MAURITS CORNELIS ESCHER (1898 – 1972) nizozemski grafični umetnik
Bachelor Thesis Maurits Kruithof
-
Upload
mauritskruithof -
Category
Documents
-
view
226 -
download
0
Transcript of Bachelor Thesis Maurits Kruithof
-
7/30/2019 Bachelor Thesis Maurits Kruithof
1/32
Prediction of Bankruptcy and Financial Risks:
Using the Z-Score Model for
AEX- and AMX- Investment Decisions
Bachelor Thesis Economics and Business, Finance Track
Finance group
Faculty of Economics and Business
University of Amsterdam
Date: July 2010
Name student: Maur its Kruithof
UvA student number: 5603404
Supervisor: Dr. Zacharias Sautner
-
7/30/2019 Bachelor Thesis Maurits Kruithof
2/32
2
Contents
1. Introduction ..32. Literature Study.. ..4
2.1. First Studies Related to Prediction of Corporate Bankruptcy... ..4
2.2. Discriminant Analysis and Financial Ratios Theory...42.2.1. Beavers Univariate Discriminant Analysis (1966)....42.2.2. Altmans Multivariate Discriminant Analysis (1968)... ..52.2.3. Developments After Beavers and Altmans Work....62.2.4. Ohlsons Probabilistic Approach (1980)... ..72.2.5. Financial Ratios versus Structural Approach Theory.... ..8
2.3. The Z-Score Model ..82.3.1. Component Ratios of the Model..8
2.4. Practical Use of the Z-Score Model.. ..92.4.1. Z-Score Model Accuracy and Type I and II Errors... ..92.4.2. Linking Z-Scores with Bond Ratings 10
3. Model & Data.113.1. Z-Score Model and Research Sample 113.2. Dataset AEX- and AMX-listed Firms123.3. Z-Score Trends of AEX- and AMX-indices..12
4. Analysis..154.1. AEX- and AMX-sectors Analysis... 154.2. Investment Decisions.. 17
4.2.1. High Z-Scores174.2.2. Low Z-Scores18
4.2.3. Z-Scores and S&P Bond Ratings...195. Conclusion..206. References.. 21
Appendices
Appendix 1 AEX-index Dataset. 23Appendix 2 AMX-index Dataset... 27Appendix 3 Sector-overview AEX- and AMX-index (Z-Scores) 31
-
7/30/2019 Bachelor Thesis Maurits Kruithof
3/32
3
1. I ntroduction
The bankruptcy of Lehman Brothers in September 2008 was for many people the beginning of
uncertain times. The world was facing a financial crisis, which was followed by a recession.
Bankruptcies and financial distress lie in wait for many businesses. What impact does the
recession have on the financial health of companies? Are the reserves big enough to deal with
liquidity problems and decreasing sales and profits?
In this thesis, all the non-financial companies of the AEX- and AMX-indices will be checked on
their financial health by using the Z-Score Model. This model was developed by Edward
Altman in 1968. It was used to predict corporate defaults, but it is also useful as a tool to
recognize financial difficulties. The financial companies are excluded because this model is not
suitable for banks, insurance companies and real estate investment companies.
In chapter two, after the introduction, this model will be explained and compared with other
models in a literature study. Is it really true that one can predict corporate defaults (Altman,
1968; Beaver, 1966; Wilcox, 1971)? If so, would it not be a self-fulfilling prophecy, because no
one trusts a firm that is likely to go bankrupt? What kind of information is valuable to predict
default? And, are the models and outcomes reliable or accurate?
During uncertain times like these investors have nothing to go by. Calculating the Z-Score of a
company may help in the process of investing. Chapter three describes the research sample and
data that are used for the calculations. The research in this thesis shows that the Z-Scores of
2008 and 2009 are the lowest of the six-year period 2004-2009. Most ratios used from the
balance sheets have been decreased substantially.
The differences between sectors are analyzed in chapter four. The companies are divided in sixmain sectors, namely industrials, materials, IT & telecom, food & beverage, consumer
discretionary and energy. The Z-Scores are demonstrating similarities between the industrials
and materials sectors with almost equal movements. These sectors are the only two sectors with
a recovery in 2009, where the others did not recover at all. This chapter ends with an analysis of
high and low Z-Scores , and what investors could do with this information.
As is usual, this thesis will be completed with discussion and concluding remarks in chapter five.
-
7/30/2019 Bachelor Thesis Maurits Kruithof
4/32
4
2. Literature Study
In the academic literature many articles and books have been published on the prediction of
bankruptcy. Most of this work is not solely about the prediction of bankruptcy; it also covers the
detection of financial difficulties and other financial risks (Altman, 2006). Investors, regulators,
security analysts, lawyers, managers, consultants and many more need this information for their
judgments.
2.1. First Studies Related to Prediction of Corporate Bankruptcy
Winakor and Smith (1935) concluded that failing firms exhibit significantly different ratio
measurements than continuing companies. Thirty years later in the late 1960s the knowledge,methods and theories of the prediction of bankruptcy began to develop rapidly.
According to Altman (1968), literature in that time suggested that a gap has arisen between
theory (more rigorous statistical techniques with sophisticated models) and practice
(traditional ratio analysis with data from financial statements) and empirical verification
would ensure that that gap could be bridged again. The questions which ratios are the most
important in detecting bankruptcy potential, what weights should be attached to those ratios and
how the weights should be established, needed to be answered (Altman, 1968).
2.2. Discriminant Analysis and Financial Ratios Theory
2.2.1. Beavers Univariate Discriminant Analysis (1966)In 1966 Beaver found that a number of indicators could discriminate between matched samples
of failed and nonfailed firms for as long as five years prior to failure (Beaver, 1966). These 30
indicators or financial ratios perform best to predict a bankruptcy if they are tested one ratio at a
time (univariate). Beaver has categorized these 30 financial ratios in six groups (see Table 1).
This table shows that Beaver explored a wide range of ratios in his study and that there is some
overlap. When for example 30% of the Working Capital of the examined companies is in Cash
or Quick Assets, the ratios 1 and 2 of Group IV and 1 and 4 of Group VI will reveal a similar
behavior, which is not desirable. The data can be analyzed by a comparison of the mean values
and the likelihood ratios. Beaver explains that the multiratio analysis should reduce the common
elements to a minimum to prevent this amount of overlap (Beaver, 1966).
-
7/30/2019 Bachelor Thesis Maurits Kruithof
5/32
5
Table 1 List of Ratios tested by Beaver (1966)
Group I (Cash-Flow Ratios) Group V (Liquid Asset to
1. Cash-Flow to Sales Current Debt Ratios)
2. Cash-Flow to Total Assets 1. Cash to Current Liabilities3. Cash-Flow to Net Worth 2. Quick Assets to Current Liabilities
4. Cash-Flow to Total Debt 3. Current Ratio (Current Assets
Group II (Net-Income Ratios) to Current Liabilities)
1. Net Income to Sales Group VI (Turnover Ratios)
2. Net Income to Total Assets 1. Cash to Sales
3. Net Income to Net Worth 2. Accounts Receivable to Sales
4. Net Income to Total Debt 3. Inventory to Sales
Group III (Debt to Total-Asset 4. Quick Assets to Sales
Ratios) 5. Current Assets to Sales
1. Current Liabilities to Total Assets 6. Working Capital to Sales
2. Long-Term Liabilities to Total Assets 7. Net Worth to Sales3. Current plus Long-Term Liabilities 8. Total Assets to Sales
to Total Assets 9. Cash Interval (Cash to Fund Ex-
4. Current plus Long-Term Liabilities penditures for Operations)
plus Preferred Stock to Total Assets 10. Defensive Interval (Defensive
Group IV (Liquid-Asset to Assets to Fund Expenditures for
Total-Asset Ratios) Operations
1. Cash to Total Assets 11. No-credit Interval (Defensive
2. Quick Assets to Total Assets Assets minus Current Liabilities
3. Current Assets to Total Assets to Fund Expenditures for Operations)
4. Working Capital to Total Assets
Source: Beaver (1966)
Beaver made use of paired samples of 79 failed and 79 non-failed companies with comparable
asset-size and operating within the same industry. The ability to predict failure is strongest in
the cash-flow to total debt ratio. The net income to total assets ratio predicts second best and the
total debt to total assets next best (Beaver, 1966). The question why these three perform better
than the other 27 ratios is not answered. Besides that, these three ratios only perform best in this
sample and may not be significant in other samples. None of these three ratios are used in
Altmans Z-Score model, which is remarkable. Beaver suggested in his next study (1968) that
multivariate models may yield better results than his univariate model.
2.2.2. Altmans Multivariate Discriminant Analysis (1968)It so happened that Altman presented the results of his study in 1968. He selected 33 pairs of
sample companies (bankrupt and non-bankrupt), all in the manufacturing industry. Altman
introduced a function according to the Multiple Discriminant Analysis (MDA) and not the
(popular) regression analysis, although a carefully devised and interpreted multiple regression
-
7/30/2019 Bachelor Thesis Maurits Kruithof
6/32
6
analysis methodology could have been used (Altman, 1968). Altman advocates for MDA
because of the successful use in consumer credit evaluation and investment classification
(Altman, 1968). He collected almost similar data as Beaver and sorted this data into 22 financial
ratios. MDA then attempts to derive a linear combination of these financial ratios which best
discriminates between the groups bankrupt and non-bankrupt. So the linear function that
appears from the computer is the best model for that particular sample. If Altman used
another sample the outcome would be different.
