the best cost driver. Other cost measures are the loan loss provisions

that are used as a proxy for the implications of non-performing assets on the equity

holder’s cash flow. The last driver is the cash tax rate which is a measure that (Black,

Wright & Bachman 1998) suggest to control for. It has a great impact on the bottom line

and cash flow to investors but it is something that the banks cannot control themselves.

6.3.3 The Risk Drivers

The last two drivers is concentrated around risk since this, as discussed before, is an

important part of the bank. The first driver is connected to the regulatory requirements

and is therefore focused on the amount of equity that is needed to maintain capital

adequacy. PWC focus both on tier 1 and total capital ratios in order to determine the

effect that it will have on the cash flow to shareholders. The final driver is the cost of

equity that (Black, Wright & Bachman 1998) calculates through the CAPM framework

and therefore is affected by the risk free rate, the market risk premium and the beta as

discussed in Chapter 4. In the later years there has been a dramatic increase in the cost


39

of equity which is both driven by an increasing market risk premium but also through a

higher beta. This development in both beta and cost of equity can be seen from Exhibit

6.3.26.

Exhibit 6.3.2: CoE and Sector Beta

Source: Damodaran, Own data

6 The beta is only from American bank stocks, but is used as a proxy for the bank beta in this thesis.

8.2%9.2%

8.3%7.9%

9.7% 9.6%

11.1%

12.7%12.6%

16.3%

12.9%

0.67 0.67

0.62

0.530.55

0.59

0.63

0.71

0.75 0.740.77

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0.75

0.80

6%

8%

10%

12%

14%

16%

18%

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Cost of equity Sector beta


40

7. The Shareholder Value Banks

Having discussed the external views that both valuation models and academia have and

the internal view from consulting houses it is now time to take the last group, the banks.

It is assumed that banks must be the ones that know themselves the best, and the

identified case banks therefore play an important role in the determination of value

drivers. The shareholder value banks in this study are banks who have implemented

value-based-management, or in other words banks that have oriented all key processes

and systems towards the creation of shareholder value (Young, O'Byrne 2001).

In order to determine whether a bank has introduced value-based management a method

inspired by (Rapp et al. 2011) is used. (Rapp et al. 2011) examines annual reports for all

the banks in the sample and lists some clearly defined criteria. According to the criteria

of (Rapp et al. 2011) a company has implemented a VBM system in a particular year, if

an internal control system with an integrated VBM metric is described and this measure

is used as a target or controlling mechanism.

This means that one of the following three criteria has to be satisfied before the bank is

identified as a shareholder value bank:

• The direct mentioning of an implemented value-based management system

• The bank’s vision involves creating shareholder value

• The bank’s mission or ultimate goal is to create shareholder value

The selection process can be seen in Exhibit 7.1.1.

Exhibit 7.1.1: Selection Process


2011 Annual Reports for 132 sample banks

Summarize data on the key performance indicatorsSummarize data for the cases

1. Screening

2. Screening

3. Screening

Approved VBM bank

Mission and vision

Search key words

Annual Report 2001 or earliest one available for 20 remaining banks

Mission and vision

Search key words

Company information Company website

Articles


41

The careful study of the annual reports resulted in the identification of the shareholder

value banks. Both annual reports from the beginning and the end of the sample period

were analyzed. If traces of VBM were found both in the beginning and the end of the

period it is assumed that the bank has been committed to shareholder value in the entire

sample period. A total of 20 banks were found and Appendix 17.1.8 lists the identified

banks and the key arguments behind the title as shareholder value bank.

A major weakness of the procedure is that the analysis is performed outside-in and it is

almost impossible to determine whether or not the bank actually is managed according to

the principles of value-based management (Rapp et al. 2011). An extensive research on

VBM, conducted by (Boulos, Haspeslagh & Noda 2001) revealed that it takes more than

words to outperform competitors with a VBM system. It might therefore be difficult to

assess whether a bank only states that it has implemented VBM or if it actually manage

the entire bank according to the VBM principles. With this in mind it is still found

valuable to discuss which value drivers the banks consider. In the following, Deutsche

Bank will be presented further in Exhibit 7.1.2 since it is one of the banks that are

committed to shareholder value creation. The motivation for including case studies is

that the full understanding of the focus of the shareholder value banks is important. An

analysis of the remaining 19 case banks can be found in Appendix 17.1.9.

7.1 The Value Drivers of Value-based Management Banks

Deutsche Bank directly states that they have followed the guiding principles of value-

based management (See Section 2.2) and uses TSR as their shareholder value measure:

"Based on a TSR-model which identifies the determinants of our value development

empirically, key metrics for internal steering were derived and connected with business-

specific value drivers."

- (Deutsche Bank, annual report 2010)

Since Deutsche Bank has declared its objective regarding total shareholder return it is

safe to assume that the bank in some way has identified the drivers of shareholder value

and incorporated the most important ones as KPIs in their management control systems.


42

The evidence from Deutsche Bank is the most direct statement about the use of value-

based management. In Europe the value-based management systems are also

implemented.

Exhibit 7.1.2: Deutsche Bank Case Study

Deutsche Bank is the largest bank in Germany, and headquartered in the “Twin

Towers” in Frankfurt. The bank is also on a global scale one of the largest banks as

it employs more than 100.000 people. It offers a full range of financial products such

as retail banking, private banking, investment banking, fund management etc.

KPIs

1. Return on equity 8. Economic profit growth

2. IBIT 9. Core Tier 1 ratio

3. CI-ratio 10. Leverage Ratio

4. Economic Profit 11. Liquidity

5. Revenue growth 12. Economic Capital Usage

6. IBIT growth 13. Share of classic banking

7. Asset growth 14. Revenue from growth regions

Another important feature of their VBM system is that the KPIs are evaluated each

year based on internal analysis and benchmarking. The annual review of the VBM

system is recommended by (Rappaport 1998). Deutsche bank also remunerates their

employees based on their contribution to shareholder value generation which was

recommended by (Pitman 2003).

Measured on TSR, DB has not been able to outperform the market:

-80%

-30%

20%

70%

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

TSR DB TSR MSC Fin.

Shareholder Value Focus

In the annual report from 2010 Deutsche Bank, as the only sample bank, explains

their value-based management system in detail. The description of the value-based

management system yields valuable insights about their key value drivers. Deutsche

Bank has conducted a large-scale internal analysis of their shareholder value

generation. Through analyses of the movements in key value drivers they have

identified the following KPIs:


43

In Exhibit 7.1.3 the identified KPIs from the banks with value-based management have

been summed up. Only KPIs used by at least 5 of the case banks are included in the

Exhibit.

Exhibit 7.1.3: The Most Commonly Used KPIs

Variable Count Variable Count

ROE 15 EPS 9

Tier 1 ratio 15 Revenue growth 8

Cost-income ratio 12 Business Mix 5

IBIT growth 11 Geography mix 5

ROA or RORWA 10 NIM 5 Source: Annual Reports of the 20 VBM banks, Own contribution

From Exhibit 7.1.3 it is seen that the most popular KPIs used by the banks are ROE

and the tier 1 ratio. ROE is reported in 15 out of 20 banks. The cost-income ratio is

reported by 12 of the 20 shareholder value banks.

When it comes to growth measures IBIT growth is reported by 11 of the 20 banks and

revenue growth is reported by 8 of the banks. 5 of the banks reported business mix,

geography mix and net interest margin as one of their KPIs.

From the scheme it is seen that the drivers suggested by the VBM banks are very similar

to the drivers found in Chapter 4. This close connection might stem from the stock

market’s request for standardized numbers that fit into the valuation models and make

them capable of comparing performance. It might also be that revealing sensitive

information might hurt the bank in the competition against other banks and therefore

only standard accounting numbers are revealed.

8. Mapping the Value Drivers

Exhibit 8.1.1 summarizes the value drivers identified in academia, consulting literature

and in the study of the VBM banks. One question to answer is whether there are

similarities across the internal and external literature and in that case where they are. A

discussion of these results will lead to the identification of the value drivers to be

included in the analysis in Part III.

Especially within the profitability measures are there some similarities between the

internal and external literature. Variables like ROE, Cost-income ratio, IBIT growth,

ROA and NIM are all mentioned across literature and are therefore expected to

significantly influence TSR.

Turning towards risk, both groups acknowledge the importance and prefer variables like

the loan loss rate and tier 1 ratio. A somehow similar measure is the loan loss coverage

that is also widely used and therefore investigated in the study.


44

Finally, macro factors are the last group of variables that is discussed across all literature

except from the VBM banks and therefore included in the analysis as control variables.

As discussed in Chapter 3 the economy is central to bank performance and the literature

mention variables like the interest rate, the growth in GDP, market competition and

whether the stock market is bullish or bearish. The reason for the VBM banks lacking

focus on macro variables might be due to the fact that they cannot affect them, rather

than not acknowledging their importance.

Turning towards the differences, especially the consulting literature diverges from the

others due to their generally strong focus on efficiency measures as value creators. An

example is (Duthoit et al. 2011) that has a very operational focus and contains 17

operational factors that influence value creation. However, also six VBM reports

operational measures like number of employees and customer satisfaction scores. This

focus from internal specialists on such efficiency measures indicates that there are more

to shareholder value creation than just pure accounting numbers.

Next, a measure that is discussed across different VBM banks and consultancy reports

but not even elaborated in academia is geography mix, meaning how much of the revenue

is secured outside the domestic market. Deutsche Bank e.g. considers emerging markets

percentage a key value driver (see (Deutsche Bank 2010)). This variable seems reasonable

since the emerging markets have outperformed the developed markets and the expected

growth and earnings are higher (Dayal et al. 2010). One reason why academia has not

touched upon it might be due to low data availability. If the data had been available a

measure like percentage of income from home country would have been interesting as

well as percentage of revenue from emerging markets. For this analysis it has

unfortunately not been possible to collect the data either.

Some other hard-to-measure drivers are also acknowledged by academia and consulting.

The competitive advantage period and the financial innovation are qualitative measures

almost impossible to include in a quantitative study. Finally, EPS is not investigated by

consulting or academia but reported by 11 of the VBM banks. Since it is heavily

influenced by the stock market and expectations it is not tested in this analysis. In the

following Exhibit 8.1.1 a full scheme of the value drivers can be seen.


45

Exhibit 8.1.1: Mapping the Value Drivers


Koller (2010)

Derm

ine (2008)

Dam

odaran

(2009)

Multip

les

Equity

-based

mod

els

Fiord

elisi (2010)

Gross (2006)

Baele (2007)

Day

al et. al. (2010)

Day

al et. al. (2011)

Leich

tfuss et. al. (2010)

Duth

oit, et. al. (2011)

Olsen

et. al. (2010)

Visali (2011)

Deeld

er (2008)

Black

et. al. (1998)

Deu

tsche B

ank

Barclay

s

SEB

Nord

ea

UB

S

Lloy

ds

Ban

k of M

ontreal

CIB

C

Nation

al Ban

k of C

anad

a

Roy

al Ban

k of C

anad

a

Can

adian

Western

Toron

to Dom

inion

Pin

nacle F

inan

cial

First N

ational B

ankcorp

Stan

dard

Chartered

Ban

k of H

awaii

BO

K F

inan

cial

CV

B F

inan

cial

Brook

line

US B

ancorp

Total

Tier 1 ratio x x x x x x x x x x x x x x x x x x x x x 21

ROE x x x x x x x x x x x x x x x x x x x x 20

Cost-income ratio x x x x x x x x x x x x x x x x x x x x 20

LLR or LLP x x x x x x x x x x x x x x x x 16

IBIT growth x x x x x x x x x x x x x 13

EPS x x x x x x x x x x x 11

Business Mix x x x x x x x x x x x 11

ROA or RORWA x x x x x x x x x x 10

Revenue growth x x x x x x x x x x 10

Operational x x x x x x x x x 9

Assets, loans or deposits x x x x x x x x 8

NIM x x x x x x x 7

Geography mix x x x x x 5

Cost efficiency x x x x x 5

COE x x x x x 5

Regulatory x x x 3

Interest rate on products x x x 3

Loan Loss Coverage x x 2

Tax x x 2

GDP x x 2

Stock Market x x 2

SCA period x 1

CapEx x 1

Interest rates x 1

Revenue efficiency x 1

Competition x 1

Financial innovation x 1

Valuation Case BanksConsultingAcademia


46

In conclusion, the tier 1 ratio (risk measure), ROA (profitability measure), cost-income

ratio (efficiency measure), LLR (risk measure), business mix (diversification measure)

and revenue growth (growth measure) are some of the most reported measures across

academia, consulting literature and annual reports of VBM banks, for the full list of

variables included see Appendix 17.1.10. This also means that there is a common

knowledge regarding which value drivers that creates value, with a few exceptions. The

banks tend to focus on very intuitive and classical firm-specific measures, similar to those

applied in equity valuation-based literature whereas both consulting and academia also

seem to favour macro variables. The reason for this is that VBM banks’ KPIs need to be

understood by both employees and shareholders. Whereas academic articles are written

to a limited number of economic experts and the consulting reports are written to bank

managers. (Rappaport 1998)

It is clear that only small differences appear between the internal and external literature

and those found seems to occur due to low data availability. These small differences are

expected since all groups impact each other’s thinking.

47

Part III - Analysis

The overall purpose of Part III is to provide an answer to RQ3 and RQ4. In order to

answer RQ3 a multiple regression analysis is performed on the data set. The value

drivers found to be most significant are considered the key value drivers of retail banking.

To make the results reliable, extensive robustness checks are performed. Exhibit 9.1.1

summarizes the structure of Part III.

Exhibit 9.1.1: Structure of Part III


9. The Data Set

The data collected for this thesis is organized as a balanced panel data set containing

yearly observations for 132 banks from 2001 to 2011. The data has been gathered

following a strict process, ensuring that it only contains banks that have been listed since

2001 and are headquartered in either North America or Europe. For a detailed

presentation of the data selection process, see Appendix 13.2.1.

