An Integrated Framework for Classification and Selection of Stocks for Portfolio Construction: Evidence from NSE, India

  • Sayan Gupta Department of Management Studies, NIT, Durgapur, India
  • Gautam Bandyopadhyay Department of Management Studies, NIT, Durgapur, India
  • Sanjib Biswas Department of Management Studies, NIT, Durgapur, India
  • Arup Mitra Department of Management Studies, MAKAUT, Haringhata, India
Keywords: Efficient stock portfolios (ESP), DP omnibus test, TOPSIS, Bayes Model


Investment extortion in the stock market is a crucial aspect considered by the investors. Therefore, investors implemented different strategies. This study was intended at constructing an investment portfolio (IP) of stocks within the NSE 100 listed companies of Non-parametric nature, fulfilling the basic premise of portfolio making that is, reducing risks while yielding an attractive return higher than any other instrument for the investors. Using DP omnibus test, the desired sample of companies following the non-normal distribution was achieved. Using financial beta, we have selected the outcome based on the nature of their ‘return’ and ‘risk'. We introduce TOPSIS (Technique for order of performance by similarity to ideal solution), a multi-criteria decision-making process (MCDM) to study the profitability of stocks, rank wise for each year, and finally, the Bayes portfolio model help to select the overall profitability associate with low risk for the construction of the portfolio.


Download data is not yet available.

Author Biography

Gautam Bandyopadhyay, Department of Management Studies, NIT, Durgapur, India


Alali, F., & Tolga, A. C. (2019). Portfolio allocation with the TODIM method. Expert Systems with Applications, 124, 341-348.

Ampomah, E. K., Nyame, G., Qin, Z., Addo, P. C., Gyamfi, E. O., & Gyan, M. (2021). Stock Market Prediction with Gaussian Naïve Bayes Machine Learning Algorithm. Informatica, 45(2), 243-256.

Aouni, B., Doumpos, M., Pérez-Gladish, B., & Steuer, R. E. (2018). On the increasing importance of multiple criteria decision aid methods for portfolio selection. Journal of the Operational Research Society, 69(10), 1525-1542.

Atukalp, M. E. (2021). Determining the relationship between stock return and financial performance: an analysis on Turkish deposit banks. Journal of Applied Statistics, 48(13-15), 2643-2657.

Avramov, D. (2002). Stock return predictability and model uncertainty. Journal of Financial Economics, 64(3), 423-458. DOI:

Banz, R. W. (1981). The relationship between return and market value of common stocks. Journal of Financial Economics, 9(1), 3-18. DOI:

Baser, P., & Saini, J. R. (2015). Agent based stock clustering for efficient portfolio management. International Journal of Computer Applications, 116, 35–41. DOI:

Basu, S. (1983). The relationship between earnings yield, market value, and return for NYSE common stocks: further evidence. Journal of Financial Economics, 12(1), 129-156. DOI:

Bayramoglu, M. F., & Hamzacebi, C. (2016). Stock selection based on fundamental analysis approach by grey relational analysis: a case of Turkey. International Journal of Economics and Finance, 8(7), 178-184. DOI:

Bhandari, L. C. (1988). Debt/equity ratio and expected common stock returns: Empirical evidence. Journal of Finance, 43(2), 507-528. doi:10.1111/j.1540-6261.1988.tb03952.x DOI:

Biswas, S., Bandyopadhyay, G., Guha, B., & Bhattacharjee, M. (2019). An ensemble approach for portfolio selection in a multi-criteria decision making framework. Decision Making: Applications in Management and Engineering, 2(2), 138-158.

Biswas, S. (2020). Measuring performance of healthcare supply chains in India: A comparative analysis of multi-criteria decision making methods. Decision Making: Applications in Management and Engineering, 3(2), 162-189. DOI:

Biswas, S., & Anand, O. P. (2020). Logistics Competitiveness Index-Based Comparison of BRICS and G7 Countries: An Integrated PSI-PIV Approach. IUP Journal of Supply Chain Management, 17(2), 32-57.

