The stock market is considered as one of the most vital components of a free-market economy. The investments of the stock market are associated with a greater amount of risk. Due to the uncertainty associated with stocks, there is a great need to understand the trend in rising and fall of stocks corresponding to a particular sector or an industry to invest profitably. In view of this, authors have taken up a study to identify the best sector in BSE SENSEX for investments. Fuzzy Analytical Hierarchy Process is used to evaluate and study the dominance of various sectors including Automobile, Finance, Information technology, Oil, Pharmaceuticals, and Power. Four crucial derivatives criteria’s Return on equity, Book value per share, Price-earnings ratio, Price to book ratio are considered to study the dominance of each sector. The results of this study help in prioritizing the sectors for future investments.
Fuzzy analytical hierarchy process Sectors Satty scale Performance index of sectors BSE SENSEX
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P. Satya would like to thank DST-FIST vide SR/FST/MSI-090/2013(C) for providing infrastructure support and UGC for providing financial support as JRF (Ref. No: 22/12/2013(ii)EU-V with Sr. No. 2121341012).
Menon, S.: A comparative study of the Indian stock market with TWO international stock markets between 2012 and 17. 5(3), 14 (2018)Google Scholar
Poshakwale, S.: Evidence on weak form efficiency and day of the week effect in the Indian stock market, 12 (1996)Google Scholar
Modigliani, F., Miller, M.H.: The cost of capital, corporation finance and the theory of investment. Am. Econ. Rev. 48(3), 261–297 (1958)zbMATHGoogle Scholar
Cleary, R.J.: Applied data mining: statistical methods for business and industry. J. Am. Stat. Assoc. 101(475), 1317–1318 (2006)CrossRefGoogle Scholar
Hadi, A.R.A., et al.: The effect of oil price fluctuations on the malaysian and indonesian stock markets, 23 (2009)Google Scholar
Pettitt, A.N.: Testing the normality of several independent samples using the Anderson-Darling statistic. Appl. Stat. 26(2), 156 (1977)CrossRefGoogle Scholar
Peter, D.: Easton: PE ratios, PEG ratios, and estimating the implied expected rate of return on equity capital. Account. Rev. 79(1), 73–95 (2004)CrossRefGoogle Scholar
Campbell, J., Hentschel, L.: No News is Good News: an Asymmetric Model of Changing Volatility in Stock Returns. National Bureau of Economic Research, Cambridge, MA (1991)CrossRefGoogle Scholar
Agarwal, S.: Scale efficiency with fuzzy data. Int. J. Bus. Syst. Res. 11(1–2), 152–162 (2017)CrossRefGoogle Scholar
Erensal, Y.C., et al.: Determining key capabilities in technology management using fuzzy analytic hierarchy process: a case study of Turkey. Inf. Sci. 176(18), 2755–2770 (2006)CrossRefGoogle Scholar
Jha, S.K., Puppala, H.: Prospects of renewable energy sources in India: prioritization of alternative sources in terms of energy index. Energy 127, 116–127 (2017)CrossRefGoogle Scholar
Kahraman, C. (ed.): Fuzzy Multi-criteria Decision Making: theory and Applications with Recent Developments. Springer, New York, NY (2008)zbMATHGoogle Scholar
Chatzimouratidis, A.I., Pilavachi, P.A.: Multicriteria evaluation of power plants impact on the living standard using the analytic hierarchy process. Energy Policy 36(3), 1074–1089 (2008)CrossRefGoogle Scholar
Dağdeviren, M., Yüksel, İ.: Developing a fuzzy analytic hierarchy process (AHP) model for behavior-based safety management. Inf. Sci. 178(6), 1717–1733 (2008)CrossRefGoogle Scholar
Kalban, M.M.: Decision support for energy conservation promotion: an analytic hierarchy process approach. Fuel Energy Abstr. 45(5), 365 (2004)Google Scholar
Saaty, T.L.: Decision making with the analytic hierarchy process. Int. J. Serv. Sci. 1(1), 16 (2008)CrossRefGoogle Scholar
Ertuğrul, İ., Karakaşoğlu, N.: Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods. Expert Syst. Appl. 36(1), 702–715 (2009)CrossRefGoogle Scholar
Jain, V., et al.: Supplier selection using fuzzy AHP and TOPSIS: a case study in the Indian automotive industry. Neural Comput. Appl. 29(7), 555–564 (2018)CrossRefGoogle Scholar
Seçme, N.Y., et al.: Fuzzy performance evaluation in turkish banking sector using analytic hierarchy process and TOPSIS. Expert Syst. Appl. 36(9), 11699–11709 (2009)CrossRefGoogle Scholar