The general model used in the analysis contains indexed variables with subscript i for the

individual bank (i = 1,…,N) and t for all time periods (t = 1,…,T):

yit= β0 + x'itβ+ εit

The dataset

The model

Robustness

9.1 The conditions

9.2 Other issues

10.3 Robustness checks Univariate approach

Factor approach

Further analyses

10.1 The academia approach

10.2 The applied model

10.4 Reduced model including robustness checks

10.5 Correcting for expectations

11.1 Investigating VBM banks

11.2 Investigating top-performing banks

11.3 Prioritizing Profitability and Growth

11.4 Pre-crisis and crisis

11.5 North America and Europe

Testing for heteroskedasticity, autocorrelation and endogeneity

Estimation procedures:FD, FE, RE, 2SLS, GLS

Estimation procedures:FD, FE, RE, 2SLS, GLS

Split-sampling procedures that gives further understanding of value drivers

12. Construction of the value driver mapValue driver map

Summarizing results using the guidance of Rappaport (1998)

EQ 10

Part III – Analysis

48

The availability of repeated observations of the same unit allows for a more specific

model and makes the estimators more accurate than both cross sectional and time series

data (Verbeek 2009). (Baltagi 2001) lists numerous of other important advantages of

panel data:

• Panel data controls for individual heterogeneity and does therefore not run the

risk of obtaining biased results like time series or cross section might do.

• Panel data gives more informative data, more variability, less co linearity among

the variables, more degrees of freedom and more efficiency.

• Panel data is better able to study the dynamics of adjustment.

• Panel data is better able to identify and measure effects that are simply not

detectable in pure cross-section or pure time-series data. An example of this is

given by (Ben-Porath 1973).

• Panel data models make it possible to construct and test more complicated

behavioural models than purely cross-section or time-series data.

• Panel data gathered on micro units are more accurately measured and results in

less bias.

The complications when using panel data is more of a practical nature because it is no

longer appropriate to assume independence between the different observations. This

means that a time-constant unobserved effect, ai, is left in the error-term creating

endogeneity. An example of such an unobserved variable could be the cultural differences

between the banks or if some banks are better at attracting the most skilled employees.

However, several techniques are able to account for this (Wooldridge 2009).

There are three general techniques when estimating panel data (Wooldridge 2002). The

first two estimation techniques, first differencing (FD) and fixed effects (FE), are used if

the data shows sign of unobserved time constant effects. FD is build upon the difference

between two time periods. By subtracting one period from the other the time constant

unobserved effects, that by nature are identical in all periods, will be removed. Under the

assumption of E[uit-uit-1│xit-xit-1 ] = 0 the FD estimator is unbiased and consistent. The

second technique, FE estimation, is based on the same principle as first differencing, and

also uses a transformation to remove the unobserved effect ai before the estimation. Each

cross-section is averaged over time and by subtracting the averaged model from the

initial model the time-constant unobserved effect is removed.

The last technique is the random effects estimation (RE). This estimation assumes that

the model does not suffer from time-constant unobserved effects. If the correlation

between the explanatory variables and the unobserved time-constant effects is expected

to be zero (cov(xij,ai )=0) then removing the unobserved effects, as is done in the other

approaches, would result in inefficient estimates which is why the RE model has its


49

relevance (Wooldridge 2002). For further description of the different estimation methods

see Appendix 17.2.2.

9.1 The Conditions

For the three estimators to produce valid and reliable estimates the data needs to satisfy

a number of conditions. The full list of conditions can be seen in Exhibit 9.1.2. The task

at hand is to identify the most efficient estimator.

Exhibit 9.1.2: The conditions of the three estimation techniques

First differencing (FD) Fixed effects (FE) Random effects (RE)

1. yit = β0 + x’it β + ait + uit yit = β0+x’it β + ait + uit yit = β0 + x’it β + ait + uit

2. Random sample Random sample Random sample

3. Variable change over time Variables change over time No perfect linear relationships

4. Strictly exogenous No endogeneity No endogeneity

5. Homoskedasticity Homoskedasticity Homoskedasticity

6. Differences in errors are

uncorrelated, No SC

Errors are uncorrelated,

No serial correlation

Unobserved effects, uncorre-

lated with explanatory variable

7. Normally distributed Normally distributed

Source: (Wooldridge 2002), own contribution

Under their first four assumptions the FD and FE estimators are both unbiased and

satisfying the six first assumptions makes FD the best linear unbiased estimator (BLUE).

However, when choosing between the two estimation procedures it is found that when

the errors are serially uncorrelated the FE estimator is more efficient than the FD

estimator. However, when the serial correlation is negative the FE estimator is the most

efficient. Focusing on the RE estimator it is both consistent and asymptotically normally

distributed under the first six assumptions (Wooldridge 2002). Depending on which

conditions that are satisfied the preferred estimation procedure can be identified.

In the following sections tests upon condition 4-7 will only be performed on the preferred

model, described in Chapter 10. Condition tests on the other models can be found in

Appendices 16.2.3, 16.2.4 and 16.2.5.

9.1.1 No Endogeneity

Having looked at the time-constant unobserved effects in the start, this section discusses

the consequences of time-varying unobserved effects which can lead to an omitted

variable problem. When an omitted variable is correlated with the explanatory variables

it leads to endogeneity in the variables causing a violation of assumption 4, making all

estimators biased. An example of this has been seen on the Danish market, were Danske

Bank was the first bank to introduce mobile banking. It gave them a competitive

advantage, which impacted their profitability positively and created endogenous variables

(Wooldridge 2009). It was, however, only for a short period since the application was

easily copied by the competitors. As it is not possible to measure such competitive


50

advantage the model suffers from omitted variable problems. Since including the omitted

variable is not a solution other methods have to be considered. Most studies choose to

ignore the presence of endogeneity and they therefore either ignore the problem and

suffer the consequence of biased and inconsistent estimators or they assume that the

omitted variables does not change over time and therefore is removed by the use of FE or

FD.(Wooldridge 2009)

However, due to the non-transparent balance sheets of banks, the difficulty for external

analysts to estimate leverage variables and the very subjective way of announcing loan

loss provisions, as discussed in Chapter 3, it is suggested by the authors that the model

might suffer from omitted variables. It is suggested that these omitted variables are to be

found in the method of calculating capital and the bank leverage. Further, it is expected

that the stock market’s view towards such variables differs across time (higher weight in

crisis periods) and the impact they have on the variables therefore differs. A combined

test for endogeneity inspired by (Wooldridge 2002) is carried out in Appendix 17.2.3. In

this analysis it is found that the FE model show signs of endogeneity in some of the

independent variables seen in Exhibit 9.1.3.

Exhibit 9.1.3: Testing for Endogeneity and Finding Suitable Instruments

Estimator F-test Endogenous variable Instrument

FE 0.000

ROA ROE

Contingency Deposits to assets

Ln assets Risk cost

TSR(-1) MSCI Finance(-1)


Since ROA is dependent of the leverage, it is suggested that the omitted variables

causing ROA to be endogenous might be found in this particular area. From Chapter 4 it

was seen that ROE is correlated with ROA but independent of the bank’s leverage level

and is therefore applied as an instrument variable for ROA. Contingency is also found to

be endogenous which might be due to some kind of banking type problem. If the bank

has a certain structure or a culture for holding high amounts of liquid assets, this

banking type variable will be correlated with Contingency. As an instrument variable

deposits to total assets is applied since it correlates with contingency but is expected to

correlate less with the omitted variable. Also, Ln assets is found to be endogenous. As

discussed by (Khorana 2011) conglomerate discounts are often seen among large

corporations and this might cause the variable to be endogenous. Instead, risk cost is

tested as a suitable endogenous variable since it is expected to correlate less with the

conglomerate variable that is omitted but still correlate with the size of the bank.

Finally, TSR (-1) is by construction endogenous and MSCI Finance (-1) is used as an

instrument variable due to the high correlation between those variables.


51

Having discussed the variables, each endogenous variable is regressed on the instrument

variable and all are found to be significant explanatory variables, and therefore tried as

instrumental variable. See Appendix 17.2.3 for output data.

In order to handle the endogenous variables Two-Stage Least Squares (2SLS) is

conducted using suitable instrumental variables that are correlated with the endogenous

variable but uncorrelated with the error term (Wooldridge 2002).

Having identified the instrumental variables, it is necessary to test whether the

endogeneity problem is solved. This is done in the following way:

• Estimate the structural equation by 2SLS and obtain the 2SLS residuals, ûi

• Regress ûi on all exogenous variables. Obtain the R-squared

• Under the null hypothesis that all IVs are uncorrelated with ui, R12 ~ a Χq

2, where

q is number of instrument variables minus the number of endogenous independent

variables.

With 1320 observations and R12 = 0.00064 the test-statistic is 0.8448 meaning that the

null hypothesis cannot be rejected and the instruments are exogenous in the fixed effects

model.

9.1.2 Homoskedasticity

The standard errors have to be homoskedastic in order for the estimators to produce

unbiased test-statistics. Under the homoskedasticity assumption it is stated that the

variance of the unobserved error, conditional on the explanatory variables, is constant.

Without this assumption the model suffers from heteroskedasticity, making the estimator

of the variances and the t-test biased (Wooldridge 2009). To test for the presence of

heteroskedasticity both the Breusch-Pagan test and a study of the residual plots is

applied. The results can be seen in Exhibit 9.1.4 where it is confirmed that the standard

errors suffer from heteroskedasticity. For a more detailed discussion on heteroskedasticity

and the tests see Appendix 17.2.4.

Exhibit 9.1.4: The Tests for Heteroskedasticity

Test FE

Breusch-Pagan F-test 5.148

P-value 0.000

Plot Clear sign

Source: Bloomberg, Own Contribution

As both the residual plot and the Breusch-Pagan test shows signs of heteroskedasticity,

robust standard errors are applied throughout the analysis. This is in accordance with

(White 1980).


52

9.1.3 No Serial Correlation

Serial correlation in linear panel data models biases the standard errors and causes the

results to be less efficient meaning that it is neither a best linear unbiased estimator nor

are the test-statistics asymptotically valid. For further information on autocorrelation see

Appendix 17.2.5.

A test, designed specifically for testing autocorrelation in fixed effects models and

recommended by (Bhargava, Franzini & Narendranathan 1982) and (Verbeek 2009) is

conducted. The test makes a correction for the classical Durbin-Watson in order to apply

it on panel data because it allow the upper and lower limits of the Durbin-Watson Test

to depend on time (T), number of companies (N) and number of variables (K). The

upper and lower bounds used in the analysis are found in (Bhargava, Franzini &

Narendranathan 1982). The most suitable option was to apply N=100, T=10 and K=9

which yields the results illustrated in Exhibit 9.1.5.

Exhibit 9.1.5: The Durbin-Watson Test for Serial Correlation

DW Lower Bound Upper Bound Conclusion

Positive serial correlation 2.15 1.878 1.916 Reject

Negative serial correlation 1.85 1.878 1.916 Accept Source: Bloomberg, own contribution

The test shows that the model suffers from negative autocorrelation at a 5% significance

level while the test for positive autocorrelation was rejected. In order to cope with the

problems arising from heteroskedasticity and serial correlation robust standard errors will

be used in the reminder of the analysis, a procedure recommended by (Wooldridge 2002)

and (White 1980). With the substantial negative autocorrelation, FE estimator is

suggested to be more efficient than the FD estimator (Wooldridge 2009). However,

(Wooldridge 2009) recommends that both models are applied in order to yield further

insights. For further guidance on the Durbin-Watson Test for panel data, see (Verbeek

2009) and (Bhargava, Franzini & Narendranathan 1982).

9.1.4Normally Distributed Errors

The last assumption check tests for normally distributed errors. In order to confirm

whether or not the errors of the model have the skewness and kurtosis matching a normal

distribution, the Jarque-Bera Test is performed. Exhibit 9.1.6 shows a plot of the

standardized residuals.


53

Exhibit 9.1.6: The Standardized Residuals

Source: Bloomberg, Eviews output

The normal distribution has a skewness of zero and a kurtosis equal to three while errors

have a skewness of 0.311 and a kurtosis of 5.804. This yields the following test statistic:

JB = n6 @SA + 1

4 (K − 3)AE = 13206 @0.311823A + 1

4 (5.804015 − 3)AE = 453.83

Even though the data, just by looking at it, suggest normally distributed errors, the test

rejects that the sample has the skewness and kurtosis matching a normal distribution.

However, the fact that the sample does not match the normal distribution is not a

problem due to the large sample size (Wooldridge 2009, Verbeek 2009).

9.1.5 Summing up the Assumptions Check

The conclusion to these assumptions tests is that there are problems with all four main

assumptions. As was discussed there are however methods that can be applied in order to

deal with this. To sum up the findings:

• Key explanatory variables are endogenous and 2SLS, with the identified

instrumental variables, is necessary in order to produce unbiased results. The

instrumental variables have been identified following the guidance of (Wooldridge

2009).

• The models suffer from heteroskedasticity and serial correlation. To cope with this

robust standard errors, recommended by (White 1980), (Verbeek 2009) and

(Wooldridge 2009) are used.

0

50

100

150

200

250

300

-1.5 -1.0 -0.5 0.0 0.5 1.0

Series: Standardized ResidualsSample 2002 2011Observations 1320

Mean 3.57e-19Median -0.013352Maximum 0.992496Minimum -1.407938Std. Dev. 0.219789Skewness 0.311823Kurtosis 5.804015

Jarque-Bera 453.8287Probability 0.000000

EQ 11


54

• The standard errors are not normally distributed but with a large sample size this

assumption does not yield any major issues (Wooldridge 2009).

This means that the preferred estimator is the FE estimator with 2SLS implemented to

take care of the endogeneity problem.

9.2 Other Issues

Having investigated the assumptions, some issues still need to be addressed in order to

secure the best possible model. The thesis focuses on making a comprehensive study of

both practitioners such as equity analysts, management consulting houses, the banks and

academia. These findings have resulted in numerous potential value drivers of bank

value. The large number of variables poses an issue that needs to be considered before

the different model building procedures are applied.