Biswas, S., Majumder, S., & Dawn, S. K. (2021). Comparing the Socioeconomic Development of G7 and BRICS Countries and Resilience to COVID-19: An Entropy–MARCOS Framework. Business Perspectives and Research, 22785337211015406.

Biswas, S., Majumder, S., Pamucar, D., & Dawn, S. K. (2021a). An Extended LBWA Framework in Picture Fuzzy Environment Using Actual Score Measures Application in Social Enterprise Systems. International Journal of Enterprise Information Systems (IJEIS), 17(4), 37-68.

Black, F. (1993). Beta and return. Journal of Portfolio Management, 20(1), 8-18. doi:10.3905/jpm.1993.409462 DOI:

Brida, J. G., & Risso, W. A. (2010). Hierarchical structure of the German stock market. Expert Systems with Applications, 37, 3846–3852. DOI:

Cabrera, G., Coronado, S., Rojas, O., & Romero-Meza, R. (2018). A Bayesian approach to model changes in volatility in the Mexican stock exchange index. Applied Economics, 50(15), 1716-1724. DOI:

Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52(1), 57-82. DOI:

Chan, L. K., Hamao, Y., & Lakonishok, J. (1991). Fundamentals and stock returns in Japan. Journal of Finance, 46(5), 1739-1789. doi:10.2307/2328571 DOI:

Cheng, K. C., Huang, M. J., Fu, C. K., Wang, K. H., Wang, H. M., & Lin, L. H. (2021). Establishing a Multiple-Criteria Decision-Making Model for Stock Investment Decisions Using Data Mining Techniques. Sustainability, 13(6), 3100.

Chong, J., & Phillips, G. M. (2012). Low-(economic) volatility investing. The Journal of Wealth Management, 15, 75–85. DOI:

Cooper, M. J., Gutierrez, R. C., & Hameed, A. (2004). Market states and momentum. The Journal of Finance, 59, 1345–1365. DOI:

Da Costa Jr, N., Cunha, J., & Da Silva, S. (2005). Stock selection based on cluster analysis. Economics Bulletin, 13, 1–9.

Dehdasht, G., Ferwati, M. S., Zin, R. M., & Abidin, N. Z. (2020). A hybrid approach using entropy and TOPSIS to select key drivers for a successful and sustainable lean construction implementation. PloS one, 15(2), e0228746.

De Rossi, G., Kolodziej, J., & Brar, G. (2020). A recommender system for active stock selection. Computational Management Science, 17(4), 517-547.

Dincer, H., & Hacioglu, U. (2015). A comparative performance evaluation on bipolar risks in emerging capital markets using fuzzy AHP-TOPSIS and VIKOR approaches. Engineering Economics/Inžinerinė ekonomika, 26(2), 118-129. DOI:

Dose, C., & Cincotti, S. (2005). Clustering of financial time series with application to index and enhanced index tracking portfolio. Physica A: Statistical Mechanics and its Applications, 355, 145–151. DOI:

Fama, E. F. (1970). Efficient capital markets a review of theory and empirical work. The Journal of Finance, 25(2), 383-417. DOI:

Fama, E. F., & French, K. R. (1992). The cross-section of expected stock returns. The Journal of Finance, 47, 427–465. DOI:

Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56. DOI:

Fama, E. F., & French, K. R. (2017). International tests of a five-factor asset pricing model. Journal of Financial Economics, 123, 441–463. DOI:

Fama, E. F., & French, K. R. (2018). Choosing factors. Journal of Financial Economics, 128, 234–252. DOI:

Ghosh, S. (2021, January). Application of a New Hybrid MCDM Technique Combining Grey Relational Analysis with AHP-TOPSIS in Ranking of Stocks in the Indian IT Sector. In International Conference on Computational Intelligence in Communications and Business Analytics (pp. 133-149). Springer, Cham.

Goodwin, T. H. (1998). The information ratio. Financial Analysts Journal, 54(4), 34-43. DOI:

Graham, B., Dodd, D. L. F., & Cottle, S. (1934). Security analysis (Vol. 452). New York: McGraw-Hill.

