Gyutai Kim, Suhee Jung Department of Industrial Engineering, Chosun University, Gwangju, Korea

Powerpoint Templates


EXPLOITING THE DECISION-MAKING TECHNIQUE TO EXPLORE THE RELATIONSHIP BETWEEN THE

FANCIAL FACTORS AND THE STOCK PREFERECE

Gyutai Kim, Suhee JungDepartment of Industrial Engineering, Chosun University, Gwangju, Korea


Table of contents

Introduction1

The Decision-Making Framework for a Stock Investment2

Determine the Best Alternative Using the TOPSIS Technique3

Financial Analysis4

To Compare the TOPSIS Result with the Financial Analysis Result5

The Concluding Remarks6


Introduction

When investors make a decision which stocks to invest, they have to simultaneously take into consideration of a number of financial and nonfinancial factors affecting a stock price.

Suck an investment decision is to some extent extremely difficult to make.

In this paper, we employed the TOPSIS technique with which we considered only the financial factors due to the availability of obtaining relevant data.


A difference between TOPSIS and existing method◦ The existing method

· Consider only the financial ratios influencing the stock price.

◦ The TOPSIS method· Do grouping all the financial ratio using a factor analysis.

√ The financial ratios usually involve the subordinate relationship among them.

→ total rate of return = ratio of net income to net sales ⅹ total asset turnover ratio

We implemented a comparison analysis for the preference ordering determined by between the general four financial classifications and the TOPSIS.

Introduction


A Brief Liturature Survey T. C. Wang and J. C. Hsu, “Evaluating of the Business Operation Performance

of the Listing Company by Applying TOPSIS Method,” 2004 IEEE International Conference on System, Man and Cybernetics.

M. Guo and Y. B. Zhang, “A Stock Selection Model Based on Analytic Hierarchy Process, Factor Analysis and TOPSIS,” 2010 International Conference on Computer and Communication Technology in Agriculture Engineering.

T. C. Chu and C. T. Tsao, and Y. R. Shiue, “Application of Fuzzy Multiple Attribute Decision Making on Company Analysis for Stock Selection,” 1996 IEEE.

P. Xidonas and D. Askounis, “ Common Stock Portfolio Selection: A Multiple Criteria Decision Making Methodology and An Application to the Athens Stock Exchange,” Operations Research International Journal, Vol. 9, 2009, pp. 55-79.

I. Ertugrul and N. Karakasogu, “Performance Evaluation of Turkish Cement Firms with Fuzzy Analytic Hierarchy Process and TOPSIS Methods,” Expert Systems with Application, Vol. 36, 2009, pp. 702-715.


The Decision-Making Framework for a Stock Investment

Select the base factors and collect

data

- Financial data - Non-financial data

Normalize the values of the base

factors

- Minkowski metrics · Manhattan distance · Euclidean distance · Chebyshev distance

Factor analysis with the normalized

data

- Regroup the existing groups into the newly formed groups according to the result of the factor analysis

1,

/1

1

21

pxxd

pn

j

p

jjp


The Decision-Making Framework for a Stock Investment

Perform a regression analysis

- Group a benefit concept of the factors - Group a cost concept of the factors

Apply the TOPSIS technique

- Rank the preference ordering of the stocks based on the TOPSIS result.


The Procedure of Comparing result of the two techniques

Calculate the ranking of each ratio

- A 16 financial ratios

Determine the ranking of the financial

analysis

- Calculate the average of ranking of ratios

Compare the financial analysis result with

TOPSIS

- Perform the Spearman’s rank correlate analysis between each ratio and TOPSIS

Compare the ranking of each category with

TOPSIS

- Execute the Spearman’s rank correlate analysis between each category and TOPSIS

Calculate the ranking of each category

- Calculate the average of ranking of ratios by each category

Compare the ranking of each ratio with

TOPSIS

- In order to analyze in detail, execute the Spearman’s rank correlate analysis between each ratio and TOPSIS


The Selection of the Base Factors and Data Collection for the Alternative Analysis

Base factors

Data collection ◦ The financial statements of each company in eight years for the

communication and broadcasting equipment manufacturing companies (from 2001 to 2008)