Since the model in this thesis initially has many explanatory variables, some of them are

almost bound to be correlated (Wooldridge 2009). Correlation among the dependent

variables leads to multicollinearity which produces unreliable estimates through large

variance in the beta estimates (Wooldridge 2009) which is just as severe as having a

small sample size. Since it is not a direct violation of any of the model assumptions the

literature argues that it is not a problem (Goldberger 1991). Although the problem of

multicollinearity is not clearly defined one thing is clear, all else being equal less

correlation is better (Wooldridge 2009) and some tests are able to give hints on whether

to draw attention to the multicollinearity problem or not. Even though it is not possible

to specify an acceptable amount of correlation (Brooks 2003) suggests four different

solutions for problems regarding multicollinearity:

• Ignore it

• Drop one of the collinear variables

• Transform the highly correlated variables into a ratio

• Collect more data – increase sample period or frequency

(Wooldridge 2009) suggests that most analysis should not consider multicollinearity,

however, this study has to consider it due to the mathematical connection between many

of the variables. The multicollinearity is assessed in each of the models but only in order

to understand the dynamics of the variables. Only in extreme cases will correlating

variables be excluded. For further discussion on the matter, see Appendix 17.2.6.

9.2.1 Robustness

Another issue is the robustness of the findings. A common exercise in econometric

analysis, and recommended by (White, Lu 2010), is to test the core regression coefficients

by adding and removing regressors. If the coefficients are plausible and robust throughout


55

the procedure they are considered valid (White, Lu 2010). In the study this exercise has

been performed throughout all the analyses with a focus on the sign and the level of the

core variables. The method for making robustness checks applied in this thesis is

suggested by (Fiordelisi, Molyneux 2010, Gross 2006) where the data is applied on a wide

variety of models to see how they differ in their findings. For this univariate and two

types of factor analysis is applied.

Further, using robust standard errors can result in efficiency loss when the

heteroskedasticity is weak. (Wooldridge 2009) recommends using generalized least

squares weights in the estimation when this is expected. As a consequence, generalized

least squares (GLS) estimation will be added as a robustness check.

10. Analyses

Having found the value drivers to test, the estimation procedures to use and examined

the conditions, the next step is to perform the estimation. Since 47 variables are too

many to include in a single multiple regression model due to their mathematical nature

as discussed in Chapter 9, different ways of bringing this number down is applied. The

standard approach among academic literature is to discuss the potential variables from

an economic perspective and then choose those that make most sense, see (Fiordelisi,

Molyneux 2010, Rapp et al. 2011, Fiordelisi 2007). This approach is the preferred method

for this thesis, but will be accompanied by some more statistical methods as discussed in

Chapter 9.

10.1 The Academic Approach

In the light of articles like (Fiordelisi, Molyneux 2010, Rapp et al. 2011, Fiordelisi 2007)

the preferred approach, called the academia approach, for this thesis is to discuss each

variable from an economic view and through the knowledge gained about the banking

sector in Chapter 3 combined with a view towards the value driver scheme seen in

Exhibit 8.1.1. By looking at both what the different groups suggest and combine it with

economic sense, it is believed that the best suited model can be constructed.

However, before choosing the variables they will be classified into eight groups

constructed by the authors. The grouping is done in order to avoid a high correlation

among the variables (Hair 2009). They are named profitability, growth, risk cost,

liquidity, efficiency, bank type and two control variables that consist of macro variables

and market variables. In Exhibit 10.1.1 the different groups can be seen, a thorough

description of each variable can be found in Appendix 17.1.10.


56

Exhibit 10.1.1: The Eight Variable Categories

Profitability Growth Risk cost Efficiency

ROE G_REV NPL_ASSETS CI_RATIO

NIM G_LOAN NPL_LOANS EXP_ASSETS

ROA G_DEP LLR LN_ASSETS_EMP

RORWA G_ASSETS LLC LN_LOANS_EMP

IBIT_MARGIN NPL_COV LN_NET_REV_EMP

ECO_PROF LLP_INT_INC

G_IBIT RWA_ASSETS

Deposit int rate R_E

RISK_COST

RISK_INCOME

WACC

TIER1

EQ_ASSETS

Liquidity Bank specific Control (Macro) Control (Stock Market)

LOANS_ASSETS Ln assets TAX_RATE PE_RATIO

CONTINGENCY Business mix 10 year PB_RATIO

DEP_ASSETS 5 year MSCI_FINANCE

LOANS_DEP 2 year

HH_INDEX

G_GDP

ASSET_GDP

EUROPE


Before looking at the value drivers the control variables first of all need to be identified.

Studies like (Fiordelisi, Molyneux 2010) and (Gross 2006) includes variables to control for

the macro environment. In this study it is also necessary to control for the stock market

and expectations because of the use of TSR as the dependent variable (Koller, Goedhart

& Wessels 2010). From the market control variable PB ratio is, based on the discussion

in Chapter 4, chosen over PE ratio as the preferred expectation variable which is in line

with what (Damodaran 2009) and (Fama, French 1992) suggests. Further, research has

found that industry structures affect the performance of the individual company (Porter

1980). In order to correct for this the sector return, MSCI Finance, is applied as a proxy

variable (Koller, Goedhart & Wessels 2010). Finally, (Rapp et al. 2011, Dey 2008) and

(Wooldridge 2009) suggest including a lagged dependent variable which is why TSR -1 is

incorporated throughout all the analyses.


57

10.1.1 Profitability

The procedure for finding the most suitable profitability measure is influenced by (Rapp

et al. 2011). In this method each profitability measure is used as an independent variable

together with the variables from the other groups. The preferred variable is the variable

producing the test with the highest R squared. In Exhibit 10.1.2 this method can be seen.

Exhibit 10.1.2: The Investigated Profitability Measures

Source: Own contribution. * = Significant at 10%, ** = Significant at 5%, *** = Significant at 1%

Even though IBIT margin actually has a slightly higher R squared than ROA, it is

rejected as the best profitability measure due to the unnatural positive sign of Cost-

income-ratio. From an economic view it is not possible to argue why higher cost-income

ratio should increase TSR. It might be suggested that ROE should be included as the

preferred profitability variable due to its high number of appearances across all types of

literature (mentioned in 18 of the studies). However, ROA is besides being a better

estimator from a statistical point of view also a better variable from an economic

perspective. This is seen from the following formula

ROA = ROE·Equity/Assets

y = TSR y = TSR y = TSR y = TSR y = TSR y = TSR y = TSR y = TSR

ROA 7.317***[2.347]

ROE 0.361***[0.120]

IBIT growth 0.0356***[0.008]

Economic Profit 3.96*10^-8***[9.37*10^-9]

Net interest margin 0.435[1.92]

RORWA 0.015[0.017]

IBIT margin 0.554***[0.104]

Deposit interest rate -0.246[0.217]

Revenue growth 0.230*** 0.189*** 0.180*** 0.283*** 0.288*** 0.288*** 0.267*** 0.280***[0.039] [0.043] [0.047] [0.044] [0.044] [0.044] [0.048] [0.046]

Cost-income ratio -0.126* -0.149 -0.148 -0.135 -0.164* -0.165* 0.303*** -0.168*[0.073] [0.110] [0.107] [0.089] [0.090] [0.089] [0.091] [0.091]

LLC 0.0020*** 0.0015*** 0.0012*** 0.0013*** 0.0014*** 0.0014*** 0.0022*** 0.0014***[0.0002] [0.0002] [0.0002] [0.0002] [0.0002] [0.0002] [0.0003] [0.0002]

Contingency 0.108*** 0.134*** 0.142*** 0.103*** 0.106*** 0.105*** 0.100*** 0.114***[0.036] [0.041] [0.039] [0.036] [0.038] [0.037] [0.035] [0.03]

Tier 1 Ratio 0.759 0.734 0.846* 1.225*** 1.105** 1.094** 1.079** 1.050**[0.479] [0.483] [0.467] [0.473] [0.476] [0.476] [0.496] [0.478]

Price-book ratio 0.109** 0.110** 0.125** 0.125** 0.126** 0.126** 0.104** 0.126**[0.054] [0.055] [0.059] [0.060] [0.060] [0.060] [0.052] [0.060]

Ln assets -0.134*** -0.144*** -0.147*** -0.135*** -0.139*** -0.141*** -0.132*** -0.141***[0.034] [0.035] [0.037] [0.037] [0.036] [0.037] [0.033] [0.037]

2 year -6.39*** -6.16*** -5.14*** -5.86*** -5.75*** -5.76*** -6.72*** -5.63***[0.748] [0.725] [0.745] [0.753] [0.770] [0.759] [0.781] [0.766]

MSCI Finance 0.530*** 0.533*** 0.515*** 0.525*** 0.528*** 0.528*** 0.556*** 0.522***[0.056] [0.056] [0.057] [0.058] [0.058] [0.058] [0.055] [0.059]

GDP growth 2.713*** 2.903*** 2.49*** 3.30*** 3.35*** 3.36*** 2.29*** 3.32***[0.650] [0.648] [0.718] [0.706] [0.710] [0.710] [0.654] [0.713]

TSR(-1) -0.302*** -0.295** -0.275*** -0.271*** -0.270*** -0.271*** -0.315*** -0.272***[0.028] [0.028] [0.033] [0.031] [0.031] [0.031] [0.027] [0.031]

R squared 0.556 0.545 0.549 0.530 0.527 0.527 0.558 0.527

EQ 12


58

It is clear that the information from ROA both tell the stock market something about

ROE but it also yields information regarding the leverage. As discussed in Chapter 3 the

leverage has had an increasing focus since the start of the crisis and ROA is therefore the

preferred profitability variable.

10.1.2 Growth

The preferred growth measure is identified using the same procedure as applied in the

profitability group. It is found that revenue growth is the preferred variable both based

on Exhibit 10.1.3 where it is the variable that produces the model with the highest R

squared but also based on the value driver scheme from Chapter 8 where it is the most

suggested growth variable. Further, (Dermine 2008) supports it as being an important

value driver.

Exhibit 10.1.3: The Investigated Growth Measures

Source: Own contribution. Explanation: * = Significant at 10%, ** = Significant at 5%, *** = Significant at 1%

10.1.3 Risk cost

The risk cost group is further broken down into two groups: cost variables that are

directly connected to the cost of defaulted loans and cost variables that are connected to

the cost of holding equity. For the latter group Tier 1 is chosen since it is the most

discussed variable across all types of literature and the authors cannot find any economic

y = TSR y = TSR y = TSR y = TSR

Revenue growth 0.230***[0.039]

Asset growth 0.134***[0.048]

Deposit growth 0.087*[0.048]

Loan growth 0.142***[0.046]

ROA 7.317*** 8.000*** 8.389*** 7.988***[2.347] [2.459] [2.459] [2.438]

Cost-income ratio -0.126* -0.169** -0.167** -0.177**[0.073] [0.077] [0.077] [0.099]

LLC 0.0020*** 0.0016*** 0.0016*** 0.0016***[0.0002] [0.0002] [0.0002] [0.0002]

Contingency 0.108*** 0.071 0.084* 0.083*[0.036] [0.045] [0.044] [0.045]

Tier 1 Ratio 0.759 0.877* 0.775 0.890*[0.479] [0.494] [0.498] [0.493]

Price-book ratio 0.109** 0.112** 0.113** 0.110**[0.054] [0.056] [0.056] [0.056]

Ln assets -0.134*** -0.134*** -0.131*** -0.136***[0.034] [0.036] [0.036] [0.036]

2 year -6.394*** -6.522*** -6.567*** -6.656***[0.748] [0.757] [0.752] [0.758]

MSCI Finance 0.530*** 0.558*** 0.554*** 0.561***[0.056] [0.058] [0.059] [0.058]

GDP growth 2.713*** 2.209*** 2.349*** 2.282***[0.650] [0.641] [0.651] [0.638]

TSR(-1) -0.302*** -0.311*** -0.308*** -0.312***[0.028] [0.029] [0.029] [0.029]

R squared 0.556 0.541 0.539 0.543


59

reasons that favorise the other variables. From the former group, loan loss coverage is

picked. This is done from an economic point of view because it focuses on both this year’s

loss and future expected losses, as opposed to pure provisions for loan loss. Further it

seems as good proxy for risk control.

10.1.4 Efficiency

Even though efficiency might be correlated with profitability, the cost-income ratio is

included because of its wide application among the different types of literature as the

second most preferred value driver from Exhibit 8.1.1. Further, the variable has seen an

increase during the crisis and as the consulting literature emphasizes it is a key area to

focus on in order to increase shareholder value.

10.1.5 Liquidity

As discussed in Chapter 3, liquidity will have a large impact on the banking sector in the

coming years due to the regulators fear of bank runs (Basel Committee on Banking

Supervision 2010). Therefore, the variable chosen is the one called contingency since it by

its construction, can be seen as a bank run defence variable. As described in Appendix

17.1.10 it is constructed by taking liquid assets divided by customer deposits. It is

therefore a strong measure for how large a portion of the customers that can withdraw

their money before the bank dries out. It is preferred above the other variables since they

do not have this focus on the banks liquidity.

10.1.6 Bank Type

Ln assets is included as a proxy for size, the intuition behind is influenced by (Rapp et

al. 2011) and (Fama, French 1992). Further, even though business mix is suggested by

the literature all the analyses conducted in this thesis find it extremely insignificant and

it is therefore not applied in the model. Further, as discussed in Chapter 3, (Walter 1997)

finds that mainly the large banks have a diversified portfolio and it may be argued that

business mix is therefore covered by Ln assets.

10.2 The Applied Model

Having included all the proposed variables in a regression model some interesting findings

can be seen both in regards to the significant variables but also the insignificant variables

yield some interesting information. The academia approach tests the following model:

TSRt = ROAt + gRevt + LLCt + PB ratiot + 2year + MSCI_Financet +

gGDP + TSRt-1 + νit

EQ 13


60

Exhibit 10.2.1 shows the results of the academia approach. The results from fixed effects

(FE), first differencing (FD), random effects (RE), two-stage least squares (2SLS) and

generalized least squares (GLS) are all reported.