The multivariate model can reach greater statistical significance than the univariate model and
the MDA creates an entire profile of financial ratios relevant to the selected companies, as well
as the interaction of these ratios. Analyzing these ratios together could remove possible
ambiguities (the early mentioned overlap) and misclassifications.
The result of Altmans study is the following model (Altman, 1968):
Z = .012X1 + .014X2 + .033X3 + .006X4 + .999X5 (1)
where: X1= working capital total assets
X2= retained earnings total assets
X3= (earnings before interest and taxes) total assets
X4= (stock price * outstanding shares) total liabilities
X5= Sales total assets
The gap between practice and theory is bridged by presenting an application for the real
credit world: a firm with a Z-Score greater than 2.675 is classified as a nonbankrupt firm.
Firms with a deteriorating financial future have a Z-Score between 1.81 and 2.675, also called
the grey area. The grey area is not really safe, but also not in serious trouble. Potentially
insolvent companies have a Z-Score below 1.81. Chapter 2.3 provides a full explanation of the
Z-Score model.
2.2.3. Developments After Beavers and Altmans WorkBeaver and Altman have set the mainstream theoretical framework for decades to follow.
Altman explains in his book that additional research was undertaken by various researchers, like
Blum, Deakin and Libby (Altman, 2006), addressing several methodological issues. But the
most notable contribution from the 1970s comes from Wilcox (Hillegeist et al, 2004).
-
7/30/2019 Bachelor Thesis Maurits Kruithof
7/32
7
According to Hillegeist, most of the research on the predictive power of financial ratios has
been focused on the statistical methodology (Hillegeist et al, 2004). Altman himself was the first
one to recognize that the MDA was not as widely used as Regression Analysis (1968). It was
more difficult to do valid regression analysis (considering assumptions of normality etc.) than to
calculate a linear formula using MDA with the technology of 1968. The only thing that has to be
done is to discriminate between the two groups (bankrupt and non-bankrupt) and MDA does the
job well. Later, some major statistical developments took place in the early 1980s. An
alternative statistical methodology called the probabilistic or logit methodology was introduced
by Ohlson (Altman, 2006).
2.2.4. OhlsonsProbabilistic Approach (1980)In his study Ohlson (1980) addresses a number of modelling issues and summarizes the many
statistical problems in using the MDA. Until then, MDA was the most popular methodology in
the financial ratio theory and calculations. Ohlsons main issues were (1) the small company
samples; (2) the source of the data (comes from one database Moodys); and (3) not knowing if
the financial statements were published before or after the bankruptcy.
Ohlson (1980) summarizes the statistical problems of MDA as follows:
(i) Looking at the distributional properties of the coefficients, e.g. the variance-covariance matrices should be the same for bankrupt and non-bankrupt firms. A
violation of this condition may be unimportant or irrelevant if the model only has to
discriminate. Ohlson wants to go further than the traditional econometric analysis
and finds it urgent to test variables for statistical significance.
(ii) The score that an MDA model gives has little intuitive interpretation. It may helpfor decision problems, but it does not take away the chance of a misclassification.
(iii) As mentioned before, if a different sample is used it may not lead to the sameconclusions. Classified as an MDA problem by Ohlson the matching procedures toget a set of paired samples are arbitrary. This is in fact a data collection issue.
In contrast with Beaver and Altman the sample used in Ohlsons study was over 2,000 firms, of
which 105 companies failed. Beaver and Altman research after the bankruptcy has taken place
(ex-post), while Ohlson researches ex-ante. Ohlson also estimates a probability of default.
The Probability of Bankruptcy is known as the O-Score (Ohlson, 1980). Together with the Z-
Score they are widely used by banks and investors as credit scoring models to analyze credit
risk.
-
7/30/2019 Bachelor Thesis Maurits Kruithof
8/32
8
2.2.5. Financial Ratios versus Structural Approach TheorySince 1980 more statistical developments were made and Financial Ratios Theory was replaced
by Structural Approach Theory. Formulas like Black and Scholes, Merton, KMV, Jones and
Trussel became more popular and accurate. According to Hillegeist et al (2001), a combination
of the two theories is the most powerful. Apparently, the information captured by the accounting
based theory holds more information than the marked-based theory, and vice versa.
2.3. Z-Score model
The Z-Score model is doing more than just calculating a number which predicts bankruptcy.
The model can also be used as an analytical technique/tool to discover financial risks faced by
the corporation. Critical (financial) problems could be: (1) liquidity problems; (2) operational
problems; (3) shareholders confidence; and (4) leverage problems. The Z-Score is a summary
statistic of these (potential) problems (Arnold and Earl, 2006).
2.3.1. Component Ratios of the Model
The Z-Score model consists of five ratios mentioned in paragraph 2.2.2.; where X1 (Working
Capital/Total Assets) is covering liquidity issues. This ratio is a measure of the net liquid assets
of the firm relative to the total capitalization (Altman, 2006). The working capital ratio is the
most instable ratio of all. For example, banks do not have a clear working capital ratio which
can be used for Z-Score calculations. Therefore they are excluded from the sample used in this
thesis.
X2 (Retained Earnings/Total Assets) covers shareholder claims against assets. When a firm has
high retained earnings relative to their total assets it is financed with profits from previous years
and though have not utilized much debt. This ratio also implicitly shows the age of the firm and
the cumulative profitability over the life of the company (Altman, 2006).
Profitability is measured in X3 (EBIT/Total Assets). This ratio measures the pure productivity of
the firm, independent of tax, leverage or interest factors. It is an important ratio for credit risk
analysis, because it shows the ability of making profits given the total assets. The X3 ratio is the
most significant ratio of the Z-Score model. Thus, a small change of this ratio results in a bigger
change of the Z-Score compared to the other four ratios.
-
7/30/2019 Bachelor Thesis Maurits Kruithof
9/32
9
The variable with the smallest coefficient, but still an important ratio, is X4 (Market Value of
Equity/Total Liabilities). The measure shows how quick the firm can be insolvent and what the
shareholders level of confidence is. If total equity plus debt exceeds the firms assets, the firm
will be insolvent and will go bankrupt. The shareholders confidence is measure d by market
stock prices. Thus, share prices should be positively related with Z-Scores.
The fifth ratio of the Z-Score model is X5 (Sales/Total Assets). This capital turnover ratio ranks
high in its contribution to the overall discriminating ability if the Z-Score Model, but is the least
significant. This is because its relationship to other variables. Although there is a wide variation
among industries, the importance of MDA is its ability to separate groups using multivariate
measures. In the end, the Z-Score Model is an indicator whether firms are financially healthy,
risky or completely insolvent.
The coefficients of the Z-Score Model are determined by the computer algorithm and not by
Altman himself. A computer algorithm is a finite and well-defined sequence of instructions that
describe computations and eventually terminating in a final ending state. If Altman had changed
his sample, other variables and/or different coefficients would be the outcome of the Z-Score
Model. This is also the case for the boundaries of the critical areas. Now, below 1,81 is very
critical, but with a different sample it could have been lower or higher.
2.4.Practical Use of the Z-Score Model
2.4.1. Z-Score Model Accuracy and Type I and II ErrorsSince the introduction of the Z-Score Model in 1968, many tests of the Z-Score Models
accuracy were performed. In his book, Altman (2006) provides outcomes of three important
tests. Again, the research was done after firms went bankrupt or were already distressed (ex-
post). The Z-Score Model, using a cut off score of 2.675, was between 82 percent and 94
percent accurate, one financial reporting period prior to the Chapter 11 (Altman, 2006). On the
other hand, the Type II error (classifying firms as distressed when they do not go bankrupt or
default in their obligations) increased to 25 percent having Z-Scores below 1.81.
The Type I error (firms that go bankrupt or default in their obligations, and are missed by the
Z-Score Model) seems to be quite acceptable (less than 20 percent). Altman concludes that in
almost four decades of research U.S. firms are far more risky than in the past (Altman, 2006).
Firms are more leveraged (debt-equity ratio) and have less retained earnings relative to the total
assets.
-
7/30/2019 Bachelor Thesis Maurits Kruithof
10/32
10
Other tests (Al-Rawi, 2008, Altman 1984, 2002, Gerantonis, 2009 and Moyen, 2005) have
shown similar results. In each test the Z-Score Model is accurate and reliable. But authors also
mention the fact that the Z-Score Model is not the only tool for checking a firms financial
status. One should always consider other tools, facts, calculations and information to determine
distress or default probabilities.
2.4.2. Linking Z-Scores with Bond RatingsInvestors, security analysts, regulators and other parties also look at Bond Ratings, such as the
S&P ratings, Moodys and Fitch. Since there has been a large database and number of defaults
that had ratings attached to their securities, Altman (2006) can link his Z-Score Model to these
ratings and compare them. The results are as follows:
Table 2 Average Z-Scores by S&P Bond Rating, 1996-2001
Average
AnnualNumberof firms
AverageZ-Score
StandardDeviation
AAA 66 6,2 2,06
AA 194 4,73 2,36
A 519 3,74 2,29
BBB 530 2,81 1,48
BB 538 2,38 1,85B 390 1,8 1,91
CCC 10 0,33 1,16
Da
244 -0,2 n.a.aMedian, based on data from 2000 to 2004.
