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
Page 1
Table of contents
1
Introduction
3
The Decision-Making Framework
for a Stock Investment
Determine the Best Alternative Using the
TOPSIS Technique
4
Financial Analysis
5
To Compare the TOPSIS Result with
the Financial Analysis Result
6
The Concluding Remarks
2
Powerpoint Templates
Page 2
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.
Powerpoint Templates
Page 3
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.
Powerpoint Templates
Page 4
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.
Powerpoint Templates
Page 5
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
n


d p   x1j  x 2j

 j 1

1/ p
p




, p 1
Powerpoint Templates
Factor analysis
with the normalized
data
- Regroup the existing
groups into the newly
formed groups
according to the result
of the factor analysis
Page 6
The Decision-Making Framework for a
Stock Investment
Perform a regression
analysis
Apply the TOPSIS
technique
- Group a benefit
concept of the factors
- Group a cost concept
of the factors
- Rank the preference
ordering of the stocks
based on the TOPSIS
result.
Powerpoint Templates
Page 7
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 ranking
of each category with
TOPSIS
Calculate the ranking
of each category
- Execute the Spearman’s
rank correlate analysis
between each category
and TOPSIS
- Calculate the average of
ranking of ratios by each
category
Powerpoint Templates
Compare the financial
analysis result with
TOPSIS
- Perform the Spearman’s
rank correlate analysis
between each ratio and
TOPSIS
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 TOPSISPage 8
The Selection of the Base Factors and Data
Collection for the Alternative Analysis

Base factors
Liquidity ratios Leverage ratios
- Current ratio
- Acid-test ratio

- Debt ratio
- Debt-to-equity
ratio
Activity ratios
Profitability ratios
- Return on Total
- Total Asset
Asset
Turnover ratio
- Return on Equity
- Fixed Assets
- Return on Net
Turnover ratio
Income
- Inventory Turnover
- Ratio of Ordinary
ratio
profit
- Ratio of Net Profit to
Net Income
Valuation ratios
- Book-Value per
Share
- Price/Earnings Ratio
- Earning per Share
- Price on Book-Value
ratio
Data collection
◦
The financial statements of each company in eight years for the
communication and broadcasting equipment manufacturing companies
(from 2001 to 2008)
Powerpoint Templates
Page 9
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.
rijk  xijk /
l
m