Exhibit 10.2.1: The Applied Model

FE FD RE FE (2SLS) FE (GLS)

ROA 7.317*** 5.680** 6.818*** 8.314*** 7.388***

[2.347] [2.595] [1.929] [2.504] [1.205]

Revenue growth 0.230*** 0.172*** 0.258*** 0.217*** 0.203***

[0.039] [0.037] [0.036] [0.043] [0.027]

Cost-income ratio -0.126* -0.111 -0.078*** -0.116* -0.131**

[0.073] [0.070] [0.033] [0.070] [0.058]

LLC 0.0020*** 0.0007*** 0.0020*** 0.0019*** 0.0018***

[0.0002] [0.0002] [0.0002] [0.0002] [0.0001]

Contingency 0.108*** 0.039 0.047*** 0.102 0.057**

[0.036] [0.050] [0.016] [0.113] [0.027]

Tier 1 Ratio 0.759 1.582*** -0.052 0.631 0.957***

[0.479] [0.565] [0.317] [0.513] [0.350]

Price-book ratio 0.109** 0.136 0.072*** 0.118* 0.162***

[0.054] [0.089] [0.021] [0.068] [0.021]

Ln assets -0.134*** -0.152*** -0.002 -0.059 -0.074***

[0.034] [0.034] [0.005] [0.149] [0.023]

2 year -6.394*** -6.51*** -5.22*** -6.51*** -6.72***

[0.748] [0.843] [0.543] [0.752] [0.559]

MSCI Finance 0.530*** 0.508*** 0.582*** 0.544*** 0.435***

[0.056] [0.061] [0.045] [0.060] [0.030]

GDP growth 2.713*** 4.807*** 2.653*** 2.804*** 2.548***

[0.650] [0.736] [0.548] [0.639] [0.413]

TSR(-1) -0.302*** -0.558*** -0.180*** -0.300*** -0.317***

[0.028] [0.022] [0.028] [0.029] [0.021]

R squared 0.556 0.594 0.438 0.550 0.438

Source: Own contribution. * = Significant at 10%, ** = Significant at 5%, *** = Significant at 1%

Since the insignificant variables are included in this model, it is not possible to give a full

interpretation of the coefficient (this will be done in Section 10.4). However the sign and

the significance level are important and do not differ from the reduced model. First of all,

an overall look at the model yields not many surprises regarding the sign of the variables.

ROA, revenue growth, CIR and LLC all have the “right” sign from an economical point

of view, with ROA being the most significant variable. Further, contingency also have a

positive sign which is in line with Chapter 3 that emphasize the great focus this number


61

has received since the introduction of new liquidity requirements. Most remarkable is Ln

assets which state that increasing size will have a negative impact on TSR. Literature

such as (Goodhart 2011) and others have argued that bank managers would increase the

assets making the banks to-big-to-fail and thereby increase shareholder value and their

own bonuses. Even though the argument sounds plausible it cannot be supported by the

data in this study. Instead, the negative sign on Ln assets follows the findings from

(Fiordelisi, Molyneux 2010). The conclusion therefore might be that the disadvantages

suggested by (Walter 1997) and (Baele, De Jonghe & Vander Vennet 2007) in relation to

size, weigh more than the advantages. Another interesting variable is the Tier 1 ratio

that is found to be insignificant. The reason might be that even though controlling for

the capital ratio is important, most banks in the study already have tier 1 ratio above

the requested rate (only few have across the eleven years had Tier 1 ratio lower than 6

%) and therefore further increasing the ratio does not add extra value.

Looking at the control variables it is clear to see by their significance level that, as

discussed in Chapter 3, bank performance is very dependent of the overall economy and a

large part of the variance in TSR is also explained by external factors. It might be worth

noticing the negative sign on the interest rate. The reason for this might be found in the

maturity mismatch between assets and liabilities discussed in Chapter 3. When banks are

borrowing at the short interest rate and lending at the long interest rate, an increase in

the short rate will increase costs.

An argument for expecting a positive sign might however be connected to floor risk

which is the interest rate risk on those deposits, loans and advances whose interest rates

depend on the central banks leading interest rate. Since it is not possible for the bank to

lower some of those entries at the same pace as the central bank, it will have a negative

effect on the banks performance (Danske Bank 2012). The results, however, indicates the

net effect being negative.

10.3 Robustness Check using Different Model Specification Methods

Having found the variables for the preferred model this section will take a more

statistical way of finding those variables that best describe TSR. The first method is

called the univariate method and the last two are the “Factor analysis model – Factor”

and “Factor analysis model – Surrogate”, the former testing whether the different overall

groups picked in the preferred model can be justified and the latter testing the robustness

of the variables found.

10.3.1 Univariate Analysis

Univariate is the simplest form of quantitative analysis as it describes the dependent

variable based on a single variable (Babbie 2010). In the univariate method TSR is

regressed on each of the 46 variables by applying the univariate approach. The variables

with a significant impact on TSR on a 5% significance level are kept and sorted by their


62

impact on TSR. From each overall group the variable with the highest impact from each

group is chosen as the preferred variable. By applying a standard multiple regression

analysis, following the guidance of (Wooldridge 2002, Wooldridge 2009), the total

number of variables are reduced to the 11 variables listed in Exhibit 10.3.1.

Exhibit 10.3.1: The Chosen Variables from the Univariate Analysis

Coefficient

(univariate)

Prob.

(univariate)

R squared

(univariate)

Category

ROA 11.497 0.000 0.168 Profitability

Revenue Growth 0.231 0.007 0.112 Growth

CI ratio -0.382 0.000 0.0979 Efficiency

Loans-to-deposits -0.134 0.005 0.079 Liquidity

NPL-to-assets -6.222 0.000 0.136 Risk

Ln assets -0.243 0.000 0.152 Bank specific

PB ratio 0.167 0.000 0.217 Control: Expectations

HH INDEX 0.000 0.005 0.0778 Control: Competition

MSCI FINANCE 0.600 0.000 0.283 Control: Stock market

5 Year 3.334 0.001 0.0821 Control: interest rate

GDP growth 2.261 0.000 0.0871 Control: Economy


The full univariate analysis can be found in Appendix 17.2.7. The analysis reveals that

many of the variables are similar to what was found in the academia model. This means

that the purely analytical univariate approach confirms the results of the less analytical

academia approach.

10.3.2 Factor Analysis

Compared to previous studies on shareholder value in banks this thesis studies a

significantly higher number of variables, see (Fiordelisi, Molyneux 2010, Gross 2006), etc.

Therefore an increased knowledge of the structure and the interrelationships between the

explanatory variables is needed. A factor analysis is suitable for testing some of these

multidimensional relationships and examine whether the information can be summarized

in a smaller number of factors (Hair 2009).

In Exhibit 10.3.2 the findings from the factor analysis can be seen. For a full discussion

on how the factor analysis is conducted see Appendix 17.2.8.


63

Exhibit 10.3.2: The Factor Analysis Categories

Component

1 2 3 4 5 6 Component name

ROA -.730

LLR .909

LLP_int_inc .933

IBIT_margin -.719

Ln_assets_emp .886

Ln_loans_emp .954

Ln_net_rev_emp .709

Business_mix -.897

Contingency .708

Ln_assets .734

g_rev .788

g_loan .944

g_dep .915

g_assets .899

exp_assets .716

Loans_assets .864

RWA_assets .891

10_year .703

5_year .922

2_year .858

Source: Bloomberg, Own contribution

The factor analysis investigates the 47 variables and identifies six factors which support

the overall groups chosen by the authors, see Section 10.1. The only variable in the

analysis that is somehow hard to explain is contingency. It was suggested by the authors

that it should have been part of the liquidity group, but the factor analysis specify it as a

bank type variable. It should be noted that it is not possible to compare the sign of the

variables due to the construction of a factor analysis. E.g. profitability have a negative

sign when including it in a regression but as can be seen from Exhibit 10.3.2, ROA and

IBIT margin have negative factor loadings whereas LLR and LLP have positive factor

loadings.

Having tested the robustness of the overall groups the next step is to identify the

appropriate variables since this is the main objective of the study. This is done by

selecting one variable from each group to work as a surrogate variable for the whole

factor. Since all of the variables have a high loading on each factor it is suggested by

(Hair 2009) to choose the surrogate variable based on priori economic knowledge.

However, the preferred model is based on economic knowledge, therefore the surrogate

Profitability

Cost efficiency

Bank type

Growth

Macro

Liquidity


64

variables will be chosen based on the univariate analysis from before. For a more

thorough description of the approach and a description of the final factor model see

Appendix 17.2.8.

10.4 The Reduced Model Including Robustness Checks

In this section, the results of the previous discussed analyses are collected in order to

answer RQ3 and the economic meaning of the findings is discussed. Exhibit 10.4.1 shows

all the different analyses and the corresponding findings at a 5% significance level. Since

the preferred model is the academia FE 2SLS approach that both take heteroskedasticity

and endogeneity into account, the findings in this model are the most interesting.

However, the other models work as robustness checks, and the range and the median

yields information regarding the quality of the findings.

Exhibit 10.4.1: The Reduced Model with Robustness Checks


Looking at the preferred model (Academia FE 2SLS), findings suggest that a one

percentage point increase in ROA will increase TSR with 8.44 percentage points. Even

though the sign is supported by (Fiordelisi, Molyneux 2010, Fiordelisi 2007) and the level

is backed up by all the other models, the level might seem low from an economic point of

view. Putting it into contents with ROE, JP Morgan reported a ROA of 0.84% and a

corresponding ROE of 10.6% in their 2011 annual report. Therefore, holding everything

else constant, an increase in ROA by 1 percentage point would mean more than doubling

JP Morgan’s ROE to approximately 22.6%. In Chapter 4 it was further discussed that as

a rule of thumb, the stock market calculates the PB ratio as the normalized ROE divided

by the ROE that the stock market requires. This indicates that the coefficient in front of

ROA is too low. Further, the valuation model constructed in Appendix 17.2.9 indicates

that the fair price would increase by 100% for the constructed Bank ABC if the return of

ROARevenue

growth

Contin-

gency

Ln

assets

PB

ratio2 year

MSCI

Finance

GDP

growthTSR(-1) LLC 5 year

NPL

Assets

Tier 1

ratio

CI

ratio

HH

index

Business

mix

Expenses

to assets

Ln Net

Revenue

Emp

FE 7.778 0.241 0.120 -0.130 0.105 -6.817 0.548 2.754 -0.296 0.002

FD 6.063 0.209 -0.180 -6.061 0.575 5.743 -0.561 0.001 1.395

2SLS 8.440 0.200 0.124 -6.952 0.563 3.029 -0.289 0.002

GLS 7.388 0.203 0.057 -0.074 0.162 -6.718 0.435 2.548 -0.317 0.002 0.957 -0.131

FE 6.074 0.130 -0.157 0.080 0.538 -6.298 -0.143 0.000

FD 0.194 0.691 3.247 -9.481

2SLS 7.288 0.173 0.058 0.480 -2.928 -1.425

GLS 4.950 0.137 -0.115 0.106 0.492 -6.225 -0.175 0.000 0.168

FE 7.440 0.226 -0.150 0.106 -7.268 0.559 2.934 -0.290 -3.303

FD 6.087 0.215 -0.337 0.683 5.956 -0.559 -10.795 -5.599

2SLS 8.271 0.211 0.126 -6.980 0.572 3.098 -0.289

GLS 7.237 0.179 -0.109 0.156 -7.461 0.454 2.844 -0.319 -4.336 0.086

7.002 0.193 0.088 -0.156 0.114 -6.894 0.549 3.573 -0.365 0.002 -7.145 -1.425 1.176 -0.150 0.000 0.168 -4.413 0.086

Low 4.950 0.130 0.057 -0.337 0.058 -7.461 0.435 2.548 -0.561 0.001 -10.795 -1.425 0.957 -0.175 0.000 0.168 -5.599 0.086

Median 7.288 0.202 0.088 -0.140 0.106 -6.952 0.553 3.029 -0.306 0.002 -6.298 -1.425 1.176 -0.143 0.000 0.168 -4.336 0.086

High 8.440 0.241 0.120 -0.074 0.162 -6.061 0.691 5.956 -0.289 0.002 -2.928 -1.425 1.395 -0.131 0.000 0.168 -3.303 0.086

*All variables are significant at a 5% significance level

Average

ACADEMIA

UNIVARIATE

FACTOR


65

equity was doubled. An explanation to this might be that one year’s increase in ROA

does not have full effect on the share price. The stock market wants steady numbers over

a period of time before it is fully accounted for in the stock price (Koller, Goedhart &

Wessels 2010). Further, a 1% point increase in ROA is very extreme and the data might

not be able to capture the full effect at these extreme points (Wooldridge 2009).

Revenue growth is the only variable included in all the different models. With a

coefficient of 0.20 it indicates that TSR will increase 0.2 percentage points for a 1

percentage point increase in revenue growth. This is in line with (Koller, Goedhart &

Wessels 2010) and also very close to the median of all the models.

Since both ROA and revenue growth are significant in almost all of the models and that

they are the most significant variables within each model it supports the findings in

(Koller, Goedhart & Wessels 2010) regarding ROIC and growth as the main value drivers

and these conclusions can therefore be transferred to the banking industry.

Further, this reduced model emphasizes the importance of the external control variables.

The price-book ratio which has the main objective of controlling for expectations is

significant with a coefficient of 0.125. The interpretation of the coefficient is straight

forward. When the price-book ratio increases 1 percentage point TSR is raised by 0.125

percentage points. According to (Koller, Goedhart & Wessels 2010, Dermine 2009, Gross

2006) banks are very sensitive to the world economy and this is confirmed by the results.

The 2 year interest rate variable has a coefficient of –6.952 and is therefore a very

significant value destroyer. When the 2 year interest rate appreciates 1 percentage point

the total shareholder return decreases 6.952 percentage points. As expected GDP growth

have a positive impact on TSR. The coefficient of 3.092 suggests that 1 percentage point

growth in the domestic economy affects TSR positively by 3.092 percentage points. The

stock market is controlled for using the MSCI Finance index. As expected an increase in

the index has a positive effect on the value creation. For guidance on interpretation of

the coefficient see (Wooldridge 2009). One last robustness check is, however, necessary

according to (Koller, Goedhart & Wessels 2010). This is performed in the following

section.

