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

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. Table of contents. 1. Introduction.

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

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  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 Financial Analysis 4 To Compare the TOPSIS Result with the Financial Analysis Result 5 The Concluding Remarks 6

  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. 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.

  6. The Decision-Making Framework for a Stock Investment

  7. The Decision-Making Framework for a Stock Investment

  8. The Procedure of Comparing result of the two techniques

  9. 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)

  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. (1) where, i : a company index for i=1,2,…,l j : a year index for j=2001,2002, …,m k : a base factor index for1,2, …,n xijk : data of the kth factor for company i and period j

  11. Transform the Raw Data into the Normalized Data The raw data of the factors The normalized data of the factors

  12. Factor Analysis 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.

  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). (2) (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 for1,2, …,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

  14. Calculate the factor value using a principal component analysis The converted factor value 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 for1,2, …,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

  17. Specified Factor X2 Specified Factor X1 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. A* A1 A2 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 ithcompany

  19. Calculate a weighted-normalized value <A foundation factor value> <A weighted-normalized value>

  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

  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

  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

  26. Financial Analysis

  27. Financial Analysis results

  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.

  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.

  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 knowthe 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.

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