Liquidity ratios Leverage ratios Activity ratios Profitability ratios Valuation ratios

- Current ratio - Acid-test ratio

- Debt ratio - Debt-to-equity ratio

- Total Asset Turnover ratio - Fixed Assets Turnover ratio - Inventory Turnover ratio

- Return on Total Asset - Return on Equity - Return on Net Income - Ratio of Ordinary profit - Ratio of Net Profit to Net Income

- Book-Value per Share - Price/Earnings Ratio - Earning per Share - Price on Book-Value ratio


Use the vectors normalization method with p=2 in the Minkowski’s lp metrics to transform the raw data into the normalized data to compare one with another alternative.

Transform the Raw Data into the Normalized Data

where, i : a company index for i=1,2,…,l j : a year index for j=2001,2002, …,m k : a base factor index for 1,2, …,n xijk : data of the kth factor for company i and period j

l

i

m

jijkijkijk xxr

1 2001

2/ (1)


Samsung Electronics

YearCurrent Ratio

Acid- Test Ratio

Debt Ratio

...Price/

Earnings Ratio

2008 1.517811 1.192102 0.24789 ... 13.88549

2007 1.538655 1.22966 0.265021 ... 12.73988

2006 1.527155 1.193011 0.279031 ... 13.17394

2005 1.711944 1.36331 0.27603 ... 14.73145

2004 1.605946 1.244249 0.272242 ... 7.496795

2003 1.466771 -1.19697 0.332792 ... 13.55107

2002 1.590061 -1.29062 0.36997 ... 7.918366

2001 1.069899 -0.75871 0.433694 ... 16.70282

Samsung Electronics

YearCurrent Ratio

Acid- Test Ratio

Debt Ratio

...Price/

Earnings Ratio

2008 0.048082 0.039903 0.005511 ... 0.01810

2007 0.048742 0.04116 0.005892 ... 0.01661

2006 0.048378 0.039933 0.006203 ... 0.01717

2005 0.054231 0.045634 0.006137 ... 0.01920

2004 0.050874 0.041648 0.006053 ... 0.00977

2003 0.046465 -0.04007 0.007399 ... 0.01766

2002 0.05037 -0.0432 0.008225 ... 0.01032

2001 0.033893 -0.0254 0.009642 ... 0.02177

The raw data of the factors The normalized data of the factors

Transform the Raw Data into the Normalized Data


The result of the factor analysis with eigenvector being more than “1”

- Those six factors were newly obtained from 16 independent variables based on the VARIMAX technique.

Factor Analysis

Total Explained Variance

Component

Initial Eigenvalue Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings

Total % Variance % Cumulative Value % Variance % Cumulative Value % Variance % Cumulative

1 4.335 27.091 27.091 4.335 27.091 27.091 3.672 22.953 22.9532 3.096 19.349 46.440 3.096 19.349 46.440 3.062 19.135 42.0883 2.118 13.239 59.680 2.118 13.239 59.680 2.150 13.440 55.5284 1.576 9.851 69.531 1.576 9.851 69.531 2.055 12.841 68.3695 1.061 6.632 76.163 1.061 6.632 76.163 1.189 7.434 75.8036 1.014 6.340 82.504 1.014 6.340 82.504 1.072 6.700 82.5047 .893 5.582 88.0868 .612 3.827 91.9139 .482 3.014 94.926

10 .307 1.918 96.84411 .271 1.693 98.53812 .117 .732 99.27013 .070 .440 99.70914 .028 .175 99.88415 .010 .065 99.94916 .008 .051 100.000

Extraction Method: Principal Component Analysis


where, i : a company index for i=1,2,…,l, j : a year index for j=2001,2002, …,m k : a base factor index for 1,2, …,n, Aijk : a variable for combining k factors Zijk : kth common factor for the ith company in the jth period Uij : a factor related to only the variable of xij

Wijk : a coefficient of the kth factor for the ith company in the jth period xijk : a normalized value of the kth factor for the ith company in the jth period

ijijkijkijijijijijk UZAZAZAx 2211 (2)

l

i

m

jijkijkijk xWF

1 2001(3)

Calculate the factor value using a principal component analysis

The values of factors were calculated in a linear combination on the basis of the responses of the variables observed. The values of factors which were not observed could be derived in a linear combination using Equation(2) and the values of the factor for a specific year of each company could be estimated with Equation (3).


