1. EXPLOITING THE DECISION-MAKING TECHNIQUE TO EXPLORE THE RELATIONSHIP BETWEEN THE FANCIAL FACTORS AND THE STOCK PREFERECE
Gyutai Kim, Suhee Jung
Department of Industrial Engineering,
Chosun University, Gwangju, Korea
Powerpoint Templates

2. Table of contents 1 Introduction The Decision-Making Framework
for a Stock Investment 2
Determine the Best Alternative Using the
TOPSIS Technique 3 4
Financial Analysis
To Compare the TOPSIS Result with
the Financial Analysis Result 5 6
The Concluding Remarks

3. 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.

4. Introduction 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 ⅹtotal asset turnover ratio
We implemented a comparison analysis for the preference ordering determined by between the general four financial classifications and the TOPSIS.

5. 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
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

6. 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

7. 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.

8. 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

9. The Selection of the Base Factors and Data Collection for the Alternative Analysis
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
- Inventory Turnover
- 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

10. Transform the Raw Data into the Normalized Data
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.
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
(1)

11. Transform the Raw Data into the Normalized Data
Samsung Electronics
Year
Current Ratio
Acid- Test Ratio
Debt Ratio
...
Price/
Earnings Ratio
2008
2007
2006
2005
2004
2003
2002
2001
Samsung Electronics
Year
Current Ratio
Acid- Test Ratio
Debt Ratio
...
Price/
Earnings Ratio
2008
2007
2006
2005
2004
2003
2002
2001
The raw data of the factors
The normalized data of the factors

12. Total Explained Variance
Factor Analysis
The result of the factor analysis with eigenvector being more than “1”
Total Explained Variance
Component
Initial Eigenvalue
Extraction Sums of Squared Loadings
Rotation Sums of Squared Loadings
Total
% Variance
% Cumulative
Value 1
4.335
27.091
3.672
22.953 2
3.096
19.349
46.440
3.062
19.135
42.088 3
2.118
13.239
59.680
2.150
13.440
55.528 4
1.576
9.851
69.531
2.055
12.841
68.369 5
1.061
6.632
76.163
1.189
7.434
75.803 6
1.014
6.340
82.504
1.072
6.700 7
.893
5.582
88.086 8
.612
3.827
91.913 9
.482
3.014
94.926 10
.307
1.918
96.844 11
.271
1.693
98.538 12
.117
.732
99.270 13
.070
.440
99.709 14
.028
.175
99.884 15
.010
.065
99.949 16
.008
.051
Extraction Method: Principal Component Analysis
Those six factors were newly obtained from 16 independent variables based on the VARIMAX technique.

13. 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).
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
(2)
(3)

14. Calculate the factor value using a principal component analysis
No.　
　Company
Year
Factor1
Factor2
Factor3
Factor4
Factor5
Factor6 1
Samsung Electronics
2008
2.8002 2
2007 3
2006 4
2005
0.3407 5
2004 6
2003 7
2002
0.4729
1.9433 8
2001
1.039
The converted factor value
No.　
Company
Factor1
Factor2
Factor3
Factor4
Factor5
Factor6 1
Samsung Electronics
The arithmetic mean

15. 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.
(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

16. The Regression Analysis with the Values of the Factors
A VARIANCE ANALYSIS FOR A REGRESSION TEST
OF THE AUTOMOBILE PART MANUFACTURING INDUSTRY
Model
Sum of Squared
Degrees of Freedom
Mean Square F
Sig. 1
Regression 6
0.000
Residual 56
Total 62
a Predictors: (Constant), Factor6, Factor5, Factor4, Factor3, Factor2, Factor1
b Dependent Variable: Communication and broadcasting equipment manufacturing Stock Price
Coefficients(a)
Model
Unstandardized Coefficients
Standardized Coefficients t
Sig. B
Std. Error
Beta 1
(constant)
0.000
Factor1
0.849
Factor2
0.001
Factor3
0.015
Factor4
0.055
Factor5
Factor6
0.106
a Dependent Variable: Automobile Stock Price

17. Determine the Best Alternative Using the TOPSIS Technique
(Technique for Order Preference by Similarity to Ideal Solution)
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.
Specified Factor X1
Specified Factor X2 A* A2 A1 A-
A* : Positive ideal Solution A1 : Alternative plan 1
A- : Negative ideal Solution A2 : Alternative plan 2

18. Calculate a weighted-normalized value
It is necessary to convert the values of the factors into the product of the weight and the value.
(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

19. Calculate a weighted-normalized value
<A foundation factor value>
Factor1
Factor2
Factor3
Factor4
Factor5
Factor6
Optimus
DONGWON SYSTEMS
DAIDONG ELECTRONICS
Kedcom
LG Electronics
Huneed Technologies
Samsung Electronics
GS Instruments
Weight
<A weighted-normalized value>
Factor1
Factor2
Factor3
Factor4
Factor5
Factor6
Optimus
DONGWON SYSTEMS
DAIDONG ELECTRONICS
Kedcom
LG Electronics
Huneed Technologies
Samsung Electronics
GS Instruments

20. Construct the Ideal and Negative-ideal Solution
(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

21. Construct the Ideal and Negative-ideal Solution
Factor1
Factor2
Factor3
Factor4
Factor5
Factor6 A* A-

22. Calculate a separation measure
The separation of each company from the ideal and negative-ideal solutions.
(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

23. Calculate a separation measure
No.
Company
Si* 1
Optimus 5
LG Electronics 2
DONGWON SYSTEMS 6
Huneed Technologies 3
DAIDONG ELECTRONICS 7
Samsung Electronics 4
Kedcom 8
GS Instruments

24. Calculate the relative closeness the ideal solution
(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

25. The analysis of the TOPSIS results
No.
Company
Ci*
Ranking 1
Optimus 7 2
DONGWON SYSTEMS 3
DAIDONG ELECTRONICS 8 4
Kedcom 5
LG Electronics 6
Huneed Technologies
Samsung Electronics
GS Instruments

26. 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
-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
-The measuring indices of the physical utilization of
the total asset and inventory etc.
Total asset turnover,
Inventory turnover,
Fixed asset turnover
Market value
-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

27. Financial Analysis results
No.
Company
Ranking 1
Optimus 6 2
DONGWON SYSTEMS 4 3
DAIDONG ELECTRONICS
Kedcom 8 5
LG Electronics
Huneed Technologies 7
Samsung Electronics
GS Instruments

28. 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.

29. Compare the TOPSIS Result with the Financial Analysis Result
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.

30. A Comparisons of the Financial Result and TOPSIS Result
<The preference ordering of the TOPSIS and the financial analysis>

31. A Comparisons of the Financial Result and TOPSIS Result
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

32. A Comparisons of the Financial Result and TOPSIS Result
< The preference ordering of the TOPSIS and stability analysis >

33. A Comparisons of the Financial Result and TOPSIS Result
< The preference ordering of the TOPSIS and profitability analysis >

34. A Comparisons of the Financial Result and TOPSIS Result
< The preference ordering of the TOPSIS and activity analysis >

35. A Comparisons of the Financial Result and TOPSIS Result
< The preference ordering of the TOPSIS and market value analysis >

36. A Comparisons of the Financial Result and TOPSIS Result
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.
Stability
analysis
Profitability
Activity
Market value
TOPSIS
-0.4
0.3
0.5

37. 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.

38. The concluding remarks
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.

39. The concluding remarks
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.