Source: Altman (2006), Compustat data tapes, 1996-2001
Altman uses a three-step process for the assignment of appropriate default probabilities on
corporate credit assets:
1. Credit Scoring Models (Z-Score, O-Score Models)2. Capital Market Risk Equivalentsusually bond ratings.
3. Assignment of Probability of Default and possibly Loss Given Default (LGD) on portfolio.
Some argue step 3 is a combination of step 1 and 2, but Altman uses different methods in order
to calculate PDs and LGDs (Altman 2006). This bachelor thesis focuses on step 1 (Z-Scores)
and step 2 (S&P Bond Ratings), and gives an analysis of the financial status of AEX- and
AMX-listed firms after the financial crisis of 2008 and the recession of 2009.
-
7/30/2019 Bachelor Thesis Maurits Kruithof
11/32
11
3. Model and Data
The Z-Score Model is introduced and explained in the previous chapter. The third chapter
provides the data and Z-Score calculations of all non-financial AEX- and AMX-listed
companies over the period 2004-2009.
3.1. Z-Score Model & Research Sample
As mentioned before in chapter 2, the Z-Score Model provides scores which determine if
companies are potentially in distress or not. The total amount of AEX- and AMX-listed
companies is 50, but in this bachelor thesis only 39 firms will be part of the sample. This is
because some errors aroused with DataStream and the working capital ratio. It seems thatfinancial companies such as banks, insurance and (real estate-) investment companies do not
have a clear working capital ratio to work with, at least DataStream did not provide the ratio
properly. The companies that are excluded from this sample are AEGON, Corio and ING from
the AEX-index. And from AMX-index: BinckBank, Delta Lloyd, EUROCOMMERCIAL, SNS
Reaal and VastNed Retail.
Other firms are recently added to the stock exchange and therefore listed too short (AEX:
TomTom and AMX: Mediq). The AMX firm AMG accessed the stock exchange on 11th July
2007, data for the year 2004 werent available. DataStream did not provide any useful figures of
Reed Elsevier (too many errors), therefore this company is also not included.
Although the Dutch stock exchange has a strong and large financial sector (12,6 percent), the
industry and materials sectors (28 percent) are represented even more in the weighted index.
Furthermore, some of the (world) market leaders have a stock exchange quotation in the
Netherlands and are originally Dutch firms. For example Royal Dutch Shell (15,83 percent),
Philips (8,0 percent), Ahold (4,1 percent), Heineken (3,4 percent) and Akzo Nobel (3,3 percent).
An analysis of the developments and trends of the Z-Scores per sector will be given in chapter
four.
-
7/30/2019 Bachelor Thesis Maurits Kruithof
12/32
12
3.2. Dataset Z-Scores AEX- and AMX-listed Firms
Balance sheets from the six-year period 2004-2009 are used for this dataset. The figures are
imported with DataStream, directly into Excel. The ratios Working Capital, Total Assets, Sales,
EBIT, Equity, Debt and Retained Earnings are extracted from these balance sheets and the
following formula calculated the final Z-Score: Z = .012X1 + .014X2 + .033X3 + .006X4 +
.999X5. The full AEX-overview can be found in Appendix 1 from page 23 to 26 and for AMX-
listed firms from page 27 to 30 in Appendix 2.
The outcomes of the Z-Score calculations are summarized in an overview (Table 3, page 13).
This table is the most important table of this bachelor thesis, because it contains all relevant
information in one overview. Again, Z-Scores below 1,81 means that firms are in the danger
zone, scores between 1,81 and 2,675 are in the grey area and scores above 2,675 are in the
safe zone.
3.3. Z-Score Trends of AEX- and AMX-indices
The average Z-Score over this six-year period is 3,07 for the AEX-index and 2,94 for the AMX-
index (Figure 1, page 14). The lowest score in this research sample is achieved in the year 2008,for both stock exchanges with 2,50 and 2,60 as average Z-Scores. This is within Altmans grey
area, and therefore demonstrates the danger and strength of the 2008 financial crisis. The
retained earnings declined because firms immediately made depreciations. Also the debt/equity
ratio changed due to losses on stock exchanges and increasing amounts of debt.
Most figures and ratios are very volatile over the period 2004-2009, where 2004 is the best year
(expressed in Z-scores) and 2008 is the worst. The decline of Z-Scores starts from 2005 until
2008 for the AMX-index. The AEX on the other hand goes up and down each year and shows
no clear pattern. The recovery after the 2008 financial crisis already starts in 2009. Although,
the 2009 average AEX Z-Score is still below the average score over the six-year period. The
2009 AMX Z-Score is slightly above the six-year average score (3,12 vs. 2.94), which indicates
a strong recovery.
-
7/30/2019 Bachelor Thesis Maurits Kruithof
13/32
13
Table 3 Summary overview of test results: Z-Scores of 39 AEX and AMX firms
AEX-Index
2004 2005 2006 2007 2008 20092004-2009
S&P Credit
Rating
Ahold 2,64 2,56 3,03 3,50 3,95 2,87 3,09BBB-
Air France KLM 1,55 1,04 1,50 1,63 1,78 0,95 1,41Akzo Nobel 2,55 2,62 2,85 4,08 1,80 2,31 2,70BBB+
Arcelor Mittal 3,96 2,06 1,82 2,19 2,35 2,82 2,53BBB+
ASML Holding 2,86 2,96 6,03 4,54 4,44 3,24 4,01BBB-
BAM 3,26 1,91 1,71 1,82 2,01 1,83 2,09
Boskalis 17,21 7,28 6,64 6,58 2,93 11,20 8,64
DSM 3,03 3,78 4,32 3,85 3,29 3,45 3,62A-
Fugro 1,46 2,44 2,29 2,49 2,56 2,78 2,34
Heineken 2,11 2,21 2,64 2,93 1,30 1,65 2,14
KPN 1,44 0,53 0,61 0,58 0,61 0,87 0,77BBB+
Philips 4,30 4,47 5,51 6,27 4,20 3,96 4,79A-
Randstad 5,27 5,08 8,70 4,61 2,02 3,70 4,90
Royal Dutch Shell 6,08 6,49 6,65 6,87 5,77 4,83 6,12AA+SBM Offshore 1,65 1,73 1,77 1,99 1,51 1,88 1,76
TNT 3,29 3,73 3,15 2,59 2,42 2,37 2,93BBB+
Unilever 1,97 2,22 2,67 2,84 2,90 2,97 2,60A+
Unibail-Rodamco 1,42 2,34 2,65 1,62 0,93 0,78 1,62
Wereldhave 1,66 1,49 2,85 2,47 2,01 1,87 2,06
Wolters Kluwer 1,17 1,25 1,19 1,56 1,26 1,21 1,27BBB+
Average 3,44 2,91 3,43 3,25 2,50 2,88 3,07
AMX-Index
2004 2005 2006 2007 2008 20092004-2009
S&P Credit
Rating
Aalberts Indust. 1,87 1,83 1,81 2,15 1,88 1,99 1,92
AMG #NA 1,72 1,77 2,72 1,84 2,31 2,07
Arcadis 4,83 3,04 2,89 2,43 2,52 2,76 3,08
ASM International 2,06 1,94 2,67 3,22 3,45 1,76 2,52BB-
Crucell 6,73 7,23 5,86 4,49 4,54 9,13 6,33
CSM 2,73 3,94 3,07 3,55 3,31 3,96 3,43
Draka 1,81 1,80 1,92 2,30 2,70 2,92 2,24
Heijmans 2,92 2,50 2,41 2,47 2,19 2,91 2,57
Imtech 5,08 5,26 3,80 3,10 2,18 2,65 3,68
Logica 2,26 2,50 2,08 2,49 3,11 3,80 2,71
Nutreco 3,54 3,86 4,38 3,16 3,06 4,24 3,71Ordina 3,20 3,77 2,98 3,21 1,56 2,87 2,93
Oc 3,05 1,74 2,11 2,42 2,22 2,09 2,27
Smit International 3,16 2,54 2,87 2,54 1,99 3,32 2,74
Ten Cate 3,46 2,86 4,21 2,88 2,62 3,46 3,25
USG People 3,65 1,06 1,72 2,68 2,94 3,50 2,59
Vopak 1,66 1,66 1,77 1,81 1,53 1,73 1,69
Wavin 1,45 1,01 1,73 1,91 1,96 2,68 1,79
Wessanen 5,27 5,62 5,05 4,34 3,78 1,28 4,22
Average 3,26 2,94 2,90 2,84 2,60 3,12 2,94
Source: DataStream and author calculations using the Z-Score Model, June 2010
-
7/30/2019 Bachelor Thesis Maurits Kruithof
14/32
14
Figure 1 Average Z-Scores AEX- and AMX-index (2004-2009 period)
Source: DataStream and author calculations using the Z-Score Model, June 2010
More operational ratios such as EBIT and Sales achieved their lowest values in 2009. The
liquidity and banking crisis took place in 2008 and therefore also the financial difficulties and
risks, but the real recession and economic depression affected the balance sheets in 2009, which
were operational difficulties.
Since the beginning of the financial crisis in 2008, the AMX-index performs better than the
AEX-index, expressed in value changes (percentages). In this research sample the average Z-
Scores are higher since 2008. It is not unusual that mid caps perform better in a bullmarket than
large caps, because investors are risk-seeking. In a bearmarket, risky assets (mid caps in this
case) are sold more often.
Besides the differences of the AEX- and AMX-indices, there are also discrepancies among
sectors. Some sectors made strong recoveries in 2009, whereas others did not recover at all.