The converted factor value

No.　　 Company Year Factor1 Factor2 Factor3 Factor4 Factor5 Factor6

1

Samsung Electronics

2008 -0.11362 0.24278 -0.17695 -0.38225 2.8002 0.16142

2 2007 -0.09888 0.34286 -0.16988 -0.3157 2.89493 0.22562

3 2006 -0.09584 0.39929 -0.19241 -0.26471 2.74001 0.27687

4 2005 -0.10015 0.43644 -0.15838 -0.22327 2.46718 0.3407

5 2004 -0.06387 0.60567 -0.16802 -0.20031 2.64874 0.24976

6 2003 -0.0853 0.37802 -0.52218 -0.16976 1.89792 0.09583

7 2002 -0.06383 0.4729 -0.49821 -0.16516 1.9433 -0.02273

8 2001 -0.09921 0.24594 -0.55912 -0.1616 1.039 0.01778

No.　 Company Factor1 Factor2 Factor3 Factor4 Factor5 Factor6

1Samsung

Electronics -0.09009 0.390488 -0.30564 -0.23535 2.30391 0.1681563

The arithmetic mean

Calculate the factor value using a principal component analysis


ijkkijijij FFFy 22110 (4)

where, i : a company index for i=1,2,…,l j : a year index for j=2001,2002, …,m k : a base factor index for 1,2, …,n k : a non-normalized value for the kth factor Fijk : a value of the kth factor for the ith company in the jth period

The Regression Analysis with the Values of the Factors

The main purpose of the work was to discriminate the factors into the group between a benefit concept and a cost concept.


A VARIANCE ANALYSIS FOR A REGRESSION TEST OF THE AUTOMOBILE PART MANUFACTURING INDUSTRY

Model Sum of SquaredDegrees of Freedom Mean Square F Sig.

1 Regression 0.733512 6 0.122252 61.40051 0.000 Residual 0.111499 56 0.001991Total 0.845011 62

a Predictors: (Constant), Factor6, Factor5, Factor4, Factor3, Factor2, Factor1

b Dependent Variable: Communication and broadcasting equipment manufacturing Stock Price

Coefficients(a)

ModelUnstandardized Coefficients

Standardized Coefficients

t Sig.B Std. Error Beta1 (constant) 0.049599048 0.005622 8.822702 0.000

Factor1 -0.001082429 0.005667 -0.009271801 -0.19101 0.849 Factor2 0.018949373 0.005667 0.162315285 3.343864 0.001 Factor3 -0.014160832 0.005667 -0.12129792 -2.49886 0.015 Factor4 -0.011119761 0.005667 -0.095248912 -1.96223 0.055 Factor5 0.105166183 0.005667 0.900825546 18.55795 0.000 Factor6 0.009299714 0.005667 0.079658874 1.641056 0.106

a Dependent Variable: Automobile Stock Price

The Regression Analysis with the Values of the Factors


Determine the Best Alternative Using the TOPSIS Technique

(Technique for Order Preference by Similarity to Ideal Solution)

Specified Factor X1

Specified Factor X2

A*

A2

A1

A-

A* : Positive ideal Solution A1 : Alternative plan 1A- : Negative ideal Solution A2 : Alternative plan 2

TOPSIS was developed under concept which the selected alternative is the nearest from the ideal solution and the farthest from the negative-ideal solution.

TOPSIS is the MADM method which select the alternative according to relative closeness to the ideal solution which considered simultaneously a distance about ideal solution and negative-ideal solution.


ijjij rwv (5)

where, i : a company index for i=1,2,…,l j : a year index for j=2001,2002, …,m vij : a normalized value of the jth factor for the i company wij : a value of the jth factor rij : a value of the jth factor of the ith company

Calculate a weighted-normalized value

It is necessary to convert the values of the factors into the product of the weight and the value.