2
xijk
i 1 j  2001
where,
(1)
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
Powerpoint Templates
Page 10
Transform the Raw Data into the
Normalized Data
...
Price/
Earnings
Ratio
Current
Year
Ratio
...
Price/
Earnings
Ratio
2008 1.517811 1.192102 0.24789
...
13.88549
2008 0.048082 0.039903 0.005511
...
0.01810
2007 1.538655 1.22966 0.265021
...
12.73988
2007 0.048742 0.04116 0.005892
...
0.01661
2006 1.527155 1.193011 0.279031
Samsung
Electronics 2005 1.711944 1.36331 0.27603
...
13.17394
...
0.01717
...
14.73145
2006 0.048378 0.039933 0.006203
Samsung
Electronics 2005 0.054231 0.045634 0.006137
...
0.01920
2004 1.605946 1.244249 0.272242
...
7.496795
2004 0.050874 0.041648 0.006053
...
0.00977
2003 1.466771 -1.19697 0.332792
...
13.55107
2003 0.046465 -0.04007 0.007399
...
0.01766
2002 1.590061 -1.29062 0.36997
...
7.918366
2002 0.05037 -0.0432 0.008225
...
0.01032
2001 1.069899 -0.75871 0.433694
...
16.70282
2001 0.033893 -0.0254 0.009642
...
0.02177
Current
Year
Ratio
AcidTest
Ratio
Debt
Ratio
The raw data of the factors
AcidTest
Ratio
Debt
Ratio
The normalized data of the factors
Powerpoint Templates
Page 11
Factor Analysis
The result of the factor analysis with eigenvector being more than “1”
Total Explained Variance
Initial Eigenvalue
Extraction Sums of Squared Loadings
Rotation Sums of Squared Loadings
Component
Total
% Variance
% Cumulative
1
4.335
27.091
27.091
2
3.096
19.349
46.440
3
2.118
13.239
59.680
4
1.576
9.851
69.531
5
1.061
6.632
76.163
6
1.014
6.340
82.504
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
100.000
Extraction Method: Principal Component Analysis
Value
% Variance
% Cumulative
Value
% Variance
% Cumulative
4.335
3.096
2.118
1.576
1.061
1.014
27.091
19.349
13.239
9.851
6.632
6.340
27.091
46.440
59.680
69.531
76.163
82.504
3.672
3.062
2.150
2.055
1.189
1.072
22.953
19.135
13.440
12.841
7.434
6.700
22.953
42.088
55.528
68.369
75.803
82.504
- Those six factors were newly obtained from 16 independent variables based on the VARIMAX technique.
Powerpoint Templates
Page 12
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).
xijk  Aij1Zij1  Aij 2 Zij 2    Aijk Zijk  Uij
Fijk 
l
(2)
m
 W
ijk xijk
(3)
i 1 j  2001
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
Powerpoint
Templates
xijk : a normalized
value of the kth factor
for the ith company in the jth period
where, i : a company index for i=1,2,…,l,
Page 13
Calculate the factor value using
a principal component analysis
No.
The converted
factor value
1
2
3
4
5
6
7
8
Company
Samsung
Electronics
Year
Factor1
Factor2
Factor3
Factor4
Factor5
Factor6
2008
2007
2006
2005
2004
2003
2002
2001
-0.11362
-0.09888
-0.09584
-0.10015
-0.06387
-0.0853
-0.06383
-0.09921
0.24278
0.34286
0.39929
0.43644
0.60567
0.37802
0.4729
0.24594
-0.17695
-0.16988
-0.19241
-0.15838
-0.16802
-0.52218
-0.49821
-0.55912
-0.38225
-0.3157
-0.26471
-0.22327
-0.20031
-0.16976
-0.16516
-0.1616
2.8002
2.89493
2.74001
2.46718
2.64874
1.89792
1.9433
1.039
0.16142
0.22562
0.27687
0.3407
0.24976
0.09583
-0.02273
0.01778
No.
Company
Factor1
Factor2
Factor3
Factor4
Factor5
Factor6
1
Samsung
Electronics
-0.09009
0.390488
-0.30564
-0.23535
2.30391
0.1681563
The arithmetic
mean
Powerpoint Templates
Page 14
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.
yij  0  1Fij1   2 Fij 2     k Fijk
where,
(4)
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
 : a non-normalized value for the kth factor
k
Fijk : a value of the kth factor for the ith company in the jth period
Powerpoint Templates
Page 15
The Regression Analysis with the Values
of the Factors
A VARIANCE ANALYSIS FOR A REGRESSION TEST
OF THE AUTOMOBILE PART MANUFACTURING INDUSTRY
Model
1
Sum of Squared
Degrees of
Freedom
Mean Square
F
Regression
0.733512
6
0.122252
61.40051
Residual
0.111499
56
0.001991
Total
0.845011
62
a Predictors: (Constant), Factor6, Factor5, Factor4, Factor3, Factor2, Factor1
b Dependent Variable: Communication and broadcasting equipment manufacturing Stock Price
Sig.
0.000
Coefficients(a)
Model
1
Standardized
Coefficients
Beta
t
Sig.
-0.009271801
0.162315285
-0.12129792
-0.095248912
0.900825546
0.079658874
8.822702
-0.19101
3.343864
-2.49886
-1.96223
18.55795
1.641056
0.000
0.849
0.001
0.015
0.055
0.000
0.106
Unstandardized Coefficients
B
Std. Error
(constant)
0.049599048
Factor1
-0.001082429
Factor2
0.018949373
Factor3
-0.014160832
Factor4
-0.011119761
Factor5
0.105166183
Factor6
0.009299714
a Dependent Variable: Automobile Stock Price
0.005622
0.005667
0.005667
0.005667
0.005667
0.005667
0.005667
Powerpoint Templates
Page 16
Determine the Best Alternative Using the
TOPSIS Technique
(Technique for Order Preference by Similarity to Ideal Solution)

A*
Specified
Factor X2
A1
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
Athe ideal solution which
Specified Factor X
considered simultaneously a
A* : Positive ideal Solution A1 : Alternative plan 1
distance about ideal solution and
A- : Negative ideal Solution A2 : Alternative plan 2
negative-ideal solution.
Powerpoint Templates
A2