Chapter four will give an analysis of this phenomenon.
-
7/30/2019 Bachelor Thesis Maurits Kruithof
15/32
15
4. Analysis
This chapter analyzes the Z-Scores more detailed per sector. It also covers the patterns, trends,
rationale and developments of the Z-Scores from the dataset used in this bachelor thesis. Finally,
this chapter explains where the Z-Score can be used for and which parties may benefit from the
calculations if investment decisions have to be made.
4.1. AEX- and AMX-sectors Analysis
The AEX- and AMX-indices can be divided into six main sectors (seven if the financial sector
was included). Table 4 provides an overview of how the companies are scattered:
Table 4 Companies per sector
IT &Telecom
Food &Beverage
ConsumerDiscretionary
Industrials (1) Industrials (2) Materials Energy
ASML Ahold Oc Aalberts SBM Akzo Nobel Shell
ASM Int. CSM Philips Air-France KLM Smit AMG VopakImtech Heineken Wolters Kluwer Arcadis TNT Arcelor MittalKPN Nutreco BAM Unibail Rodamco Crucell
Logica Unilever Boskalis USG People DrakaOrdina Wessanen Fugro Wavin DSM
Heijmans Wereldhave Ten CateRandstad
Source: author compilation, June 2010
The sectors are the same as Bloomberg uses in its analysis. The industrials are very well
represented with 15 companies, while the energy and consumer discretionary on the other hand
only consists of respectively two and five companies. The energy sector has a very high Z-Score,
but that is because Royal Dutch Shell has a very strong financial structure and solvency. At the
same time Royal Dutch Shell is the only firm with a AA+ credit rating. Therefore, the image of
a strong energy sector is distorted.
Each sector of the stock exchange has different Z-Scores over the period 2004-2009. The
materials and industrials sector demonstrate similar patterns and trends, with strong recoveries
in 2009 after a huge drop in 2008. The other four sectors also point out similar movements with
further decreases in 2009. Figure 2 provides an overview of the Z-Scores per sector.
-
7/30/2019 Bachelor Thesis Maurits Kruithof
16/32
16
Figure 2 Z-Scores of all AEX- and AMX-sectors
Source: DataStream and author calculations using the Z-Score Model, June 2010
The reason why the materials and industrials sectors move together has to do with their business
cycles. Each sector has a different business cycle within the economic and market cycle. The
Sector Rotation Model (Figure 3) shows that investors try to anticipate how the market reacts to
economic changes.
Figure 3 Sector Rotation Model
Source:www.stockcharts.com, Legend: Market Cycle and Economic Cycle
http://www.stockcharts.com/http://www.stockcharts.com/http://www.stockcharts.com/http://www.stockcharts.com/ -
7/30/2019 Bachelor Thesis Maurits Kruithof
17/32
17
The difficulty for investors is to identify in which cycle the economy is. The very low Z-Scores
of 2008 and many recoveries in 2009 indicate that the market bottom (Market Cycle) and full
recession (Economic Cycle) of this Sector Rotation Model are behind us. Therefore, the best
moment to invest (at the lowest point in the market) is already behind us.
Appendix 3 provides a full overview of all sectors with the linked Z-Scores. This shows
together with Figure 2 that the weakest sectors (expressed in Z-Scores) during the financial
crisis are the Industrials and IT & Telecom sectors and the strongest are the Energy and Food &
Beverage sectors. It is up to the investor if he or she wants to invest in a strong or weak sector.
The growth and profitability perspectives of weak sectors are maybe better, but could also be
more risky because failure or default is nearby. This trade off is the basic principle of most
investments.
4.2. Investment Decisions
According to the Sector Rotation Model and with the recoveries taken into account, the Market
Cycle is now bull market. This means investors are more risk-seeking than before and thus will
take more positions in the AMX-index. The performance of this index will be better than the
larger AEX-index, if we follow the above explanation. But there is also a lot of uncertainty and
volatility around markets, because investors and other parties expect a second crisis.
4.2.1. High Z-ScoresGood or wise investment decisions are above all a matter of experience rather than science.
Tools like the Z-Score model can make decisions more sophisticated, but not comprehensive.
Firms with high Z-Scores are generally financed very safe and have a solid financial structure.
They do not have a high leverage ratio (X4) and mostly keep a lot of retained earnings (X2).
High profits from shareholding cannot be made very quickly in a short time. In most cases,
these companies are stable in the long run. Companies from the AEX- and AMX-indices with
average Z-Scores above the value of 2,675 are considered highand safe (Altman, 2006).
The risk is relatively low, but this also means returns and/or yield are low, according to the
Capital Asset Pricing Model. Investors should have a long time horizon for these types of
companies and should not expect high returns or profits in the short-run (Table 5).
-
7/30/2019 Bachelor Thesis Maurits Kruithof
18/32
18
Table 5 Companies with high Z-Scores for safe and long-term investments
AEX-index AMX-index
Ahold Arcadis
AkzoNobel Crucell
ASML CSM
Boskalis ImtechDSM Logica
Philips Ordina
Randstad Nutreco
Royal Dutch Shell Smit
TNT Ten Cate
Wessanen
Source: author compilation, July 2010
4.2.2. Low Z-ScoresFirms with low Z-Scores have an aggressive type of financing compared to firms with high Z-
Scores. Short-term profits are easier made and the shares (or securities) are far more risky to
hold. In some cases firms are shortcoming of sales or working capital. This means weak
productivity (X5) and a fragile financial structure (X1). The Z-Score tells the investor to be
careful with this company and he or she should do more analysis and research before investing
in this particular company. There are 19 companies with an average Z-Score above 2,675 and
20 with an average Z-Score below 2,675.
Table 6 Companies with low Z-Scores (within Altmans grey area or below)
AEX-index AMX-index
Air France-KLM Aalberts Industries
Arcelor Mittal AMG
BAM ASM International
Fugro Draka
Heineken Heijmans
KPN Oc
SBM Offshore USG People
Unilever Vopak
Unibail-Rodamco Wavin
Wereldhave
Wolters Kluwer
Source: author compilation, July 2010
Companies with red marking have Z-Scores below Altmans 1,81 and are considered dangerous
and risky. If an investor wants to invest in these companies, he or she should check other
models.
-
7/30/2019 Bachelor Thesis Maurits Kruithof
19/32
19
4.2.3. Z-Scores and S&PBond RatingIn the end, the Z-Score is not the only factor or model to check when making investment
decisions. As mentioned before in chapter 2.4.2., bond ratings are step 2 in the Altman (2006)
process. These ratings are very important for investors and they rely on it. Not every AEX- and
AMX-company has a S&P credit rating, therefore not all 39 companies are included in Table 7.
Table 7 S&P bond ratings compared with Z-Scores
Company Avg. Z-Score S&P Credit Rating
Royal Dutch Shell 6,12 AA+
Philips 4,79 A-
ASML Holding 4,01 BBB-
DSM 3,62 A-
Ahold 3,09 BBB-TNT 2,93 BBB+
AkzoNobel 2,70 BBB+
Unilever 2,60 A+
Arcelor Mittal 2,53 BBB+
ASM International 2,52 BB-
Wolters Kluwer 1,27 BBB+
KPN 0,77 BBB+
Source: DataStream and author calculations using the Z-Score Model, June 2010
There are four discrepancies (compared to Table 2) in this table, namely ASML Holding,
Unilever, Wolters Kluwer and KPN. The imperfection of ASML Holding is a low debt/equity
ratio (three times more equity than debt) which makes the Z-Scores very high. Unilever has a
very large and negative working capital ratio, which causes the low Z-Scores. The problem with
Wolters Kluwer is that the company has two times more debt than equity and also has a
negative working capital ratio. KPN beats them all with four times more debt than equity,
negative retained earnings, a small EBIT and a negative working capital ratio.
-
7/30/2019 Bachelor Thesis Maurits Kruithof
20/32
20
5. Conclusion
Over the last four decades, many articles have been published on corporate financial distress
and bankruptcy. In summary, the first methodology tries to predict default based on a number of
financial ratios. Beaver and Altman (1966 and 1968) both used the Discriminant Analysis
within the framework of the Financial Ratios Theory. The Z-Score Model became the most
popular and widely used model until a more structural approach was introduced by Wilcox
(1971). In 2001, Hillegeist found that a combination of the two theories was the strongest.
Although the Z-Score Model is linear, the underlying hypotheses of Altmans model may not be
formulated as linear equations. It is not clear if the interdependence between variance, variables
and volatility influence the results of the researches. Therefore, further (statistical) methodsneed to be developed in the near future.
Nevertheless, the information and accuracy of the Z-Score Model should not be underestimate.
Until today, many tests have shown that the Z-Score Model is a good indicator for financial
difficulties. In this thesis, Z-Scores of all the AEX- and AMX-index companies were calculated,
excluding the financials. The influence of the 2008 financial crisis is reflected by the Z-Scores
calculated in Table 3, with the lowest scores in that particular year as a first result of the test.
There are some recoveries in 2009, but in most cases the Z-Scores are still not above theaverage score of the period 2004-2009. The recoveries demonstrate that the worst part of the
crisis is behind us.
The sector analysis shows the similarities of the industrials and materials sectors. These sectors
are the only two sectors with a higher Z-Score in 2009 compared to 2008. The Sector Rotation
Model explains why the sectors have different patterns. This is because sectors have their own
business cycle within the Economic and Market Cycles. This is important information for
investors, given the fact that they want to invest at the lowest point of the market. Because the
industrials and materials sectors showed recoveries, other sectors will show these in 2010.