Grinblatt, M., Titman, S., & Wermers, R. (1995). Momentum investment strategies, portfolio performance, and herding: A study of mutual fund behavior. The Amer- ican Economic Review, 85, 1088–1105

Guha, B., Dutta, A., & Bandyopadhyay, G. (2016). Measurement of risk vs return of Indian sectoral indices. Journal of Advanced Management Science, 4(2), 106-111. DOI:

Gupta, S., Mathew, M., Syal, G., & Jain, J. (2021). A hybrid MCDM approach for evaluating the financial performance of public sector banks in India. International Journal of Business Excellence, 24(4), 481-501.

Gupta, S., Bandyopadhyay, G., Bhattacharjee, M., & Biswas, S. (2019a). Portfolio Selection using DEA-COPRAS at risk–return interface based on NSE (India). International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(10), 4078-4086.

Gupta, S., Bandyopadhyay, G., Biswas, S., & Upadhyay, A. (2019b). A hybrid machine learning and dynamic nonlinear framework for determination of optimum portfolio structure. In Innovations in Computer Science and Engineering (pp. 437-448). Springer, Singapore.

Hassanzadeh, M. R., & Valmohammadi, C. (2021). Evaluation and ranking of the banks and financial institutes using fuzzy AHP and TOPSIS techniques. International Journal of Operational Research, 40(3), 297-317.

Hatami-Marbini, A., & Kangi, F. (2017). An extension of fuzzy TOPSIS for a group decision making with an application to Tehran stock exchange. Applied Soft Computing, 52, 1084-1097. DOI:

Hoseini Ebrahimabad, S. A., Heidari, H., Jahangiri, K., & Ghaemi Asl, M. (2019). Using Bayesian Approach to Study the Time Varying Correlation among Selected Indices of Tehran Stock Exchange. Financial Research Journal, 21(1), 59-78.

Hsu, J., & Li, F. (2013). Low-volatility investing. Journal of Index Investing, 4, 67–72. DOI:

Huang, Z., Heian, J. B., & Zhang, T. (2011). Differences of opinion, overconfidence, and the high-volume premium. Journal of Financial Research, 34, 1–25. DOI:

Hurson, C., & Zopounidis, C. (1997). On the use of multicriteria decision aid methods to portfolio selection. In Multicriteria analysis (pp. 496-507). Springer, Berlin, Heidelberg. DOI:

Hwang, C. L., & Yoon, K. P. (1981). Multiple attribute decision making: Methods and applications. New York: Springer-Verlag. DOI:

Iorio, C., Frasso, G., Dambrosio, A., & Siciliano, R. (2018). A p-spline based clustering approach for portfolio selection. Expert Systmes with Applications, 95, 88–103. DOI:

Jammalamadaka, S. R., Qiu, J., & Ning, N. (2019). Predicting a stock portfolio with the multivariate Bayesian structural time series model: do news or emotions matter?. International Journal of Artificial Intelligence, 17(2), 81-104.

Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance, 48(1), 65-91. doi:10.1111/j.1540-6261.1993.tb04702.x DOI:

Jensen, M. C. (1968). The performance of mutual funds in the period 1945-1964. The Journal of finance, 23(2), 389-416. DOI:

Karmakar, P., Dutta, P., & Biswas, S. (2018). Assessment of mutual fund performance using distance based multi-criteria decision making techniques-An Indian perspective. Research Bulletin, 44(1), 17-38.

Laha, S., & Biswas, S. (2019). A hybrid unsupervised learning and multi-criteria decision making approach for performance evaluation of Indian banks. Accounting, 5(4), 169-184.

Lintner, J. (1965). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. Review of Economics and Statistics, 47(1), 13-37. DOI:

Makui, A., & Mohammadi, E. (2019). A MCDM-based approach using UTA-STRAR method to discover behavioral aspects in stock selection problem. International Journal of Industrial Engineering & Production Research, 30(1), 93-103.

Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7, 77–91 DOI:

Mashayekhi, Z., & Omrani, H. (2016). An integrated multi-objective Markowitz–DEA cross-efficiency model with fuzzy returns for portfolio selection problem. Applied Soft Computing, 38, 1-9. DOI:

Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica, 34(4), 768-783. doi: 10.2307/1910098 DOI:

Mukhametzyanov, I., & Pamucar, D. (2018). A sensitivity analysis in MCDM problems: A statistical approach. Decision making: applications in management and engineering, 1(2), 51-80. DOI:

Nanda, S., Mahanty, B., & Tiwari, M. (2010). Clustering Indian stock mar- ket data for portfolio management. Expert Systems with Applications, 37, 8793–8798. DOI:

Narang, M., Joshi, M. C., & Pal, A. K. (2021). A hybrid fuzzy COPRAS-base-criterion method for multi-criteria decision making. Soft Computing, 25(13), 8391-8399.

Nguyen, P. H., Tsai, J. F., Hu, Y. C., & Ajay Kumar, G. V. (2022). A Hybrid Method of MCDM for Evaluating Financial Performance of Vietnamese Commercial Banks Under COVID-19 Impacts. In Shifting Economic, Financial and Banking Paradigm (pp. 23-45). Springer, Cham.

Pamučar, D. S., Božanić, D., & Ranđelović, A. (2017). Multi-criteria decision making: An example of sensitivity analysis. Serbian journal of management, 12(1), 1-27. DOI:

Pamučar, D., Žižović, M., Biswas, S., & Božanić, D. (2021). A new logarithm methodology of additive weights (lmaw) for multi-criteria decision-making: application in logistics. Facta Universitatis, Series: Mechanical Engineering. 19(3), 361-380.

Pätäri, E., Karell, V., Luukka, P., & Yeomans, J. S. (2018). Comparison of the multicriteria decision-making methods for equity portfolio selection: The US evidence. European Journal of Operational Research, 265(2), 655-672. DOI:

Peachavanish, R. (2016). Stock selection and trading based on cluster analysis of trend and momentum indicators. In Proceedings of the international multicon-ference of engineers and computer scientists (pp. 317–321).

Peng, H. G., Xiao, Z., Wang, J. Q., & Li, J. (2021). Stock selection multicriteria decision‐making method based on elimination and choice translating reality I with Z‐numbers. International Journal of Intelligent Systems, 36(11), 6440-6470.

Pearson, E. S., D ‘‘'AGOSTINO, R. B., & Bowman, K. O. (1977). Tests for departure from normality: Comparison of powers. Biometrika, 64(2), 231-246. DOI:

Platanakis, E., Sutcliffe, C., & Ye, X. (2021). Horses for courses: Mean-variance for asset allocation and 1/N for stock selection. European Journal of Operational Research, 288(1), 302-317.

Poklepović, T., & Babić, Z. (2014). Stock selection using a hybrid MCDM approach. Croatian Operational Research Review, 5(3), 273-290. DOI:

Pramanik, P. K. D., Biswas, S., Pal, S., Marinković, D., & Choudhury, P. (2021). A Comparative Analysis of Multi-Criteria Decision-Making Methods for Resource Selection in Mobile Crowd Computing. Symmetry, 13(9), 1713.

Reinganum, M. R. (1981). Misspesification of capital asset pricing: empirical anomalies based on earnings yield and market values. Journal of Financial Economics, 9(1), 19-46. DOI:

Ren, F., Lu, Y. N., Li, S. P., Jiang, X. F., Zhong, L. X., & Qiu, T. (2017). Dynamic portfolio strategy using clustering approach. Plos One, 12, e0169299. DOI:

Rosenberg, B., Reid, K., & Lanstein, R. (1985). Persuasive evidence of market inefficiency. Journal of Portfolio Management, 11(3), 9-16. doi:10.3905/jpm.1985.409007 DOI:

Sahu, R., Dash, S. R., & Das, S. (2021). Career selection of students using hybridized distance measure based on picture fuzzy set and rough set theory. Decision Making: Applications in Management and Engineering, 4(1), 104-126. DOI:

Stattman, D. (1980). Book values and stock returns. The Chicago MBA: A Journal of Selected Papers, 4(1), 25-45

How to Cite
Sayan Gupta, Bandyopadhyay, G., Sanjib Biswas, & Arup Mitra. (2022). An Integrated Framework for Classification and Selection of Stocks for Portfolio Construction: Evidence from NSE, India. Decision Making: Applications in Management and Engineering.
Regular articles