　 Factor1 Factor2 Factor3 Factor4 Factor5 Factor6

Optimus -0.12646 -0.5561 0.154514 -0.24577 -0.399883 0.055055

DONGWON SYSTEMS -0.14434 0.176705 -0.43717 -0.08935 -0.453176 -0.013886

DAIDONG ELECTRONICS -0.05844 0.423976 1.882716 -0.25373 -0.183706 -0.302025

Kedcom -0.12749 -0.44611 -0.48579 -0.14472 -0.373801 -0.169644

LG Electronics 1.008873 0.258364 -0.44158 -0.11787 -0.040187 0.0205329

Huneed Technologies -0.21849 0.083301 -0.36473 0.045934 -0.416999 0.4592988

Samsung Electronics -0.09009 0.390488 -0.30564 -0.23535 2.30391 0.1681563

GS Instruments -0.11746 -0.29834 -0.05752 1.026124 -0.441183 -0.214925

Weight 3.027961 2.713943 2.585995 1.986546 1.8644621 1.0488159


Optimus -0.38291 -1.50922 0.399572 -0.48823 -0.745566 0.0577426

DONGWON SYSTEMS -0.43706 0.479567 -1.13053 -0.17751 -0.84493 -0.014564

DAIDONG ELECTRONICS -0.17695 1.150647 4.868694 -0.50404 -0.342513 -0.316769

Kedcom -0.38604 -1.2107 -1.25625 -0.2875 -0.696938 -0.177925

LG Electronics 3.054828 0.701186 -1.14192 -0.23416 -0.074927 0.0215352

Huneed Technologies -0.66158 0.226075 -0.94318 0.091249 -0.777478 0.4817198

Samsung Electronics -0.27278 1.059761 -0.79039 -0.46752 4.2955529 0.1763649

GS Instruments -0.35566 -0.80968 -0.14875 2.038442 -0.822568 -0.225417

<A foundation factor value>

<A weighted-normalized value>

Calculate a weighted-normalized value


miJkv

miJkvvvvvA

miJkv

miJkvvvvvA

iki

ikimk

iki

ikimk

,,2,1)(max

,,2,1)(min},,,,{

,,2,1)(min

,,2,1)(max},,,,{

2

121

2

1***2

*1

*

(6)

where, J1 : a benefit concept of the factors J2 : a cost concept of the factors A* : the ideal solution A- : the negative-ideal solution

Construct the Ideal and Negative-ideal Solution



A* -0.66158 1.150647 -1.25625 -0.50404 4.2955529 0.4817198

A- 3.054828 -1.50922 4.868694 2.038442 -0.84493 -0.316769

Construct the Ideal and Negative-ideal Solution


Calculate a separation measure

The separation of each company from the ideal and negative-ideal solutions.

l

ijiji

l

ijiji vvSvvS

1

2

1

2** )(,)( (7)

where, Si* : the separation measure from the ideal solution for the ith company

Si - the separation measure from the negative-ideal solution for the ith company


Calculate a separation measure

No. Company Si* No. Company Si

*

1 Optimus 0.423826 5 LG Electronics 0.641904

2 DONGWON SYSTEMS 0.567794 6 Huneed Technologies 0.545985

3DAIDONG

ELECTRONICS0.359001 7 Samsung Electronics 0.855031

4 Kedcom 0.530545 8 GS Instruments 0.441609


ii

ii

SS

SC

**

(8)

where, Ci* : the relative closeness of the ith company from the ideal solution

0 ≤ Ci* ≤ 1

if Ai = A-, Ci* = 0

if Ai = A*, Ci* = 1

Calculate the relative closeness the ideal solution


The analysis of the TOPSIS results

No. Company Ci* Ranking

1 Optimus 0.423826 7

2 DONGWON SYSTEMS 0.567794 3

3 DAIDONG ELECTRONICS 0.359001 8

4 Kedcom 0.530545 5

5 LG Electronics 0.641904 2

6 Huneed Technologies 0.545985 4

7 Samsung Electronics 0.855031 1

8 GS Instruments 0.441609 6


Financial Analysis

Basic classification

Definition Financial affair ratio

Stability analysis

-The measuring indices of the ability of repay the short-term debt

Current ratio, Quick ratio, Debt ratio, Equity ratio

Profitability analysis

-The evaluating indices of the ability of the produce profit.