10.5 Correcting for Expectations

As can be seen from the analysis conducted in Section 10.4 expectations have a large

impact on shareholder value creation. In connection to this (Koller, Goedhart & Wessels

2010) argues that TSR in the short run is determined by performance against

expectations and not only absolute performance. (Rappaport 1998) also states that

expectations play an important part in determining the stock price. An example from the

sample is US Bancorp who in 2011 demonstrated financial strength with a return on

assets of 1.43% and a revenue growth of 4.73% but the total return for shareholders in

2011 was still -27.17%. Future expectations simply weighed more even though median

ROA and revenue growth across the sector were 0.64% and 0.19% respectively in 2011.


66

According to (Koller, Goedhart & Wessels 2010) realized TSR has the following short-

term key drivers:

• Initial expectations

• Realized return on capital and growth

• Changes in expectations

Over longer time spans TSR is not as strongly influenced by changes in expectations

(Koller, Goedhart & Wessels 2010). In order to control for expectations over the long-

term and test whether the main drivers are significant four different analyses have been

conducted. In the following Exhibit 10.5.1 the 1, 3, 5 and 10 year analysis can be seen.

Exhibit 10.5.1: Controlling for Expectations

TSR 1 year TSR 3 year TSR 5 year TSR 10 year

Coefficient P-value Coefficient P-value Coefficient P-value Coefficient P-value

ROA 6.733 0.001 8.704 0.000 9.516 0.000 15.131 0.000

G_Rev 0.147 0.000 0.115 0.078 0.346 0.000 0.608 0.000

G_IBIT 0.032 0.000 0.003 0.152 0.015 0.000 -0.002 0.310

Contingency 0.151 0.000 0.114 0.002 0.033 0.001 0.038 0.009

PB_Ratio 0.107 0.017 0.031 0.038 0.016 0.022 -0.019 0.200

LNAssets -0.138 0.000 -0.259 0.000 -0.004 0.037 -0.016 0.006

_2_YEAR -6.126 0.000 -8.493 0.000 -6.239 0.000 3.457 0.079

G_GDP 2.096 0.001 -0.617 0.317 2.337 0.000 5.075 0.008

MSCI -

Finance 0.535 0.000 0.750 0.000 1.006 0.000 12.363 0.045


It is clear that over a 10 year period (long term) TSR is more linked to the fundamental

drivers than it is to expectations. Especially between TSR and ROA is there a very

significant causal relationship, but also revenue growth is significant throughout most of

the periods. Another interesting finding is the fact that the coefficient on the price-book

ratio becomes smaller and smaller as the time span increases. This result confirms the

findings of (Koller, Goedhart & Wessels 2010) since changes in expectations tend to

matter less as the time span, over which TSR is measured, increases. The results confirm

that the findings from Section 10.2 are robust even when correcting more efficiently for

expectations.


67

11. Further Analyses

In the following analyses several related questions will be analyzed in order to get a

greater insight into shareholder value creation and the value drivers. First, it will be

tested whether the VBM banks identified in Chapter 7 outperform the other banks in the

sample when it comes to TSR generation. Second, as a validity check of the analyses

already conducted it will be tested whether top performing banks, measured on TSR, do

actually outperform low performing banks on the suggested key value drivers. Thirdly,

the question raised by (Koller, Goedhart & Wessels 2010) concerning prioritization of

profitability and growth in non-banks will in this analysis be answered with regards to

the banking sector. In relation to this, the question regarding whether some value drivers

should be preferred in crisis vs. non-crisis periods is also answered. Finally, the data

contains banks across North America and Europe and it will be tested whether there is a

difference in their performance.

11.1 Investigating TSR Performance of the VBM Banks

In this section the goal is to analyze whether the VBM banks outperform the banks that

are not officially committed to generating shareholder value. The VBM banks are

identified in Chapter 7.

In the analysis the sample is divided into two parts. One part includes the 20 VBM

banks in the sample while the other part consists of the banks that have made no official

statements about managing towards shareholder value creation. Tests on the difference in

mean values of TSR, ROA, revenue growth, ROE and IBIT-margin are conducted.

Exhibit 11.1.1: The Performance VBM Banks

Shareholder value

focused banks

Other banks p-value

TSR (CAGR) 5.9% * -1.4% 0.001

ROA 0.8% 0.7% 0.258

Revenue growth (CAGR) 8.1% 6.8% 0.280

ROE 13.1% * 9.3% 0.000

IBIT-margin 28.2% 27.6% 0.432

Source: Bloomberg, Own Contribution. * = Significant difference at a 1% significance level

In Exhibit 11.1.1 it is seen that the shareholder value banks are capable of securing

significantly higher return to their shareholders than non-shareholder banks. A result

that confirms the findings of (Rapp et al. 2011) that open commitment to value-based

management increases the share price. Another interesting finding is that the shareholder

value banks are actually able to have a significantly higher ROE than the other banks.

However, it is just as interesting that ROA does not differ significantly. The case banks

studied in section 7.1 revealed that ROE is preferred above ROA as the bank’s main


68

profitability ratio which might be the reason for this difference. However, it also supports

that banks can increase performance by focusing on the right value drivers.

As a final conclusion it should be mentioned that there might be a very large bias in

these results. It is noted that banks often highlight their share performance in good

times. During crises they then stop commenting on share performance and remove the

shareholder value objective from the mission statements (Rapp et al. 2011). This bias is

minimized by looking at annual reports for the entire time span.

In total it is, however, safe to say that the banks that have openly committed themselves

to shareholder value outperform the banks that haven’t. Therefore, RQ4 can be

answered. VBM banks manage what they intend to manage: shareholder value. In

connection to the overall analysis in Chapter 10 VBM banks might increase shareholder

value performance further by focusing on ROA instead of ROE.

11.2 Investigating the Top Performing Banks

In order to fully understand the dynamics of the value drivers of banking further analyses

are needed. Inspired by (Frigo et al. 2010) and (Stadler 2007) the sample banks are

divided into quartiles sorted after their TSR performance over the sample period. To

confirm the results of the regression the top banks (the top 25% in the sample) should

outperform the other banks in the sample measured on the key value drivers found in

Chapter 10 and other profitability measures that are highly correlated with ROA. The

ranked banks can be seen in Appendix 17.2.10.

Exhibit 11.2.1: Performance of Top, Medium and Low Performers

Top

Performance

Banks

Medium

Performance

Banks

Low

Performance

Banks

Total

TSR (CAGR) 11.3% ** 1.20% -15.1% -0.33%

ROA 0.99% ** 0.87% 0.30% 0.76%

Revenue growth (CAGR) 11.2% ** 6.41% 3.91% 6.97%

ROE 12.1% * 11.5% 4.41% 9.86%

IBIT-margin 34.4% * 30.5% 15.4% 27.7%

Source: Bloomberg. The top-performing banks are the 25% highest TSR generators in the sample over the

sample period. The medium performers are the banks with a TSR ranging from -6.9% to 7.6%. The low

performance banks are the banks with a TSR below -6.9% during the period.

*Significant difference between Top performance banks and Low Performance banks

** Significant difference between Top performance banks and Medium Performance banks

From Exhibit 11.2.1 it is confirmed that top performing banks have significantly

outperformed the low performing banks on all the tested value drivers. It is, however,

especially notable that the top performers outperform the other banks when it comes to

the key value drivers ROA and growth.


69

11.3 Prioritizing Profitability and Growth

(Koller, Goedhart & Wessels 2010) stresses the importance of prioritizing profitability

and growth when trying to increase shareholder value. E.g. in companies with high

profitability, managers should focus on increasing growth while low profitability

companies need to focus on increasing their profitability. This is also discussed in the

paper by (Jiang, Koller 2007) where non-financial firms should prioritize ROIC when it is

below WACC and prioritize growth when ROIC is above WACC.

In this analysis ROE and cost of capital are used instead of ROIC and WACC since it

yields the same interpretation for banks (Gross 2006). Otherwise, the analysis is inspired

by (Jiang, Koller 2007) and therefore the companies’ compounded average annual TSR

over the period 2001 – 2011 is calculated for four groups of companies. Those banks with

ROE higher or lower than their cost of equity combined with their respective revenue

growth compared to the sample average of 6%, in Exhibit 11.3.1 this analysis can be

seen. The arrows show which strategy to pursue.

Exhibit 11.3.1: Prioritizing ROE and Growth


From Exhibit 11.3.1 it is found that the worst performing banks are those with average

ROE during the period that is lower than the CoE and a revenue growth below the

market growth. It is furthermore clear that these banks should try to increase ROE

above the CoE before starting to focus on growth. This result is in accordance with the

findings of (Koller, Goedhart & Wessels 2010). For banks positioned in the upper left

corner, the right strategy should be to increase their revenue growth instead of increasing

the ROE further. However, it should not be by taking in activities where ROE is below

CoE. Finally, high growth banks with ROE below CoE should focus on increasing their

ROE> CoE

ROE< CoE

Below Above

6.8%

-0.9%

2.3%

-9.6%


70

ROE even at the cost of more growth. All in all it supports the findings by (Koller,

Goedhart & Wessels 2010).

Testing this through a more statistical procedure yields the results found in Exhibit

11.3.2.

Exhibit 11.3.2: Investigating Interaction Terms

Coefficient Std. error t-statistic P-value

G_REV*EP_NEG 0.574 0.085 6.785 0.000

G_REV*EP_POS 0.793 0.141 5.611 0.000

ROE*EP_NEG 1.101 0.256 4.303 0.000

ROE*EP_POS 1.064 0.174 6.107 0.000

LN_ASSETS -0.015 0.002 -7.577 0.000


The results are not as clear as the findings of (Jiang, Koller 2007) but this might be due

to the fact that prioritization of profitability and growth is not as simple when it comes

to banks because of the fact that banks need equity to grow. (Gross 2006)

Revenue growth over a 10 year period has a positive influence on total shareholder return

both when ROE is above and below the cost of equity. This is due to the positive

coefficient of G_REV*EP_NEG and G_REV*EP_POS.

Finally, improved profitability adds value both when ROE is above the cost of equity

and when it is below. The coefficients of ROE*EP_NEG and ROE*EP_POS are positive

which indicates that increasing ROE creates most value both when ROE is above and

below the cost of equity.

Looking at the size of the coefficients both ROE*EP_NEG and ROE*EP_POS are

higher than the corresponding alternatives. This somehow supports the findings by

(Jiang, Koller 2007) since it appears more valuable to increase ROE when it is below

CoE than when it is above and it also adds more value to grow the bank when economic

profit is positive compared to when it is negative.

11.4 The Relative Importance of Value Drivers Before and During the

Crisis

The cyclical nature of the economy makes it is important for bank managers to know

whether the value drivers differentiate in crisis periods compared to growth periods. In

order to investigate whether this is the case, the sample is divided into two time periods.

The pre-crisis years are the years from 2001 to 2006 and the crisis years are from 2007 to

2011 (French et al. 2011) and (Sorkin 2009)


71

Exhibit 11.4.1: Results from Pre-crisis and Crisis using FE and White Standard Errors

Pre-crisis Crisis

Coefficient Prob. Coefficient Prob.

ROA 15.069 0.004 3.656 0.007

Revenue growth 0.095 0.003 0.155 0.002

Contingency 0.210 0.000 -0.050 0.529

Ln assets -0.147 0.002 -0.039 0.281

PB ratio 0.038 0.264 0.411 0.000

2 year -3.751 0.000 -11.425 0.000

MSCI Finance 0.604 0.000 0.495 0.000

GDP growth -4.168 0.001 0.232 0.741

Source: Bloomberg, own contribution.

Some interesting insight can be gained from the analysis in Exhibit 11.4.1. First of all it

is seen that increases in ROA impacts TSR with almost 5 times as much in pre-crisis

years compared to the crisis years. The reason for this might be that increases in ROA

through a down-turn period are, by the market, seen as a one-time effect and not

expected to be persistent whereas increases in ROA in up-turn periods are interpreted as

a more valid sign of profitability (Dermine 2009). Further, the negative coefficient on the

2 year interest rate is in crisis years three times as large compared to the pre-crisis years.

This indicates that during crisis years the negative macro value drivers become more

important factors. Furthermore, the GDP growth seems to be fully explained by the 2

year rate in the crisis years.

Growth in revenue becomes more significant during the crisis. This is counterintuitive

when looking at the results in Section 11.3 where it was seen that when ROE is lower

than CoE revenue growth does not create any value. During the crisis period many banks

experienced levels of ROE that were lower than their corresponding CoE (Visali et al.

2011) and therefore growth was expected to be less value-adding during the crisis. Even

though this conflicts with the findings from Section 11.1 there might be an explanation

for this. Only the strongest banks are capable of securing growth through a down-turn

period, either through acquisitions of struggling banks or by stealing value-creating

customers. The relation between being well-capitalized and delivering positive TSR will

result in the findings seen in Exhibit 11.4.1. Finally, the analysis confirms that ROA and

revenue growth are key value drivers both when the economic climate is good and bad.

11.5 Investigating Differences between US and Europe

It confirmed in Section 10.2 that bank performance is affected by external factors. In this

study some of these external factors are discussed by investigating the shareholder value

generation across the different countries in the sample.


72

Exhibit 11.5.1: 10 Year CAGR TSR across the Included Countries


Exhibit 11.5.1 shows clear evidence of shareholder value destruction in the banks

headquartered in the European countries. Only Scandinavian banks seem to be

performing well and the Canadian banks perform extremely well. To investigate the

difference in value generation between the European and the American banks a test is

conducted on the differences in average TSR, ROA and growth.

Exhibit 11.5.2: Performance of North American and European Banks

EU North America t-statistic p-value

TSR (CAGR) -5.03% 2.07% -3.859 0.010

ROA 0.54% 0.86% -5.941 0.000

Growth 5.74% 7.59% -2.512 0.080


The analysis supports all previous analyses as the North American banks outperform the

European banks significantly on the key value drivers. The reason for the poor

performance in profitability and growth in the European banks is not only due to bank

managers having a wrong management focus but also the European debt crisis has its

impact. Banks from Greece, Spain and Portugal all contribute with negative TSR as can

be seen in Exhibit 11.5.2.