Investors should take advantage of this information and analysis.
The Z-Scores also separate chaff from wheat. Companies with high Z-Scores in combination
with high credit ratings are considered as safe investments. Low Z-Scores tell the investor to
be cautious. This thesis showed that, by using the Z-Score Model, investments decisions can be
well thought-out and that the model is a practical and useful tool to support in that process.
-
7/30/2019 Bachelor Thesis Maurits Kruithof
21/32
21
6. References
Al-Rawi, K. et al. (2008) The Use Of Altman Equation For Bankruptcy Prediction In An
Industrial Firm,International Business & Economics Research Journal, Vol. 7, pp. 115-128.
Altman, Edward I. (1968) Financial Ratios, Discriminant Analysis and the Prediction of
Corporate Bankruptcy,Journal of Finance, Vol. 23, pp. 589-609.
Altman, Edward I. (1984) The Succes Of Business Failure Prediction Models, Journal of
Banking and Finance, Vol. 8, pp 171-198.
Altman, Edward I. (2000) Predicting Financial Distress of Companies: Revisiting the Z-Score
and Zeta Models, Working Paper.
Altman, Edward I (2002) Corporate Distress Prediction Models In A Turbulent Economic And
Basel II Environment, Working Paper, Stern School of Business, NY University.
Altman, Edward I. and Hotchkiss, E. (2006) Corporate Financial Distress and Bankruptcy, 3rd
edition, John Wiley and Sons Ltd.
Arnold, T. and Earl Jr., J.H. (2006) Applying Altmans Z-Score in the Classroom,Journal of
Financial Education, Vol. 32, pp. 98-103.
Beaver, W. (1966) Financial Ratios as Predictors of Failure, Journal of Accounting Research,
Vol. 4, pp. 71-111.
Berk, J. and DeMarzo, P (2007) Corporate Finance, Pearson Addison Wesley.
Eisdorfer, A. (2008) Empirical Evidence of Risk Shifting in Financially Distressed Firms,
Journal of Finance, Vol. 63 no. 2, pp. 609-637.
Hillegeist, S., Keating, E., Cram, D., Lundstedt, K. (2004) Assessing the Probability of
Bankruptcy,Review of Accounting Studies, Vol. 9, pp. 5-34.
Gerantonis, N. et al. (2009) Can Altman Z-Score Models Predict Business Failures in Greece,
Research Journal of International Studies, Vol. 12, pp. 21-28.
-
7/30/2019 Bachelor Thesis Maurits Kruithof
22/32
22
Moyen, N and Bhagat, S (2005) Investment and Internal Funds of Distressed Firms, Journal of
Corporate Finance, Vol. 11, pp. 449-472.
Ohlson, J. (1980) Financial Ratios and the Probabilistic Prediction of Bankruptcy, Journal of
Accounting Research, Vol.19, pp. 109-131.
Ramaswami, M. and Moeller, S. E. (1990)Investing in Financially Distressed Firms, Quorum
Books
Winakor, A., and Smith, R. (1935). Changes in the Financial Structure of Unsuccessful
Industrial Corporations.Bulletin No. 51, University of Illinois, Bureau of Business
Websites:
Euronext, Index Announcement 2009,http://www.euronext.com/fic/000/055/017/550171.pdf
Standard and Poors Credit Ratings,www.standardandpoors.com
StockCharts, investors website,www.stockcharts.com
http://www.euronext.com/fic/000/055/017/550171.pdfhttp://www.euronext.com/fic/000/055/017/550171.pdfhttp://www.euronext.com/fic/000/055/017/550171.pdfhttp://www.standardandpoors.com/http://www.standardandpoors.com/http://www.standardandpoors.com/http://www.stockcharts.com/http://www.stockcharts.com/http://www.stockcharts.com/http://www.stockcharts.com/http://www.standardandpoors.com/http://www.euronext.com/fic/000/055/017/550171.pdf -
7/30/2019 Bachelor Thesis Maurits Kruithof
23/32
23
Appendix 1 Z-Score Calculations of 20 AEX-listed Firms (DataStream/Excel)
Name AHOLD AIRF/KLM AKZO NOBEL ARCELORMITTAL ASML
Code TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS
2004 20096000 12844000 11700000 13485630 3042666
2005 19367010 21390000 11530000 25597420 3549139
2006 17914000 26472000 11832000 83755970 3750657
2007 13574000 26644000 18613010 90417260 3926720
2008 13234000 30661010 17844000 95017970 37912612009 13504000 27962000 18087010 85755580 3594234
Name AHOLD AIRF/KLM AKZO NOBEL ARCELORMITTAL ASML
Code WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL
2004 1311000 -221000 3438000 2502115 1868871
2005 516000 -910000 2951000 5158062 1785836
2006 835000 402000 3393000 11219920 2244625
2007 894000 274000 10540000 8986515 2309176
2008 1158000 957000 660000 9803572 1964906
2009 1080000 -3337000 1681000 6475346 1704714
Name AHOLD AIRF/KLM AKZO NOBEL ARCELORMITTAL ASML
Code EBIT EBIT EBIT EBIT EBIT
2004 284000 177000 1319000 5120831 406911
2005 280000 758000 1479000 4072676 487546
2006 1486000 1585000 1661000 6569930 9202862007 1265000 1521000 829000 12124400 903552
2008 1328000 1515000 -457000 9241871 359542
2009 1330000 -883000 714000 -1965105 -122247
Name AHOLD AIRF/KLM AKZO NOBEL ARCELORMITTAL ASML
Code SALES SALES SALES SALES SALES
2004 56068000 12325000 13051000 4766153 1542737
2005 54107010 12337000 12902000 4956951 1678180
2006 52000000 19078000 12705000 21355890 2696572
2007 45643010 21448000 13351000 24599060 2473677
2008 44014000 23073010 12846000 58084420 3927957
2009 25722000 24660000 15415000 85018940 2218095
Name AHOLD AIRF/KLM AKZO NOBEL ARCELORMITTAL ASML
Code EQUITY EQUITY EQUITY EQUITY EQUITY
2004 4508000 4043000 3035981 4308502 13916012005 4651000 5134000 3414981 8586900 1711836
2006 5030000 7734000 4144000 31932270 2156455
2007 3810000 8299000 11032000 38829230 1907617
2008 4676000 10536000 7463000 39632160 1988769
2009 5440000 5622000 7775000 42609410 1774768
Name AHOLD AIRF/KLM AKZO NOBEL ARCELORMITTAL ASML
Code TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT
2004 9270000 4337000 2952000 1459260 825056
2005 7082000 8189000 3059000 7028568 878911
2006 5983000 9081000 2961000 20137790 388839
2007 4882000 8555000 3589000 20979490 603222
2008 3744000 7920000 3679000 24466580 647050
2009 3203000 9419000 3872000 17318780 663102
Name AHOLD AIRF/KLM AKZO NOBEL ARCELORMITTAL ASMLCode RETAINED EAR. RETAINED EAR. RETAINED EAR. RETAINED EAR. RETAINED EAR.
2004 -8110000 93000 770000 3492643 351570
2005 -9756000 359000 860000 6675786 663034
2006 -8993000 913000 1067000 11350290 1239689
2007 -6923000 891000 9225000 16133120 1526201
2008 -5260000 748000 -1179000 21829360 1698929
2009 -4598000 -814000 215000 20757120 1450156
Z-SCORES Z-SCORES Z-SCORES Z-SCORES Z-SCORES
2004 2,638930214 1,552924902 2,548201702 3,96291202 2,858689137
2005 2,559460828 1,041738789 2,622554844 2,058458846 2,95964833
2006 3,031145751 1,49506045 2,846054632 1,815466528 6,036369973
2007 3,499963329 1,6337665 4,08125076 2,19385916 4,535919359
2008 3,951564354 1,784614978 1,803677346 2,349016155 4,441420332
2009 2,86620682 0,950983209 2,314660185 2,820459917 3,244150964
AVG. 3,09 1,41 2,70 2,53 4,01
-
7/30/2019 Bachelor Thesis Maurits Kruithof
24/32
24
Name BAM BOSKALIS DSM FUGRO HEINEKEN
Code TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS
2004 3228950 1070434 8576000 958723 10401000
2005 4953802 1325495 9508000 1117148 11543000
2006 6393628 1580829 9595000 1382167 12602000
2007 6968671 2198047 9482000 1682093 12632000
2008 6691222 2544813 9261000 2097017 203040002009 6700800 2544813 9292000 2340640 19619010
Name BAM BOSKALIS DSM FUGRO HEINEKEN
Code WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL
2004 -57923 -132200 2084000 -95348 126000
2005 223432 -52189 1774000 222485 -474000
2006 528797 -2063 1478000 150773 229000
2007 1065194 12161 1627000 171347 -349000
2008 1035530 -264284 1380000 57542 -309000
2009 786500 33531 2251000 140301 -1203000
Name BAM BOSKALIS DSM FUGRO HEINEKEN
Code EBIT EBIT EBIT EBIT EBIT
2004 140774 43053 514000 101262 1155000
2005 267004 84593 754000 149065 1368000
2006 301748 156389 813000 206748 18950002007 303458 257571 650000 321881 1544000
2008 304546 321005 842000 416749 1166000
2009 2500 245333 141000 359599 1934000
Name BAM BOSKALIS DSM FUGRO HEINEKEN
Code SALES SALES SALES SALES SALES
2004 7732023 1045523 6050000 830321 9255000
2005 6342257 980523 6470000 905821 9474000
2006 7485975 1136603 7516000 1046008 10345000
2007 7712231 1170721 8096000 1294815 11392000
2008 8747131 1555814 8430000 1608219 12218000
2009 8834766 2093800 9297000 2154474 14319000
Name BAM BOSKALIS DSM FUGRO HEINEKEN
Code EQUITY EQUITY EQUITY EQUITY EQUITY
2004 501913 464979 4745000 223913 33790002005 581677 542851 5408000 465460 3969000
2006 692594 618636 5718000 562417 5009000
2007 993530 768050 5244000 699989 5404000
2008 847400 860118 4567000 929811 4471000
2009 875000 1295767 4883000 1187731 5351000
Name BAM BOSKALIS DSM FUGRO HEINEKEN
Code TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT
2004 442981 17213 1588000 453173 3571000
2005 1008959 54525 1710000 337305 3255000
2006 1849107 71401 1514000 441886 3299000
2007 2175727 87081 1752000 534860 2656000
2008 2128527 319160 2293000 616449 10053000
2009 2106800 81430 2204000 634721 7862000
Name BAM BOSKALIS DSM FUGRO HEINEKENCode RETAINED EAR. RETAINED EAR. RETAINED EAR. RETAINED EAR. RETAINED EAR.
