Measuring the relative performance of stock market using TOPSIS

Abstract: For many years, investors used some basic financial ratios to measure the relative performance of various active industries based on two-digit ISIC classification. However, direct implementation of basic financial figures may not be practical in today & apos; s business environment since investors face with different criteria. The proposed model of this paper uses Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to compare 37 various industries based on different financial figures. We gather the necessary data over the period of 2009-2010 from Tehran Stock Exchange and investigate the data in two stages. In the first stage, we perform fundamental analysis to select the most appropriate firms and the in the second stage, we use TOPSIS to rank selected firms based on different criteria. The results of the study confirm that information and communication technology, which is one of the biggest firms in this exchange is considered as the best option (relative ranking 0.88 in two years) followed by some Cement industry (with relative ranking of 0.26 in 2009 and 0.19 in 2010) and oil refinery units (with relative ranking of 0.23 in 2009 and 0.19 in 2010). The figure also shows that other firms maintain low ratios varied from 0.23 to 0.01. The lowest industry ranking belongs to marine industry.

How to cite this paper

 Danaei, A & Haghighi, M. (2013). Measuring the relative performance of stock market using TOPSIS.Management Science Letters , 3(1), 91-96.

Refrences

Aiello, G., Enea, M., Galante, G., & La Scalia, G. (2009). Clean agent selection approached by fuzzy TOPSIS decision-making method. Fire Technology, 45, 405–418.
Amiri, M. P. (2010). Project selection for oil-fields development by using the AHP and fuzzy TOPSIS methods. Expert Systems with Applications, 37, 6218–6224.
Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis, Management Science, 39, 1261-1264.
Athanasopoulos, G., Riba, C. R., & Athanasopoulou, C. (2009). A decision support system for coating selection based on fuzzy logic and multi-criteria decision making. Expert Systems with Applications, 36, 10848–10853.
Awasthi, A., Chauhan, S. S., & Omrani, H. (2011a). Application of fuzzy TOPSIS in evaluating sustainable transportation systems. Expert Systems with Applications, 38, 12270–12280.
Awasthi, A., Chauhan, S. S., Omrani, H., & Panahi, A. (2011b). A hybrid approach based on SERVQUAL and fuzzy TOPSIS for evaluating transportation service quality. Computers & Industrial Engineering, 61, 637–646.
Aydogan, E. K. (2011). Performance measurement model for Turkish aviation firms using the rough- AHP and TOPSIS methods under fuzzy environment. Expert Systems with Applications, 38, 3992– 3998.
Charnes A, Cooper, W. W., Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of the Operational Research, 2, 429–44.
Charnes A, Cooper W. W., Lewin, A., Seiford, L. M. (1994). Data envelopment analysis: theory, methodology and applications. Massachusetts: Kluwer Academic Publishers.
Chen, S. J., & Hwang, C. L. (1992). Fuzzy multiple attribute decision making: Methods and applications. Berlin: Springer-Verlag.
Chang, C. H., Lin, J. J., Lin, J. H., & Chiang, M. C. (2010). Domestic open-end equity mutual fund performance evaluation using extended TOPSIS method with different distance approaches.Expert Systems with Applications, 37, 4642–4649.
Chamodrakas, I., Alexopoulou, N., & Martakos, D. (2009). Customer evaluation for order acceptance using a novel class of fuzzy methods based on TOPSIS. Expert Systems with Applications, 36, 7409–7415.
Kaplan, R. S. & Norton, D. P. (1992). The balanced scorecard – measures that drive performance, Harvard Business Review, 70(1), 71-79.
Kaplan, R.S. & Norton, D.P. (1996). The balanced scorecard: translating strategy into action, Harvard Business School Press, Boston, MA.
Kelemenis, A., Ergazakis, K., & Askounis, D. (2011). Support managers’ selection using an extension of fuzzy TOPSIS. Expert Systems with Applications, 38, 2774–2782.
Krohling, R. A., & Campanharo, V. C. (2011). Fuzzy TOPSIS for group decision making: A case study for accidents with oil spill in the sea. Expert Systems with Applications, 38, 4190–4197.
Nazari, I., Vakil Alroaia, Y., & Bahraminasab, S. (2012). An application of multiple criteria decisionmaking techniques for ranking different national Iranian oil refining and distribution companies. Management Science Letters, 2(7), 2341-2346.
Orougi, S., Esmailzade Maghari, A., Mohammadi, H., Gol Mohammadi, A.M., & Irani, M. (2012).
An empirical investigation to use solar–geothermal hybrid energy system for small towns.
Management Science Letters, 2(7), 2287-2292.
Rostampour, S. (2012). An application of TOPSIS for ranking internet web browsers. Decision Science Letters, 1(2), 53-58.
Saaty, T. L. (1992). How to make a decision: the analytic hierarchy process. European Journal of Operational Research, 48, 9–26.
Sun, C. C., & Lin, G. T. R. (2009). Using fuzzy TOPSIS method for evaluating the competitive advantages of shopping websites. Expert Systems with Applications, 36, 11764–11771.
Yadollahi Farsi, J., Siahkali Moradi, J., & Jamali, B. (2012). Which product would be chosen? A fuzzy VIKOR method for evaluation and selection of products in terms of customers & apos; point of view; Case study: Iranian cell phone market. Decision Science Letters, 1(1), 23-32.
Yoon, K. P., & Hwang, C. L. (1995). Multiple attribute decision making. Thousand Oaks, CA: Sage Publication.

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