Some of the differences could, however, be explained by unobserved underlying factor

shared by the European banks. In order to investigate whether such factors appears a

European dummy variable is included in the analysis. The dummy variable equals 1 for

all the European banks and 0 for the US and Canadian banks.

12%

12%

10%

3%

3%

1%

-3%

-6%

-7%

-8%

-10%

-16%

-19%

-22%

-24%

-30% -25% -20% -15% -10% -5% 0% 5% 10% 15%

Norway

Canada

Greece

Switzerland

Italy

France

Spain

Englan

Germany

Portugal

Belgium

Austria

Denmark

Sweden

US


73

Since the Europe-dummy do not change over time it is necessary to rely on random

effects estimation in this analysis (Wooldridge 2002). Exhibit 11.5.3 shows the results of

the random effects estimation with the included dummy variable.

Exhibit 11.5.3: Investigating the Europe Factor

Coefficient p-value

ROA 5.818 0.000

G_REV 0.127 0.000

G_IBIT 0.034 0.000

CONTINGENCY 0.039 0.011

PB_RATIO 0.075 0.000

LN_ASSETS -0.018 0.000

_2_YEAR -3.738 0.000

MSCI_FINANCE 0.521 0.000

EUROPE 0.074 0.001


At first hand, it is surprising to see that the European dummy variable have a positive

and significant sign. However, having controlled for variables such as ROA, growth etc. it

indicates that there is some unknown Euro-factor (or a negative US factor) that has a

positive effect on TSR in European banks. One such variable could be the too-big-to-fail

suggested by (Brewer, Jagtiani 2009), which seems to be more present in the European

banks than in the North-American banks. If European banks are more likely to be saved

when they come into financial distress than US banks, this might explain the positive

sign. It is important to notice that the endogeneity detected in Chapter 9 makes the RE

estimator biased and therefore the coefficient cannot be fully trusted.

12 Constructions of the Value Driver Map

Having conducted the analyses regarding which drivers create shareholder value this

chapter will map those into a value driver tree in order to understand the connection

between the variables. The value driver tree constructed in this thesis is based on both

the findings from the conducted analyses and an economic knowledge gained through

Part II. The key value drivers identified in the FE 2SLS regression based on the

academia approach all play an important role in the map. A value driver tree breaks the

overall drivers down into underlying categories and is a method for visually and

systematically linking a business’ value drivers to the financial numbers that creates

shareholder value. The main source of inspiration for this is found in (Rappaport 1998)

and the steps described in Chapter 2. This way of mapping value drivers, also shares

some elements with the balanced scorecard constructed by (Kaplan, Norton 1996) since it

illustrates how performance of one driver affects other drivers. In Part IV these drivers


74

will be broken down further into operational KPIs which yields several advantages and is

also the premise of (Kaplan, Norton 1996). First of all, it let managers know the relative

impact on the bank’s value drivers and they can make tradeoffs between pursuing a

critical driver vs. letting a less critical driver deteriorate. Further, managers are capable

of prioritizing activities so that the most value-creating activities are focused on first.

The value driver is illustrated in Exhibit 12.1.1. (Koller, Goedhart & Wessels 2010)

Exhibit 12.1.1: The Value Driver Tree

Source: own contribution

According to (Rappaport 1998) the next important step is to differentiate between the

value drivers that managers actually can influence and those who are beyond the control

of the bank. First of all the bank cannot influence the interest rates, the growth in GDP

or the return on the MSCI Finance index. Further, even though the stock market’s

expectations are influenced by the performance of the bank it is still difficult to control.

This leaves ROA, revenue growth and loan loss coverage as the main controllable value

drivers of a bank. Inspired by (Dietrich, Wanzenried 2011) and (Athanasoglou, Brissimis

& Delis 2008) a further break-down of the profitability measure has been performed.

Especially the loan loss rate affected the profitability (for further information see

Appendix 17.2.11). The growth variable has been operationalized by looking at the

classical growth strategies from (Ansoff 1965), and risk cost is broken down using

(Sharpe 1964) and (Lintner 1965) as discussed in Chapter 4.

The tree can be seen as a great help for bank managers since it lets them, through a

structured approach, evaluate the performance of each business unit. Even though data

for the full value driver tree is not available for external analyses, bank managers can

TSR

Risk cost

ROA

Equity

Cost

Interest rate Deposits

Interest rate Loans

Financial leverage

Profit

Risk free int. rate

Revenue growth

Valuation mul-tiples

P/B

P/E

Beta

Market risk premium

Income

Tier 1

RWA

Interest Income

Non interest income

Provision for loan loss

Nonoperating expense

Other operating income

Trading income

Fee income

Total assets

RWA/ Total assets

Revenue per client

Loans

Deposits

Number of clients

Interest cost

Non operating income

1

Long term assets

Short term assets

2

5

4

3

ROA1

Revenue growth

Loan loss coverage

Market expectations

Risk free rate

Value driver tree Key value drivers

Regulatory changes

Other operating costs

Fee costMarket

penetration

Product development

Market development

2

3

4

5

GDP growth

MSCI Finance

6

GDP growth6

7

7 MSCI Finance


75

month by month or year by year, track the development of each variable making them

capable of finding the weak spots. In Exhibit 12.1.2 the first step in such an analysis for

Deutsche Bank can be found.

Exhibit 12.1.2: First Step in the Assessment of the Value Driver Tree

Source: Deutsche Bank annual reports, own contribution

The 2007 numbers at first hand looks difficult to interpret, however the analysis hide the

fact that Deutsche Bank experienced a significantly drop in their price book value

compared to 2006. Even though the numbers from 2007 looked fine, the stock market

expected it to get worse and TSR therefore experienced a 9% drop. 2008 does also yield

some interesting results. Even though the PB ratio dropped significantly and ROA also

showed a negative development, the large TSR drop of 67% was mainly driven by a 51%

decrease in net revenue growth! Breaking Net revenue growth down even further would

have revealed that almost 10 billion EUR was lost on Net gains on financial assets and

liabilities. In 2009 Deutsche Bank was able to turn these numbers around and therefore

experienced a positive TSR of 79%.

It is interesting to see that Deutsche Bank in 2011 performed well in ROA and growth

but still experienced a negative TSR. The reason for the poor performance seems to be

that the stock market has lowered its expectation for the future performance. The bank

needs to restore the markets confidents by outperforming expectations. Part IV will

identify the operational strategies necessary to increase performance and thereby

shareholder value.

-9%

-67%

79%

-12% -23%-100%

0%

100%

2007 2008 2009 2010 2011

TSR

10%

-51%

87%

7% 7%

-100%

0%

100%

2007 2008 2009 2010 2011

Revenue growth

1.21

0.520.84 0.74

0.50

0.00

1.00

2.00

2007 2008 2009 2010 2011

P/B ratio

0.36%

-0.18%

0.27%0.14% 0.20%

-0.50%

0.00%

0.50%

2007 2008 2009 2010 2011

ROA

12% 13% 12%

23%16%

0%

20%

40%

2007 2008 2009 2010 2011

Cost of equity

76

Part IV – Operationalizing the Value Drivers

The purpose of Part IV is to give the reader a deeper understanding of the value drivers

mapped in Exhibit 12.1.1 through a further break down into operational strategies. It will

discuss how to increase the drivers through increasing profit, decreasing costs and

keeping control of the risk. The structure can be seen in Exhibit 13.1.1.

Exhibit 13.1.1: The Structure of Part IV


13. Operationalizing Profitability, Growth and Risk

Control

Having derived a detailed value driver tree constructed by the input from both academia

and practitioners Part IV identifies operational strategies and seeks to show how to

increase shareholder value (Rappaport 1998). As before mentioned academia has so far

stopped with the derivation of the most important KPIs. In order to take this thesis

further Part IV will identify which operational actions bank managers can take in order

to maximize shareholder value. Since academia only has an external view to shareholder

value, the articles used are found in the management consulting literature as these

companies have the internal sight that is important for such a study. (Duthoit et al.

2011)

The key value drivers identified in Part III were:

• Profitability (ROA)

• Revenue Growth

• Loan loss coverage (Risk Control)

Identify operational strategies for improvement of profitability

Summarize the identified KPIs

13.1 ROA

13.2 Revenue growth

13.3 Risk control

13.4 VBM KPIs

KPI 1

Identify operational strategies for increasing revenue

Identify operational strategies for better risk control

KPI 2

KPI 3 KPI 4

KPI 1 KPI 2

KPI 3 KPI 4

KPI 1 KPI 2

KPI 3 KPI 4


77

• Market expectations

• Risk-free interest rate

• GDP growth

• MSCI Finance

(Rappaport 1998) argues that only value drivers that the company can manage should be

included in the VBM system. Part IV will therefore only operationalize profitability,

revenue growth and loan loss coverage (risk control). Key criteria for a successful

operationalization are that the employees have clear performance measures that focus on

the right value adding activities, that the measures are transparent and aggressive but

achievable (Duthoit et al. 2011). Therefore the operational strategies presented in Part

IV will all have clear KPIs that help the employees track their value generating

performance.

The results so far have shown that it is important to perform well in all the key value

drivers if the banks consistently want to create shareholder value. If a bank only grows

by taking in risky customers the risk control value driver will suffer and thereby hurt

performance.

Discussing a successful application of the value driver tree, bank managers need to

evaluate the current performance in each of the value drivers (as seen in Chapter 12).

When the performance in each value driver has been assessed and evaluated, based on

benchmarking with the top-performing banks and historical numbers, the identification of

“weak spots” can begin. If the bank performs well in profitability, growth and valuation

multiples but fails to perform well in the risk control value driver then focus should be on

implementing the operationalizing strategies that optimize risk control. (Rappaport 1998)

13.1 Value Driver 1 – ROA

“With no exceptions, all banks need to lower their costs but still keeping in mind that it

should not be at the cost of customer relationship. It is not strategically wise just to cut

costs; it has to be done in an intelligent manner” (Duthoit et al. 2011)

The most significant variable in the analysis in Part III (apart from some control

variables) was throughout all the models ROA. It is a strong performance driver and

therefore also a measure for the banks operational excellence. There are many underlying

measures that affect profitability (see (Dietrich, Wanzenried 2011) and (Athanasoglou,

Brissimis & Delis 2008). Exhibit 13.1.2 illustrates the underlying measures identified in

Part III and Appendix 17.2.11 and the KPIs found in the following section.


78

Exhibit 13.1.2: KPIs for Profitability Improvement


The following part will go into detail with how managers can affect the profitability of

their bank. The key to successfully maximizing profitability is reached through improved

sales and service effectiveness, followed by an automation of the processes in order to

improve cost effectiveness. Finally, these suggestions should be incorporated in a

streamlined organization that focuses on reduced business complexity (Duthoit et al.

2011) and (Visali et al. 2011). Bank managers, however, need to keep in mind that there

is not a common framework that works for all banks, each bank needs to consider all the

operational advices and choose the strategies based on the bank’s starting position.

(Rappaport 1998)

13.1.1 Action 1: Sales and Service Effectiveness

In the analysis in Part III, three different operational effectiveness measures were

included. Even though neither of them showed any sign of significance impact on TSR

(as it is seen from the value driver tree their impact is probably described by other

factors) the development in them, shows that the banks have had a focus on increasing

their efficiency, see Exhibit 13.1.3.

KPIs

ROA

Cost

Interest rate Deposits

Interest rate Loans

Financial leverage

Profit

Income

Interest Income

Non interest income

Provision for loan loss

Nonoperating expense

Other operating income

Trading income

Fee income

Loans

Deposits

Interest cost

Non operating income

Other operating costs

Fee cost

Value driver tree

Branch wait time1

Call wait time2

Call resolution time3

Online Banking share4

Customers pr. branch5

Customer % time 6

Complexity score7


79

Exhibit 13.1.3: Operational Effectiveness Measures

Source: Own analysis, own contribution

In order to improve the ratios in Exhibit 13.1.3, (Duthoit et al. 2011) suggests actions

that give sales employees incentives to accentuate products that help tie the customers

more closely to the bank. Since, it is more costly to get a new customer than to retain an

old one (Heskett 1994), keeping customers and increase their engagements automatically

lowers unit cost and increase the effectiveness of the employees. Next, letting customers

interact with the bank through multiple channels (mobile, internet, call centres etc.)

depending on their inquiry, customer questions can be generated to those most suitable

to answer. If clients have questions that call centre can handle (opening hours, questions

regarding different standard products etc. e.g. by the use of interactive voice response

systems) the expensive specialists can then focus on the more complex questions.

(Duthoit et al. 2011)

A requirement for this is the implementation of a customer relationship management

(CRM) system that can handle these “easy-to-answer” questions. Besides, the CRM

system should also be able to provide the necessary information to these call centres

because efficient contact across all channels is then created. Since customers are divided

into customer groups based on their profitability it is important that low value customers

only receive service that is equal to what competitors are offering. In order to succeed on

those aspects the bank need the CRM system to be able to monitor and differentiate the

customers into different service levels. (Duthoit et al. 2011)

Besides affecting sales effectiveness, the fact that customers are served more quickly also

increase service effectiveness. In general, the top performing banks in (Duthoit et al.

2011) outperform the low performing banks on three KPIs: Branch wait time, call wait

time and call resolution time, measures that will help the banks track and increase their

sales and service effectiveness. (Duthoit et al. 2011)

2.17 2.332.60

2.96 3.113.42

3.70 3.77 3.71 3.513.81

3.653.94

4.484.92 5.10

5.40 5.595.97

6.27 6.06 5.98

0.160.18

0.20 0.21 0.22 0.230.24

0.22

0.260.24

0.25

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0

1

2

3

4

5

6

7

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Million USD

Million USD

Loans pr employee Assets pr employee Revenue pr employee (right axis)


80

13.1.2 Action 2: Process Automation

Another way to improve the productivity is by developing efficient and effective

processes through process automation (Duthoit et al. 2011). Process automation builds

upon avoiding a large number of manual processes in e.g. sales, service and fulfilment.