2004 154897 30170 262000 49317 2050000
2005 199360 386355 4850000 99412 2617000
2006 284577 448250 5509000 141011 3559000
2007 580723 576681 5180000 216213 3928000
2008 623387 742829 4663000 283412 3761000
2009 587059 907589 5142000 263410 3679000
Z-SCORES Z-SCORES Z-SCORES Z-SCORES Z-SCORES
2004 3,261525107 17,20768252 3,029732306 1,462888425 2,113595816
2005 1,913238803 7,284031979 3,777069522 2,441883699 2,210771354
2006 1,711717418 6,638694999 4,316866665 2,287044616 2,644502382
2007 1,823373653 6,584699491 3,845806462 2,487905594 2,927259383
2008 2,011156072 2,928022624 3,288146801 2,549102336 1,29857257
2009 1,831073437 11,20278636 3,444355868 2,778772313 1,651749185
AVG. 2,09 8,64 3,62 2,33 2,14
-
7/30/2019 Bachelor Thesis Maurits Kruithof
25/32
25
Name KPN PHILIPS RANDSTAD SHELL SBM
Code TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS
2004 21519010 29260000 1604900 140631400 2005179
2005 21354000 32338000 1961800 183543100 2081926
2006 20240000 37353010 2248800 176089500 2220002
2007 22612000 35372000 3034700 182358600 2483828
2008 22180000 32488000 7300800 200309800 31104282009 22777010 29284000 5992800 200778300 3242485
Name KPN PHILIPS RANDSTAD SHELL SBM
Code WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL
2004 -593000 4148000 583000 872608 117612
2005 -1884000 4731000 669300 10937090 -149230
2006 -761000 5832000 522200 11473850 109061
2007 -2517000 8198000 639600 14393910 140410
2008 -2026000 3938000 1354500 7927438 -242896
2009 -748000 3859000 500600 8144264 -96197
Name KPN PHILIPS RANDSTAD SHELL SBM
Code EBIT EBIT EBIT EBIT EBIT
2004 2542000 2129000 229100 22212940 101779
2005 2356000 2176000 297100 36562000 230344
2006 2211000 1556000 432000 35823060 2128302007 2485000 4744000 551800 37071220 235377
2008 2541000 338000 12900 34923940 203721
2009 2811000 745000 98800 15458450 219285
Name KPN PHILIPS RANDSTAD SHELL SBM
Code SALES SALES SALES SALES SALES
2004 11870000 29037010 5257400 176707700 1619367
2005 11105000 29169010 5294100 172292000 1230419
2006 11716000 29615010 5919500 221750900 784764
2007 11896000 31134000 7042800 253676000 1424550
2008 11936000 25593010 8474800 244556100 1893974
2009 14427000 26385010 14038400 311914200 2082518
Name KPN PHILIPS RANDSTAD SHELL SBM
Code EQUITY EQUITY EQUITY EQUITY EQUITY
2004 6821000 14860000 341700 62332510 5640642005 5076000 16666000 536200 76917470 757207
2006 4195000 22997010 790300 80140300 847975
2007 4490000 21684000 1021600 84912610 913404
2008 3730000 16243000 2251100 91390620 886535
2009 3838000 14595000 2325200 95228830 1258227
Name KPN PHILIPS RANDSTAD SHELL SBM
Code TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT
2004 9442000 4513000 220800 10629010 1000981
2005 9258000 4487000 247800 10926940 808392
2006 9068000 3869000 96200 11955930 706557
2007 11755000 3557000 528300 12397820 791431
2008 12041000 4158000 2472000 16707140 1216500
2009 13371000 4267000 1284800 24453040 1107078
Name KPN PHILIPS RANDSTAD SHELL SBMCode RETAINED EAR. RETAINED EAR. RETAINED EAR. RETAINED EAR. RETAINED EAR.
2004 1511000 19346000 191000 77594136 385496
2005 -8771000 21710000 241900 79542610 451702
2006 -8153000 22085010 375900 78165710 513648
2007 -6465000 25559010 597900 78851730 610127
2008 -6103000 20577010 447900 92543740 756614
2009 -4982000 15947000 509400 91491740 849007
Z-SCORES Z-SCORES Z-SCORES Z-SCORES Z-SCORES
2004 1,439558109 4,302887521 5,27470533 6,075043336 1,651928143
2005 0,531671376 4,467171249 5,075986755 6,496904578 1,735265004
2006 0,607272242 5,510974763 8,705378041 6,650822291 1,772476348
2007 0,583557456 6,269310685 4,607469418 6,870015303 1,989878637
2008 0,606692914 4,197358717 2,020380208 5,771409672 1,508536661
2009 0,866628617 3,956841688 3,699715725 4,829296621 1,877682591
AVG. 0,77 4,78 4,90 6,12 1,76
-
7/30/2019 Bachelor Thesis Maurits Kruithof
26/32
26
Name TNT UNILEVER UNIBAIL-RODAM. WERELDHAVE WOLTERSKLUWER
Code TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS
2004 7887000 32902000 6335000 2049700 4615000
2005 8208000 38107010 8677200 2440640 5417000
2006 6097000 36612000 10842900 2650173 5597000
2007 6892000 36818000 25500800 2802637 5234000
2008 6980000 35406000 24871810 2823185 63500002009 7462000 36608000 22633810 2595599 5946000
Name TNT UNILEVER UNIBAIL-RODAM. WERELDHAVE WOLTERSKLUWER
Code WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL
2004 584000 -3704000 2854700 0 -169000
2005 1249000 -3917000 3465400 -910000 -927000
2006 -119000 -3959000 2289400 402000 -1569000
2007 -660000 -3214000 2765400 274000 -1521000
2008 -209000 -2366000 3001800 957000 -1099000
2009 -27000 -481000 3187400 1135800 -884000
Name TNT UNILEVER UNIBAIL-RODAM. WERELDHAVE WOLTERSKLUWER
Code EBIT EBIT EBIT EBIT EBIT
2004 1192000 3521000 379100 194200 330000
2005 1216000 5414000 1720400 253853 462000
2006 1392000 5321000 2529400 454939 5270002007 1283000 5594000 1339300 288402 533000
2008 1008000 7507000 -774700 35298 502000
2009 632000 5284000 -1443300 -114306 232000
Name TNT UNILEVER UNIBAIL-RODAM. WERELDHAVE WOLTERSKLUWER
Code SALES SALES SALES SALES SALES
2004 11785000 42693010 640400 174300 3436000
2005 11853000 42254000 674700 181400 3422000
2006 9372000 38771010 657100 167060 3242000
2007 9412000 39941010 767900 194823 3482000
2008 9980000 39635010 713600 214258 3406000
2009 10707000 40523010 1786200 227281 3374000
Name TNT UNILEVER UNIBAIL-RODAM. WERELDHAVE WOLTERSKLUWER
Code EQUITY EQUITY EQUITY EQUITY EQUITY
2004 2765000 4032000 1924000 1442400 7750002005 3262000 8361000 4076100 1542162 1098000
2006 1983000 11230000 6053100 1776209 1194000
2007 1931000 12387000 14603700 1850065 1178000
2008 1733000 9948000 12885100 1740283 1414000
2009 2060000 12065000 11312900 1569554 1334000
Name TNT UNILEVER UNIBAIL-RODAM. WERELDHAVE WOLTERSKLUWER
Code TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT
2004 1491000 12266000 2172000 500000 2446000
2005 1284000 12492000 2869100 630100 2155000
2006 1566000 8664000 2923500 541039 2175000
2007 2085000 9525000 7468800 592597 1954000
2008 2241000 11081000 8445900 739586 2597000
2009 2016000 9834000 8190500 712814 2421000
Name TNT UNILEVER UNIBAIL-RODAM. WERELDHAVE WOLTERSKLUWERCode RETAINED EAR. RETAINED EAR. RETAINED EAR. RETAINED EAR. RETAINED EAR.