Return on total assets, Return on equity,

Sales margin, Ordinary margin, Net profit margin

Activity analysis

-The measuring indices of the physical utilization of the total asset and inventory etc.

Total asset turnover, Inventory turnover, Fixed asset turnover

Market value analysis

-The ratios which are associated with the share price in stock market. -This ratios can measure the company performance because these reflect both the risk and rate of return.

Book value per share, Earnings per share, Price to equity ratio, Price to earnings ratio


Financial Analysis results

No. Company Ranking

1 Optimus 6

2 DONGWON SYSTEMS 4

3 DAIDONG ELECTRONICS 3

4 Kedcom 8

5 LG Electronics 2

6 Huneed Technologies 5

7 Samsung Electronics 1

8 GS Instruments 7


Compare the TOPSIS Result with the Financial Analysis Result

The investors evaluate the value of the companies from the financial statement to determine the best investment alternative.

The financial analysis is fundamental method which decides the best investment alternative, in the same way TOPSIS is one of the decision-making techniques selecting the best stock.

So, we will analyze that the financial analysis compare with the result of TOPSIS.


We execute the Spearman’s rank correlate analysis in order to evaluate measurably the relationship between financial analysis result and TOPSIS result.

The Spearman’s rank correlate analysis◦ The Spearman’s rank correlation coefficient is used to

analyze relationship between two continual variables, if they are the criterion of the rank.

◦ The Spearman’s rank correlate analysis coefficient can have the values from “-1” to “1”.

· If the value is “1”, it means that they have same order of ranking, on the other hand, the value is “-1”, it shows that they have completely reversed order.

Compare the TOPSIS Result with the Financial Analysis Result


A Comparisons of the Financial Result and TOPSIS Result

<The preference ordering of the TOPSIS and the financial analysis>


The Spearman’s rank correlate analysis between the result and TOPSIS result

◦ The correlate coefficient is “0.4” between financial analysis and TOPSIS.

◦ We can observe there are scarcely relation between financial analysis result and TOPSIS result.

Financial analysis

TOPSIS 0.4




< The preference ordering of the TOPSIS and stability analysis >



< The preference ordering of the TOPSIS and profitability analysis >



< The preference ordering of the TOPSIS and activity analysis >



< The preference ordering of the TOPSIS and market value analysis >


The Spearman’s rank correlate analysis 4 categories and TOPSIS

◦ In all categories, the correlation coefficients are under “0.5”.

◦ Consequently, all categories have little relation with TOPSIS.

Stabilityanalysis

Profitabilityanalysis

Activityanalysis

Market valueanalysis

TOPSIS -0.4 0.3 0.3 0.5



The concluding remarks

We present one unique method when choosing the best-investment-alternative, so called TOPSIS to make a determination of the order of priority between stocks.

Then, we compare the financial analysis result with TOPSIS result to figure out the relation between two.

As a result of correlation analysis, we know the financial analysis is low correlation with TOPSIS.

◦ It means the ranking of financial analysis is not equal to the ranking of TOPSIS, although we use the same base factors to determine the preference order in the stock market.



We can explain the differences between two methods through two.

◦ First, we can be explained depending on whether we conduct the factor analysis.· TOPSIS: we execute the factor analysis to reduce the

number of factors by grouping the factors which are same effect on stocks.

◦ Second, we can describe contingent upon whether to apply weight value in the stocks.· Financial analysis : the same weight in each factor. · TOPSIS : A different weight according to degree of effect

on stock price.



We regrettably failed to set up the benchmarking base to compare the TOPSIS result.

So, we need to find out the sound and acceptable benchmarking base which will be the following research.

Gyutai Kim, Suhee Jung Department of Industrial Engineering, Chosun University, Gwangju, Korea

Documents

Transcript of Gyutai Kim, Suhee Jung Department of Industrial Engineering, Chosun University, Gwangju, Korea