This could be by automating new-accounts openings, making standard loan procedures or

other processes that enable efficient data gathering and automated decision making

(Duthoit et al. 2011). The internet has shown its worth in this area since many

customers have accepted to use it for their banking businesses. It is therefore suggested

that banks increase their online activities and cut their costs by closing some of the

costly branches, an opportunity that has only grown bigger by the introduction of mobile

banking (Visali et al. 2011). The Swedish VBM bank Nordea is an example of a bank

that has the closing of branches as an important part of their strategy to improve

shareholder value (Nordea, Annual Report 2011).

KPIs for the process automation are the percentage of clients using online banking and

the number of customers per branch (Visali et al. 2011). The online banking KPI is a

strong measure for automation since it increases the number of tasks that clients can

handle. This will free up capacity and would make it possible for each branch to serve a

larger number of clients with the same number of employees.

13.1.3 Action 3: Organizational Streamlining and Reduced Business

Complexity

Streamlining of the organization is the third strategic lever that banks could use in order

to increase productivity. This means investing in a more lean overhead, reducing

complexity and maximizing the time employees spent with the customers. It is necessary

both to centralize the processes so that they are not carried out in each branch, thereby

experiencing scale benefits and outsource those activities applicable of doing so. (Duthoit

et al. 2011)

Finally, the goal of establishing underlying capabilities and thereby create winning

conditions is achieved through two capabilities. The first is to reduce business complexity

in order to reduce the time to market and facilitate the sales force training. By doing

this, the bank will succeed in simplifying the offerings that are easily integrated across

business units and distribution channels. Further it helps streamlining the product

portfolio and keeps it up to date with the most value-adding activities. Appendix 17.3.1

explains how LEAN initiatives can successfully streamline banks. Second, the best

performing banks outperform through an implementation of a high-productivity culture

where there are clear expectations, where performance monitoring is transparent and

continuous and where incentive schemes are constructed so that they are highly

motivating and easy to understand. This is complemented by training and coaching the

employees on how value is created. (Duthoit et al. 2011)


81

(Duthoit et al. 2011) recommends measuring the percentage of employees facing

customers as a key performance indicator of how streamlined the bank is. Furthermore, a

“degree of complexity” score should be introduced to track the improvements on this

area (Duthoit et al. 2011). Increased face time with customers means fewer resources

spent on non-value adding activities.

13.2 Value Driver 2 – Revenue Growth

With expected market growth equal to zero and a changing regulatory environment, it

will not be possible to grow the bank based on either increased GDP, through increased

leverage or by increasing risk. Instead growth should be captured from strong

outperformance through tighter customer relationships and a better tailoring of the value

proposition to each customer (Visali et al. 2011). This means that instead of looking at

number of customers, banks need to focus on increasing revenue per customer and

thereby secure revenue growth. A successful implementation of the revenue growth

strategies is also closely correlated to performance of the CRM system. Success requires

that the bank knows its customers’ needs through detailed and easy accessible

information (Visali et al. 2011). Exhibit 13.2.1 summarizes the growth drivers and the

KPIs to be identified through Section 13.2.

Exhibit 13.2.1: KPIs for Achieving Revenue Growth


(Maguire et al. 2009) discusses some of the steps that managers can take in order to

drive the bank closer to perfection and positively affect shareholder value through

revenue growth. It is build upon a study of some of the largest banks in the world were

the best performing banks are compared to the worst. The general conclusion is in line

with (Visali et al. 2011) saying that focus is suggested to be on increasing customer

relations through better segmentation, streamlining the product portfolio, increase

customer satisfaction and a have a management capable of pushing the strategy all

through the bank to the front line employees. In short version, the following part will

help managers segment and secure the right customers, find them the right products

Value driver tree KPIs

Segment mix1

Share of wallet2

New customer share3

Revenue growth

Revenue per client

Number of clients

Market penetration

Market development

GDP growth

Product development

Profit per customer4

Customer retention5

Customer satisfaction6


82

through a streamlined product portfolio, secure them and finally create shareholder value

and revenue growth from them through long-term relationships.

13.2.1 Action 1: Segmentation of Customers

Looking at the identity of a bank a common phrase goes, perception is reality. Not being

able to clearly tell the customers what the banks identity is, is just as bad as failing to

align the organization and making sure that all business units are behind this identity. In

order to secure this identity perception among customers, consistent marketing

campaigns that help customers associate the banks name and logo with the desired

identity need to be carried out. However, before marketing the banks identity, a careful

segmentation will help securing that the preferred customer groups are easier found and

tied to the bank’s identity and to the bank’s core offerings. In general, customers are

either segmented by geography, by their possible product group or through a

combination (Kotler, Keller 2008). Such a process should enable both increased

effectiveness within product development but also through more targeted marketing

campaigns (Kotler, Keller 2008). As was stated in the beginning, revenue growth is not

just about increasing sales per customer but also about securing long-term sales. This is

related to both the customer satisfaction but just as much to the streamlining and

innovation of the product portfolio. Creating the right products for the right customers is

one way to secure long term sales effectiveness (Maguire et al. 2009). Rinkjøbing

Landbobank, the number one TSR performer in the data set, is a true segmentation

expert. One of the segments targeted by the bank is medical practices, a segment known

to have low-risk and high profitability (Theil 2012).

A KPI for the segmentation is the segment mix measure. The goal is to increase the

share of revenue in the most attractive segments and thereby secure profitable growth

like Rinkjøbing Landbobank has.

13.2.2 Action 2: Streamline Product Portfolio

The second step to take in order to secure increased revenue, is streamlining of the

product portfolio. Banks that perform the best often have a relatively narrow product

portfolio measured by category (Maguire et al. 2009). This focus helps all employees

gaining deep knowledge regarding the products they sell which ultimately affects the

conversion rate and cross-selling opportunities and thereby the revenue growth (Duthoit

et al. 2011).

One way to achieve a streamlined product portfolio is to make sure that the bank knows

its customers’ needs. In step one, the right customers were found, step two is then to

collect the right information. It is done through detailed information systems that help

the bank only having relevant products in their portfolio, meaning that they offer the

right products to the right customers. However, having a narrow and focused product

portfolio does not mean that no changes are made. What the best practice banks are

good at is not just keeping the portfolio narrow but they also monitor and adjust it to


83

their customers’ needs. An example of innovative marketing initiatives among some

banks is the new focus on women. Working and wealthy single and married women have

increased their interest in their own financial well being, making it an attractive segment

for most banks (Silverstein, Sayre 2009). However, this second step is closely connected

with the segmentations made in step one because a development of the most dynamic

products depends upon the accuracy of their segmentation and customer insight (Kotler,

Keller 2008). (Maguire et al. 2009) suggests that one way to create this very narrow and

focused strategy is to set up a team that is specialized in developing products for the

most profitable segments. A strong customer insight should then be gained through both

quantitative and qualitative analysis, pilot testing and thereafter fine tuning of the

products before developing a business case for the new product and communicate it

clearly both internally and externally.

A KPI proposed by (Kaplan, Norton 1996) is the “Share of wallet” measure. Each

customer should have his personal “share of wallet” measure and the job is for the

employees to maximize this measure. (Kaplan, Norton 1996)

13.2.3 Action 3: Attract and Secure the Profitable Customers

In the third step focus is on the sales people, both in their approach towards new

customers and their effectiveness. First of all, analyses have shown that the best

performing banks have sales people that day by day are working towards increased face

time with value creating customers. Further, creating long term relationships by making

a strong first impression will help the bank secure the customer. Therefore, the six

elements illustrated in Exhibit 13.2.2 are what the banks should focus on in their first

meeting with the customer. (Visali et al. 2011)

Exhibit 13.2.2: The Six Critical Success Factors in the First Client Meeting

1 Effective use of greeters in the sales

process 4 Compelling simple documentation provided

at sale including a clear professional

account opening guidebook and a lucid

explanation of which documents are

required to fulfil the bank’s needs

2 Efficient capturing of information,

including and “ask once” policy that

prevents customers from having to give

basic information repeatedly

5 Detailed explanation of the channel setup,

including potential hands on

demonstrations of web site navigation

3 Physical branch formats that are geared to

sales, with private areas available to help

with account opening, a time when

customer needs must be systematically

assessed; hard cross selling efforts should

be scrupulously avoided

6 Clear description of all fees and charges,

the concept of full transparency must be

driven home at first contact

Source: (Maguire et al. 2009), own contribution


84

In general the first month of a client relationship should be flawless, the bank should

commit itself to avoid errors and make the client feel valued in order to secure the

customer on a long term basis. However, the banks are still in it for the business and as

described in the start, increasing revenue per customer is a key objective. Therefore, after

the first period focus should shift to cross-selling activities that are tailored to each

customer. (Maguire et al. 2009)

Further, banks should incorporate a comprehensive approach that monitors and

differentiates the service level to different clients thereby helping employees to keep focus

on the value creating customers.

In order to measure the performance in this action the ratio of revenues from new

customers to total revenue should be traced. Furthermore the profit per customer could

be a useful measure. (Kaplan, Norton 1996)

13.2.4 Action 4: Focus on Retaining Customers

Having secured all value creating customers, the last step regards increasing shareholder

value through customer satisfactory and customer relation, meaning keeping the

customer a happy and profitable customer. Where customer satisfactory might be

increased through physical impressions (Maguire et al. 2009), a customer relationship is

achieved by exploiting customer data. Not just to make centralized campaigns that drive

sales of a specific product but instead use the data to reposition the service model so that

it fits each customer segment. E.g. the collection of data on the first day is valuable

information in such a CRM system. By having such a focus, bank managers can better

align the marketing campaigns, product development and the frontline staff around an

offer that suits each customer the best. An example of a successful customer relationship

focus that is able to boost the revenue is seen within online banking. The benefits of

optimizing such a multichannel strategy have been significant for those who have

adapted to it. (Badi et al. 2012)

Finally, the operational actions that effects revenue growth can only be carried out if the

organization is geared towards it. The most successful banks are often characterized by

having a strong leadership, a performance schedule that are build upon value creating

activities in all layers of the company, a wide span of control, less than seven

organizational layers and a clear understanding on how the different teams need to

interact in order to create synergies. (Visali et al. 2011)

In order to measure the progress in retaining the customers, three KPIs are suggested.

(Kaplan, Norton 1996) recommends measuring the customer satisfaction in annual

surveys. An increase in this will imply that the bank has been able to increase the

customer retention. (Kaplan, Norton 1996) also recommends tracing the customer

retention directly. Finally, (Duthoit et al. 2011) recommends tracing the percentage of

time each employee spends facing customers.


85

13.3 Value Driver 3 – Risk Control

In Chapter 3 it was discussed how the bank’s business model is centered on managing

risk and the crisis have highlighted the importance of both credit policies but also risk

management skills. What is earned during an up-turn can easily be lost during a down-

turn if the bank is not capable of managing their risk. The stock market knows this and

therefore rewards the banks with strong risk management skills by decreasing the cost of

capital and thereby increasing TSR which was confirmed in Part III. It is all about

striking a balance between growth and risk and the following part will be a guideline on

how to balance operational risk management. (Visali et al. 2011)

Upcoming regulatory changes make it necessary for banks to cope with risk on an

operational level. The regulatory changes consist of risk measures that are designed to

increase the transparency and stability in the banking sector (Basel Committee on

Banking Supervision 2010). This means that most banks have already tried to upgrade

their risk management skills in order to handle the increasing number of defaults and a

far more stressful and demanding environment, but improvements can still be made.

(Leichtfuss et al. 2010)

Risk management skills are all about the culture of the bank. The banks capable of

creating this culture will also manage risk more efficiently and precisely. It is therefore

important that all employees understand risk in connection to return (Dayal et al. 2011).

Exhibit 13.3.1 shows the components of Risk Control and the KPIs necessary for a

successful implementation of risk reducing strategies.

Exhibit 13.3.1: KPIs for Enhanced Risk Control

Source: Own analysis, Own contribution

As it is seen from the figure some fundamental operational activities are able to have a

great impact on the TSR. Even though many banks already do something, few do

enough. By taking a more comprehensive view of risk those banks that move first are

Value driver tree KPIs

Risk understanding

coverage ratio

1

Percentage of

revenue from risk

segments

2

Liquidity adjusted

profit

3

Risk cost

Equity

Risk free int. rate

Beta

Market risk premium Tier 1

RWA

Total assets

RWA/ Total assets

Long term assets

Short term assets

Regulatory changes

IT coverage ratio4


86

presented with a window of opportunities that give them competitive advantages (Dayal

et al. 2011).

13.3.1 Action 1: Understanding the Implications of Risk

As was discussed in Chapter 3 the new regulatory environment will lead to changes in

both the banks’ balance sheets, profitability, funding strategies etc. For bank managers

to be able to develop a strategy that can cope with the changes, (Dayal et al. 2011)

suggests that the following operational actions need to be taken. First it is important for

the managers to get a clear understanding of how and where the changes will impact the

business. It is necessary both to map the pending and the proposed rules in order to

make a coherent strategy. This both help the bank minimize the risk of failing some of

the regulatory variables, which will have a negative effect on the value, and it might

make the bank aware of less regulated opportunities. After that, the bank should

calculate the risk/return on each product with the new regulatory changes implemented

in order to accurately reflect the costs imposed by the reforms. Going forward from this,

it should be clear which products to avoid and which to go for in order to get an

integrated understanding of the economic implications of risk (Dayal et al. 2011). It is

important not just to focus on key ratios such as tier 1 but also to know how and where

risk is created. It is then possible to track both the deployment of capital and the

regulatory and economic implications of this. Such an integrated view of risk will help

banks optimize their funding and capacity resources, an area were many banks are still

able to increase shareholder value. In line with this, increased focus on optimal capital

deployment will also make the bank able to price risk more correctly. The information

needs to reach the sales people in the front line so they are able to perform price

differentiation, making all customers value-creating. It is however important to state that

even though price differentiation among segments is beneficial a high degree of

discrimination within the same segment might be problematic (Kotler, Keller 2008).

Further, the crisis has shown that models are only as good as the input they are provided

with meaning that the accuracy of the input data is of high importance. (Maguire et al.