2004 572000 6097000 219000 62036 135000
2005 559000 10015000 1701400 101350 918000
2006 561000 12724000 2495200 125443 1227000
2007 871000 15162000 2005800 146656 2032000
2008 434000 15812000 -1054000 152961 1564000
2009 247000 17350000 -1467800 143529 1540000
Z-SCORES Z-SCORES Z-SCORES Z-SCORES Z-SCORES
2004 3,294546856 1,970999218 1,419104909 2,170864437 1,166869893
2005 3,733771166 2,222734089 2,338121631 1,496691576 1,250137784
2006 3,154198194 2,672007042 2,648197673 2,847534047 1,189278899
2007 2,596294016 2,837192286 1,616826107 2,472781174 1,557175797
2008 2,420038001 2,901699676 0,92673827 2,011533142 1,260541959
2009 2,368025954 2,966027993 0,775338999 1,865817303 1,210429925
AVG. 2,93 2,60 1,62 2,14 1,27
-
7/30/2019 Bachelor Thesis Maurits Kruithof
27/32
27
Appendix 2 Z-Score Calculations of 19 AMX-listed Firms (DataStream/Excel)
Name AALBERTS AMG ARCADIS ASM int. CRUCELL
Code WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL
2004 3310 #NA 77615 293958 69384
2005 13274 128403 129167 336264 108290
2006 65524 74925 131513 381204 226819
2007 90692 210663 96020 392213 188520
2008 94568 144212 173834 372029 2040172009 68588 154862 257694 419535 496905
Name AALBERTS AMG ARCADIS ASM int. CRUCELL
Code TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS
2004 788554 #NA 390465 823054 98891
2005 971115 447758 636909 811772 169737
2006 1266640 416072 728179 831245 652907
2007 1418185 590477 907600 839382 6249202008 1677946 790405 1046138 765513 636297
2009 1558164 558446 1297194 843155 1010923
Name AALBERTS AMG ARCADIS ASM int. CRUCELL
Code SALES SALES SALES SALES SALES
2004 784589 #NA 848457 581868 7424
2005 826789 684536 862957 659972 9309
2006 959911 732396 910205 693101 22943
2007 1252919 732736 1063943 798179 40852
2008 1601547 837668 1280279 881094 151313
2009 1750800 979602 1740239 639370 294861
Name AALBERTS AMG ARCADIS ASM int. CRUCELL
Code EBIT EBIT EBIT EBIT EBIT
2004 91921 #NA 34259 90136 -16597
2005 120426 43803 58895 23730 -154462006 164804 31002 76587 134132 -96379
2007 195007 41195 101066 142423 -46706
2008 165367 44972 120100 72510 8995
2009 89309 -9611 125699 -55931 40915
Name AALBERTS AMG ARCADIS ASM int. CRUCELL
Code EQUITY EQUITY EQUITY EQUITY EQUITY
2004 226837 #NA 145669 256716 78535
2005 298440 -23526 176203 238594 137609
2006 383649 -25854 188881 276458 497300
2007 530448 168280 187715 318878 4372422008 577010 182872 207585 317682 453492
2009 615657 148416 351704 241229 738265
Name AALBERTS AMG ARCADIS ASM int. CRUCELLCode TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT
2004 350665 #NA 42443 297253 4597
2005 408730 218392 122801 257400 10278
2006 512571 209785 143208 228500 46413
2007 514801 96436 226382 186936 52795
2008 765298 166600 281491 153682 60751
2009 630667 142250 374562 243249 52300
Name AALBERTS AMG ARCADIS ASM int. CRUCELL
Code RETAINED EARNINGS RETAINED EARNINGS RETAINED EARNINGS RETAINED EARNINGS RETAINED EARNINGS
2004 56344 #NA 20139 24631 -274524
2005 78767 -129732 124617 -15586 -160559
2006 100030 -112821 151357 18748 -2478722007 118690 -94146 172361 73965 -293819
2008 92753 -161589 210366 92111 -2755972009 41471 -138830 271881 16145 -254005
Z-SCORES Z-SCORES Z-SCORES Z-SCORES Z-SCORES
2004 1,871851024 #NA 4,830309746 2,05631149 6,727043635
2005 1,827810956 1,723966223 3,036916181 1,935020076 7,228991755
2006 1,808175727 1,76691693 2,894887896 2,673291793 5,862153973
2007 2,148492192 2,721806896 2,428903827 3,217463488 4,491562521
2008 1,876140615 1,837830672 2,524834584 3,454335858 4,541455827
2009 1,987455449 2,306346562 2,755170443 1,757559724 9,132601795
AVG. 1,92 2,07 3,08 2,52 6,33
-
7/30/2019 Bachelor Thesis Maurits Kruithof
28/32
28
Name CSM DRAKA HEIJMANS IMTECH LOGICA
Code WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL
2004 249800 346000 349146 309676 317100
2005 336200 243700 425707 232138 222900
2006 324400 172500 510292 196801 106600
2007 275300 294200 488581 138186 -78800
2008 359200 321600 341839 75663 2042002009 284000 211400 214946 116589 -92300
Name CSM DRAKA HEIJMANS IMTECH LOGICA
Code TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS
2004 2563100 1599600 1577522 952353 1160800
2005 2133700 1585100 1901870 1292144 1759300
2006 2167800 1692300 2124205 1563852 3420200
2007 2004700 1706200 2199655 1880725 32913002008 2043000 1599600 2355484 2453369 4086500
2009 1950700 1537400 1853407 2564411 3577300
Name CSM DRAKA HEIJMANS IMTECH LOGICA
Code SALES SALES SALES SALES SALES
2004 3516800 1420200 2509323 2098465 1706600
2005 3502200 1518600 2549323 2145265 1661500
2006 2677200 1792300 2742194 2066338 1746000
2007 2543400 2170400 2916317 2823980 2185500
2008 2423800 2756100 3278078 3056576 2825900
2009 2599300 2706800 3630990 3858635 3588000
Name CSM DRAKA HEIJMANS IMTECH LOGICA
Code EBIT EBIT EBIT EBIT EBIT
2004 233900 -5600 84026 70277 61900
2005 124600 49600 137887 81886 1266002006 104600 90200 126086 112213 159100
2007 69300 174100 99958 148357 117800
2008 109000 94100 42697 181817 84200
2009 141800 9200 -45961 200496 71800
Name CSM DRAKA HEIJMANS IMTECH LOGICA
Code EQUITY EQUITY EQUITY EQUITY EQUITY
2004 726400 479100 456643 323692 385300
2005 946400 360200 389152 288091 820100
2006 844900 424400 441843 328920 1525000
2007 957700 414800 462478 366691 15970002008 941600 440400 426483 395935 2041500
2009 997800 549500 425825 498053 1897200
Name CSM DRAKA HEIJMANS IMTECH LOGICACode TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT
2004 957200 443000 323885 98547 301100
2005 433600 448900 509865 58669 341400
2006 522100 535100 624237 105487 734400
2007 410100 611600 534031 215240 591900
2008 503900 588700 642673 546420 565000
2009 392400 368400 366448 529133 429900
Name CSM DRAKA HEIJMANS IMTECH LOGICA
Code RETAINED EARNINGS RETAINED EARNINGS RETAINED EARNINGS RETAINED EARNINGS RETAINED EARNINGS
2004 881600 29400 44945 185647 -401100
2005 920400 81700 266562 212185 -343200
2006 812400 104400 314874 252560 3146002007 863100 98800 336396 310111 282800
2008 899000 108800 286447 388691 3474002009 955700 84500 267434 489606 289600
Z-SCORES Z-SCORES Z-SCORES Z-SCORES Z-SCORES
2004 2,725685498 1,809596456 2,916269595 5,078666276 2,256536857
2005 3,935023069 1,798446494 2,501113028 5,259453059 2,501164394
2006 3,068178474 1,918482958 2,405998208 3,804756895 2,075586982
2007 3,550243425 2,302442892 2,474693578 3,101547138 2,491887836
2008 3,309484283 2,700737564 2,192671893 2,182754702 3,105768126
2009 3,957338609 2,915526917 2,913695466 2,647796536 3,798468976
AVG. 3,42 2,24 2,57 3,68 2,70
-
7/30/2019 Bachelor Thesis Maurits Kruithof
29/32
29
Name NUTRECO ORDINA OCE SMIT INT TEN CATE
Code WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL
2004 398879 50590 828908 41227 124500
2005 106700 34526 99726 28895 157700
2006 584300 -4817 401283 42019 149700
2007 262000 -14238 461968 66290 198800
2008 317100 -67067 349216 81481 2427002009 272900 -34477 245704 39200 148800
Name NUTRECO ORDINA OCE SMIT INT TEN CATE
Code TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS
2004 1759100 224376 2126487 407817 378500