1
Page 17
Calculate a weighted-normalized value

It is necessary to convert the values of the factors into the
product of the weight and the value.
vij  w j rij
where,
(5)
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
Powerpoint Templates
Page 18
Calculate a weighted-normalized value
<A foundation factor 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
Huneed Technologies
Samsung Electronics
1.008873
-0.21849
-0.09009
0.258364
0.083301
0.390488
-0.44158
-0.36473
-0.30564
-0.11787
0.045934
-0.23535
-0.040187
-0.416999
2.30391
0.0205329
0.4592988
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
<A weighted-normalized value>
Factor1
Factor2
Factor3
Factor4
Factor5
Factor6
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
-0.822568
-0.225417
Powerpoint
Templates
-0.80968
-0.14875
2.038442
Page 19
Construct the Ideal and Negative-ideal
Solution
(max i vik k  J1 ) i  1,2,, m
A


(mini vik k  J 2 ) i  1,2,, m 
(mini vik k  J1 ) i  1,2,, m 

 


A  {v1 , v2 ,, vk ,vm }  

(max
v
k

J
)
i

1
,
2
,

,
m


i ik
2
*
 {v1* , v2* ,, vk* ,vm* } 
where,
(6)
J1 : a benefit concept of the factors
J2 : a cost concept of the factors
A* : the ideal solution
A- : the negative-ideal solution
Powerpoint Templates
Page 20
Construct the Ideal and Negative-ideal
Solution
Factor1
Factor2
Factor3
Factor4
Factor5
Factor6
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
Powerpoint Templates
Page 21
Calculate a separation measure

The separation of each company from the ideal and
negative-ideal solutions.
Si* 
l
(v
ij
i 1
where,
 v*j ) 2 ,
Si 
l
(v
ij
i 1
 v j ) 2
(7)
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
Powerpoint Templates
Page 22
Calculate a separation measure
No.
Company
Si*
No.
Company
S i*
1
Optimus
0.423826
5
LG Electronics
0.641904
2
DONGWON SYSTEMS
0.567794
6
Huneed Technologies
0.545985
DAIDONG ELECTRONICS 0.359001
7
Samsung Electronics
0.855031
8
GS Instruments
0.441609
3
4
Kedcom
0.530545
Powerpoint Templates
Page 23
Calculate the relative closeness the ideal
solution

S
Ci*  * i 
Si  Si
where,
(8)
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
Powerpoint Templates
Page 24
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
GS Instruments
0.441609
Powerpoint Templates
6
8
Page 25
Financial Analysis
Basic
classification
Stability
analysis
Definition
-The measuring indices of the ability of repay the
short-term debt
Financial affair ratio
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
Powerpoint Templates
Page 26
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
Powerpoint Templates
Page 27
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.
Powerpoint Templates
Page 28
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.
Powerpoint Templates
Page 29
A Comparisons of the Financial Result
and TOPSIS Result
<The preference ordering of the TOPSIS and the financial analysis>
Powerpoint Templates
Page 30
A Comparisons of the Financial Result
and TOPSIS Result
 The Spearman’s rank correlate analysis between the
result and TOPSIS result
Financial analysis
TOPSIS
0.4
◦ 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.
Powerpoint Templates
Page 31
A Comparisons of the Financial Result
and TOPSIS Result
< The preference ordering of the TOPSIS and stability analysis >
Powerpoint Templates
Page 32
A Comparisons of the Financial Result
and TOPSIS Result
< The preference ordering of the TOPSIS and profitability analysis >
Powerpoint Templates
Page 33
A Comparisons of the Financial Result
and TOPSIS Result
< The preference ordering of the TOPSIS and activity analysis >
Powerpoint Templates
Page 34
A Comparisons of the Financial Result
and TOPSIS Result
< The preference ordering of the TOPSIS and market value analysis >
Powerpoint Templates
Page 35
A Comparisons of the Financial Result
and TOPSIS Result
 The Spearman’s rank correlate analysis 4 categories and
TOPSIS
TOPSIS
Stability
analysis
Profitability
analysis
Activity
analysis
Market value
analysis
-0.4
0.3
0.3
0.5
◦ In all categories, the correlation coefficients are under
“0.5”.
◦ Consequently, all categories have little relation with
TOPSIS.
Powerpoint Templates
Page 36
The concluding remarks
 We present one unique method when choosing the bestinvestment-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. Powerpoint Templates
Page 37
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.
Powerpoint Templates
Page 38
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.
Powerpoint Templates
Page 39