2009)

In creation of an integrated view on risk, the employees need to fully understand the risk

of their own portfolio (Dayal et al. 2011). To gain further understanding training is

needed and the “risk understanding coverage” KPI is a suitable measure. Further, KPIs

that favour high quality loans with an acceptable risk profile will help decrease future

loan losses and affect shareholder value positively. The segmentation suggested earlier in

this chapter will make it possible to track the exact exposure to risky segments and the

risk segment percentage could be a suitable KPI for this.

13.3.2 Action 2: Focus on Liquidity and Risk Reduction

Chapter 3 highlighted the importance of regulatory changes on liquidity and risk. In

general, banks need to begin developing a fundamental framework on how to assess the

regulatory changes that affect the riskiness of their assets. Building up capital ratios is


87

only a start. Despite their efforts to cope with the changes and keep up to date with

regulations, only few have fundamentally transformed how they look at risk as a result of

the changes. In order to do this, banks need to develop a comprehensive plan. In (Dayal

et al. 2011) ten steps that bank managers should take in order to cope with the changes

and make sure that cost of equity is kept at a reasonable level are explained. In Exhibit

13.3.2 these ten ideas are summarized.

Exhibit 13.3.2: 10 steps to cope with Regulatory Changes

1 Establish integrated bank wide steering

mechanisms that account for balance

sheet, P&L, capital, liquidity and leverage

effects.

6 Aim to comply with - and potentially

exceed - the new Basel III ratios by 2013

to keep pace with top-tier banks.

2 Highlight and reassess risk-return

considerations at the group, segment and

product levels.

7 Plan for continued deleveraging and, in

Europe, greater disintermediation as more

companies shift toward capital markets.

3 Develop a plan for adjusting prices that

takes into account competitors' reactions

(game theory considerations)

8 Develop sustainable funding and

refinancing strategies that take into

account the liquidity challenges lying

ahead.

4 Map the regulatory landscape to cut

through the complexity, facilitate

compliance, and identify potential

arbitrage opportunities.

9 Identify unexploited risk weighted assets

(RWA) reductions by upgrading the

bank's risk models and improving the

quality of data management.

5 Understand that Basel III is the global

blueprint and that local requirements (for

example, Dodd-Frank and the Vickers

report) will add challenges on top.

10 Foster the development of a bank-wide risk

culture on the basis of a regulatory and

economic view of the new requirements.

Source: (Dayal et al. 2011), own contribution

As it is seen, strong risk management is centered on a necessity to get a clear and

accurate perspective of risk throughout the whole organization and thereby get an

understanding of where value is actually created. Those banks capable of doing that will

use financial resources more efficiently and thereby be able to pursue those opportunities

that might arise in the wake of the crisis. (Dayal et al. 2011)

In order to measure the employees on the basis of their liquidity and risk reduction

performance new measures needs to be introduced. The employees need to assess the

liquidity drain from each customer in their portfolio and make sure that the profit from

each customer exceeds this liquidity drain. (Dayal et al. 2011)


88

13.3.3 Action 3: Integrate Effective IT Systems

As IT systems are central in managing risk, the regulatory changes will put pressure on

the banks IT capabilities. The systems should be capable of both measuring and

managing risk but it should also help carrying out day to day activities (Bohmayr et al.

2011). It is expected that, as the new regulatory requirements are being implemented,

those banks capable of managing these three areas effectively will gain competitive

advantages. Looking at Exhibit 13.3.1, this means that the good banks will effectively

manage and calculate RWA due to better risk assessment with the positive side effect of

increasing profits through lower loan loss provisions.

In order to gain competitive advantages, bank managers must first of all know where

their IT systems will be put under pressure. On the technological front the bank will

most certainly be pressured within three areas. The first is data gathering that due to a

greater demand of data availability need to ensure consistency in data collection and

interpretation. Further banks will need to strengthen their data calculation and model

setup since the new regulatory changes will require both a larger set of metrics but also

more frequent updates. Finally, the reporting capabilities need to be enhanced in order to

help the employees extract more detailed information on each customer, thereby helping

them make better risk estimates of each customer (Bohmayr et al. 2011). Finally, most

banks will have to upgrade their IT systems in order to cope with the changes. Banks

with systems capable of measuring and monitoring their customers better are able to

build their own credit models instead of applying the standard models. This secures more

efficient capital deployment and risk allocation (Dayal et al. 2011).

Important measures of the IT performance include the IT coverage ratio. The IT

coverage ratio is difficult for the lower level employees to influence and it is more

regarded as the key measure for the IT department.

In Appendix 17.3.2 a description of how the Danish bank Jyske Bank has taken steps

towards both improving the IT infrastructure and decrease costs can be seen.


89

13.4 Summarizing Part IV

The operational strategies needed to maximize shareholder value in banks have been

identified through a careful study of the prevailing strategies proposed by the consulting

companies. Benchmarking against top-performers in each value driver category will show

where the bank should focus in order to maximize shareholder value (Rappaport 1998).

Exhibit 13.4.1 summarizes the actions identified to operationalize the strategies and the

KPIs crucial for the implementation.

Exhibit 13.4.1: Operational Strategies to Improve Shareholder Value and Their KPIs

Source: Own analysis, own contribution

Symptom Actions Key performance indicators

Low

profitability

1. Improve sales and

service effectiveness

* Branch wait time

* Call wait time

* Call resolution time

2. Process automation

and industrialization

* Percentage of customers using online bank

* Customers pr. branch

3. Reduce complexity * Percentage of employees facing customers

* Complexity score (survey)

Low growth 1. Effective segmentation * Segment mix

2. Streamline product

portfolio

* Share of wallet = !,$�(M"� "NO&O"M"N��(�&+ P N&N! &+ N""Q$

3. Attract profitable

customers

* New share = S"*N," T�(M N"U !,$�(M"�$V(�&+ �"*"N,"

* Profit per customer

4. Focus on customer

satisfaction

* Customer retention

* Customer satisfaction (survey)

Poor Risk

control

1. Create risk

management culture

* Risk understanding coverage ratio

* Percentage of revenue from “risk segments”

2. Focus on liquidity and

risk reduction

* Liquidity adj. proPit = ProPit – liquidity cost

3. Integrate effective IT

systems

* IT coverage ratio

90

Part V – Assessment and Conclusion

14. Critical Review of Results

An empirical study always has its boundaries and areas where it could be expanded. In

this thesis these boundaries are first of all connected to the omitted variable problems

discussed in Part III. Variables that might be valuable to include are e.g. the much

discussed geographical dummy that is able to track revenue and profitability secured by

the bank in different regions. Further, also the more operational KPIs would be

interesting to test in order to see whether some of them are capable of explaining the

variance in TSR. However, a common factor for most of the omitted variables is lack of

data availability. Such tests can therefore only be conducted by analysts in companies

such as management consulting houses that have internal knowledge about a large pool

of banks. Also risk variables that are able to cope with some of the difficulties discussed

in Chapter 3 regarding the complexity of risk cost calculations might yield a better

knowledge regarding the risk variables. Finally, from an economic view such

implementation of new regulatory changes should have a significant impact on TSR, even

though (Schäfer, Schnabel & Weder di Mauro 2012) finds no evidence of it. This might

be due to the problems discussed in Chapter 3, because the stock market incorporates

new regulatory requirements as soon as they expect them to be implemented and not

when they are actually implemented. A dummy capable of capturing these expectations

would probably yield insight into the understanding of the regulatory impact.

Further, the methodology applied for identifying VBM banks in this study, inspired by

(Rapp et al. 2011), might also be improved. It was discussed by (Boulos, Haspeslagh &

Noda 2001) that it takes more than words to implement VBM successfully. An even more

valid method would therefore be to conduct internal data in order to get more accurate

perception of the VBM usage. Analyses based on such a study would be more

comprehensive and RQ3 would be answered through a more thorough discussion. Also

further robustness tests might add value. In the thesis FE 2SLS was applied as a way to

correct for endogeneity problems. Another method for doing this suggested by (Fiordelisi,

Molyneux 2010) and (Verbeek 2009) is GMM which also let the author consider the

dynamic panel data models.

Finally, an increased time-span would add value to the thesis. Even though the study

uses an 11 year times-span, which is among the longest time-spans in shareholder value

literature, it would still add value and robustness to the study if the time-span was

increased. However, data availability sets a natural boundary for this possibility


91

14.1 Putting the Results into Perspective

For this thesis to be applicable it is important that bank managers start by stating its

current situation both from an external and internal view. From an external perspective,

the current environment with banks being more unpopular than ever, might not favour

implementation of a VBM system. Instead, softer issues such as the stakeholder value

perspective might be easier to implement. However, with stricter requirements, banks

need to adapt to the new environment, making VBM a suitable tool. Following

(Rappaport 1998), when the bank consistently delivers shareholder value it is possible to

focus on the local community and other aspects in the centre of the stakeholder

perspective.

From an internal view, the banks should value each business activity through the

constructed value driver tree, as discussed in Chapter 12. The limitation of this study

regarding internal data availability is not seen inside the bank and by applying monthly

data on the constructed value driver tree, bank managers is able to identify where value

is created and destroyed. This enables the bank to select the suggested KPIs from

Chapter 13 that affect the weak spots in the value driver tree (Rappaport 1998).

15. Conclusion

This thesis investigated the internal and external value drivers most compatible with

shareholder value maximization in banks. Even though there are a growing number of

articles surrounding the concept of shareholder value maximization, the evidence

surrounding this in connection to banks is limited. In order to assess the preferred value

drivers, five guiding research questions were posed. Answering each of these questions

was necessary in determining an answer to the key research question.

Before determining the value drivers that maximize shareholder value in banks, an

understanding of the complex banking business model is a necessary first step. Besides

revealing the differences between banks and non-banks income statements and balance

sheets, Chapter 3 also covered the great interdependence between bank performance and

the overall economy and the regulators impact on the banking environment.

In Part II, RQ1 and RQ2 were answered. To provide an answer to RQ1 the valuation

models applied by external equity analysts and the use of these models was discussed.

Following the external equity analysts’ view towards value creating drivers, an academic

literature review was conducted. Both groups were seen as having an outside-in view

towards value drivers and therefore expected to focus on overall accounting variables.

Further, management consulting reports and annual reports from 20 value based

management (VBM) banks were expected to provide the thesis with more internal

focused variables. Even though many of the 47 possible value creating drivers appeared


92

across the different types of literature, differences were seen. Especially the operational

drivers were only covered by the two latter groups.

In order to determine the significant value creating drivers and answer RQ3, an extensive

dataset was gathered and analyzed. Data on the 47 variables was collected for 132 listed,

North American and European banks from 2001 to 2011. Such a study is unique among

academic literature, both in terms of number of variables, the time period that were able

to include both pre crisis and crisis and in terms of information that many of the

variables provide.

In the specification of the preferred model, economic theory was applied in the

construction of eight overall groups that each of the 47 variables were divided into.

Several estimation techniques were considered for the preferred model. By carefully

analyzing the conditions it was found that the fixed effects two-stage least squares (FE

2SLS) estimator was the most efficient choice. The FE 2SLS model yielded the following

results:

TSRt = -0.02 + 8.44·ROAt + 0.2·gRevt + 0.002·LLCt + 0.124·PB ratiot -

6.952·2year + 0.563·MSCI_Financet + 3.029·gGDP - 0.289·TSRt-1 + νit

The findings indicate that return on assets (ROA), revenue growth and loan loss

coverage (LLC) are the main drivers of value creation, when total shareholder return

(TSR) is applied as the dependent variable. The results are in line with similar findings

conducted on non-banks, where return on invested capital (ROIC) and revenue growth

are also the main drivers (Koller, Goedhart & Wessels 2010).

A wide range of robustness checks were conducted. First of all two alternative model

specification procedures were applied. The univariate and factor approaches confirmed

the main findings of the preferred model since ROA and revenue growth also in these

tests ended up being the main value creating drivers. Inspired by (Koller, Goedhart &

Wessels 2010), an analysis correcting for expectations also revealed that when applying a

longer time span ROA and revenue growth are still significant value drivers.

To answer RQ4 and gain insight regarding VBM banks and their performance, it was

tested whether these banks had outperformed the other banks in the sample period. It

was found that VBM banks had generated significantly higher TSR, indicating that

implementing a VBM system will lead to increased shareholder value.

In order to fully grasp the dynamics of shareholder value creation, further analyses were

conducted. Inspired by (Jiang, Koller 2007) the prioritization and timing of pursuing

improved profitability or growth was analyzed. It is difficult to both grow the bank and

increase profitability along the way, therefore finding the right strategy between growth

and return, yields valuable insight. The findings suggested that the optimal strategy

depends on whether or not the banks return on equity (ROE) is above the cost of equity

(CoE). When ROE is below CoE it is suggested that banks focus on increasing


93

profitability whereas the opposite is true when ROE is above CoE. The results also

indicate that different economic periods might have different value drivers. To test this,

the sample was split into a pre-crisis and a crisis period. The results from these analyses

indicate that bank-specific value drivers are more value adding in economic up-turns

while the macro-specific drivers tend to dominate during a crisis. However, ROA and

revenue growth were significant value drivers throughout both periods, a result that adds

further robustness to the main findings. Finally, Part III ended with the construction of a

comprehensive value driver tree inspired by (Rappaport 1998).

Providing an answer to RQ5 it is required to follow the last step of (Rappaport 1998) by

operationalizing the identified value drivers. (Rappaport 1998) recommends the

identification of KPIs, enabling both managers and employee to gain stronger insight into

the value creating activities. Since this operating approach is unique among academic

literature, the operational strategies and KPIs were identified by studying management

consulting literature. From the literature it was found that the changing environment

that banks face, as discussed in the introduction, requires them to shift strategy from

“increasing number of customers” through fierce price competition to “increasing revenue

per customer” through stronger customer relationships and cross selling activities.

Having answered each of the research questions it is clear how banks should maximize

shareholder value. They should focus on the three main value drivers, implement a VBM

system and execute operational strategies that affect the key value drivers. Finally, by

choosing clearly defined KPIs for both the managers and employees that are in line with

the overall strategy, the banks are ready for the challenging future.


94

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17. Appendix

Shareholder Value in Banks

Documents

Transcript of Shareholder Value in Banks