2005 1737300 284429 2720860 516455 483700
2006 1753600 450891 2521421 577040 477200
2007 1957200 527198 2404230 756152 7083002008 2161300 453866 2442827 1186781 875000
2009 2106100 400669 2114444 1183100 728700
Name NUTRECO ORDINA OCE SMIT INT TEN CATE
Code SALES SALES SALES SALES SALES
2004 3674300 345398 2769300 355614 569600
2005 3787400 351572 2714463 339218 566900
2006 2944500 418859 2652853 315962 684700
2007 2874800 485471 2808575 421938 702000
2008 3296300 583920 3085423 520894 772200
2009 4943100 696473 2695660 704818 1032600
Name NUTRECO ORDINA OCE SMIT INT TEN CATE
Code EBIT EBIT EBIT EBIT EBIT
2004 115300 22466 114403 33468 33800
2005 125400 31626 114615 43022 391002006 140700 37974 110847 80135 49300
2007 161600 46066 135335 99374 69500
2008 177400 -94310 49783 119562 88000
2009 161300 -200 -8250 104600 29000
Name NUTRECO ORDINA OCE SMIT INT TEN CATE
Code EQUITY EQUITY EQUITY EQUITY EQUITY
2004 537781 128501 704068 203654 175900
2005 698200 152947 770830 247749 181800
2006 744100 194039 674514 288638 238700
2007 643400 254591 667133 364626 3101002008 655000 163280 635535 566988 366900
2009 730200 181100 534244 644100 380800
Name NUTRECO ORDINA OCE SMIT INT TEN CATECode TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT
2004 444400 79874 481251 67814 96200
2005 377200 60312 896328 103134 157300
2006 274000 85885 712746 108557 93900
2007 429500 97362 599869 175134 235200
2008 540900 110864 611101 334009 336500
2009 401000 114484 499598 170045 208300
Name NUTRECO ORDINA OCE SMIT INT TEN CATE
Code RETAINED EARNINGS RETAINED EARNINGS RETAINED EARNINGS RETAINED EARNINGS RETAINED EARNINGS
2004 297800 14882 339224 27520 45000
2005 318700 102682 78838 38280 118200
2006 506900 139585 283482 74969 1817002007 507100 174194 286989 105604 221300
2008 558900 81283 171710 107808 2659002009 598200 82982 70497 102384 298300
Z-SCORES Z-SCORES Z-SCORES Z-SCORES Z-SCORES
2004 3,538134544 3,196947875 3,047414925 3,15960035 3,456324272
2005 3,857195486 3,774395085 1,736202596 2,543291904 2,864390808
2006 4,376157333 2,982114689 2,112345454 2,869874728 4,20907302
2007 3,162017764 3,207387841 2,417742715 2,541051534 2,879209223
2008 3,05914616 1,556626759 2,222987811 1,999011627 2,626008252
2009 4,243134506 2,870711732 2,088457064 3,320508246 3,46197817
AVG. 3,71 2,93 2,27 2,74 3,25
-
7/30/2019 Bachelor Thesis Maurits Kruithof
30/32
30
Name USG PEOPLE VOPAK WAVIN WESSANEN
Code WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL WORKING CAPITAL
2004 63883 80400 67563 260700
2005 -116549 65600 213260 230000
2006 -87703 -27200 158163 269600
2007 101892 -54500 129370 248400
2008 23319 -128700 106034 2060002009 -138920 14900 128220 -37700
Name USG PEOPLE VOPAK WAVIN WESSANEN
Code TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS TOTAL ASSETS
2004 514297 1554600 802087 880500
2005 1972788 1720100 1593766 882600
2006 1847048 1799600 1452843 857700
2007 1918384 2116800 1482709 8243002008 1918660 2627700 1365965 806100
2009 1581980 3130400 1304460 633600
Name USG PEOPLE VOPAK WAVIN WESSANEN
Code SALES SALES SALES SALES
2004 1297800 749600 981621 2431800
2005 1269879 697400 1017522 2392900
2006 1317337 652300 1302781 1960300
2007 2460096 740200 1411141 1690200
2008 3651695 819000 1580519 1570400
2009 3815941 923500 1581200 752800
Name USG PEOPLE VOPAK WAVIN WESSANEN
Code EBIT EBIT EBIT EBIT
2004 28405 162400 77508 10900
2005 60866 181500 99178 418002006 198211 208900 155764 42400
2007 246492 288700 155065 61200
2008 101891 294900 79109 56100
2009 2807 391200 34988 -55900
Name USG PEOPLE VOPAK WAVIN WESSANEN
Code EQUITY EQUITY EQUITY EQUITY
2004 200057 525500 -51355 482800
2005 472209 584000 5620 484100
2006 574420 651500 295464 469700
2007 684684 790300 363196 4097002008 655061 913600 329015 363800
2009 638812 1231500 551653 149900
Name USG PEOPLE VOPAK WAVIN WESSANENCode TOTAL DEBT TOTAL DEBT TOTAL DEBT TOTAL DEBT
2004 157385 617000 568029 137000
2005 979205 588800 1082046 150100
2006 650739 543600 614800 163200
2007 624559 698300 561863 212300
2008 648311 1046000 509900 259400
2009 417372 1207100 295400 235300
Name USG PEOPLE VOPAK WAVIN WESSANEN
Code RETAINED EARNINGS RETAINED EARNINGS RETAINED EARNINGS RETAINED EARNINGS
2004 14784 289500 -77312 1800
2005 138616 326100 -5903 318300
2006 236867 418100 64423 3045002007 347708 552600 125954 309900
2008 332462 710400 133040 2634002009 302319 907500 134464 31400
Z-SCORES Z-SCORES Z-SCORES Z-SCORES
2004 3,655162891 1,660223694 1,453389711 5,272542504
2005 1,061686042 1,65953088 1,00165668 5,617488241
2006 1,718819071 1,771394156 1,730686961 5,047439494
2007 2,680359518 1,813029561 1,907380409 4,339267675
2008 2,940018074 1,525488633 1,963689835 3,781462475
2009 3,496073227 1,730808442 2,682200304 1,276012591
AVG. 2,59 1,69 1,79 4,22
-
7/30/2019 Bachelor Thesis Maurits Kruithof
31/32
31
Appendix 3 Sector-overview AEX- and AMX-index (Z-Scores)
Sector IT & TELECOM
2004 2005 2006 2007 2008 2009ASML 2,86 2,96 6,03 4,54 4,44 3,24
ASM International 2,06 1,94 2,67 3,22 3,45 1,76
Imtech 5,08 5,26 3,8 3,1 2,18 2,65
KPN 1,44 0,53 0,61 0,58 0,61 0,87
Logica 2,26 2,5 2,08 2,49 3,11 3,8
Ordina 3,2 3,77 2,98 3,21 1,56 2,87
Z-Scores 2,82 2,83 3,03 2,86 2,56 2,53 avg. 2,77
Sector FOOD&BEVERAGE
2004 2005 2006 2007 2008 2009
Ahold 2,64 2,56 3,03 3,50 3,95 2,87
CSM 2,73 3,94 3,07 3,55 3,31 3,96
Heineken 2,11 2,21 2,64 2,93 1,30 1,65
Nutreco 3,54 3,86 4,38 3,16 3,06 4,24
Unilever 1,97 2,22 2,67 2,84 2,90 2,97
Wessanen 5,27 5,62 5,05 4,34 3,78 1,28
Z-Scores 3,04 3,40 3,47 3,39 3,05 2,83 avg. 3,20
SectorCONSUMERDISCRETIONARY
2004 2005 2006 2007 2008 2009
Oc 3,05 1,74 2,11 2,42 2,22 2,09
Philips 4,30 4,47 5,51 6,27 4,20 3,96
Wolters Kluwer 1,17 1,25 1,19 1,56 1,26 1,21
Z-Scores 2,84 2,49 2,94 3,42 2,56 2,42 avg. 2,78
Sector INDUSTRIALS
2004 2005 2006 2007 2008 2009
Aalberts 1,87 1,83 1,81 2,15 1,88 1,99
Air-France KLM 1,55 1,04 1,50 1,63 1,78 0,95
Arcadis 4,83 3,04 2,89 2,43 2,52 2,76
BAM 3,26 1,91 1,71 1,82 2,01 1,83
Boskalis 17,21 7,28 6,64 6,58 2,93 11,20
Fugro 1,46 2,44 2,29 2,49 2,56 2,78Heijmans 2,92 2,5 2,41 2,47 2,19 2,91
-
7/30/2019 Bachelor Thesis Maurits Kruithof
32/32
Randstad 5,27 5,08 8,70 4,61 2,02 3,70
SBM 1,65 1,73 1,77 1,99 1,51 1,88
Smit 3,16 2,54 2,87 2,54 1,99 3,32
TNT 3,29 3,73 3,15 2,59 2,42 2,37
Unibail-Rodamco 1,42 2,34 2,65 1,62 0,93 0,78
USG people 3,65 1,06 1,72 2,68 2,94 3,5
Wavin 1,45 1,01 1,73 1,91 1,96 2,68
Wereldhave 1,66 1,49 2,85 2,47 2,01 1,87
Z-Scores 3,64 2,60 2,98 2,67 2,11 2,97 avg. 2,83
Sector MATERIALS
2004 2005 2006 2007 2008 2009
AkzoNobel 2,55 2,62 2,85 4,08 1,80 2,31
AMG #NA 1,72 1,77 2,72 1,84 2,31
Arcelor Mittal 3,96 2,06 1,82 2,19 2,35 2,82
Crucell 6,73 7,23 5,86 4,49 4,54 9,13
Draka 1,81 1,8 1,92 2,3 2,7 2,92
DSM 3,03 3,78 4,32 3,85 3,29 3,45
Ten Cate 3,46 2,86 4,21 2,88 2,62 3,46
Z-Scores 3,59 3,15 3,25 3,22 2,73 3,77 avg. 3,29
Sector Energy
2004 2005 2006 2007 2008 2009
Royal Dutch Shell 6,08 6,49 6,65 6,87 5,77 4,83
Vopak 1,66 1,66 1,77 1,81 1,53 1,73
Z-Scores 3,87 4,08 4,21 4,34 3,65 3,28 avg. 3,90