Stock Portfolios Optimization at the Industry Level Regarding Constraints in Practice: Liquidity, Transaction Cost, Turnover & Tracking-error

Document Type : Research Paper

Authors

1 MSc., Department of Humanity Sciences, Faculty of Economics, Khatam University, Tehran, Iran.

2 Assistant Prof., Department of Humanity Sciences, Faculty of Economics, Khatam University, Tehran, Iran.

Abstract

Objective: This study seeks to optimize stock portfolios at the industry level for an intended investment company by considering some limitations (the amount of liquidity of each industry in a month, transaction costs, portfolio turnover, and tracking error) in practice.
Methods: The research hypothesis was initially tested. In the first stage, the optimization was implemented without considering the restrictions. Then, the optimization was implemented by imposing all the constraints except the tracking error. In the third stage, the optimization was implemented by placing all the constraints.
Results: The obtained results proved portfolio optimization statistically significant and indicated that it had a higher Sharpe ratio than the construction of a random portfolio. The first step of this study showed that the intended company was far from the efficient frontier. Also, to maximize returns, minimize risks, and maximize the Sharpe ratio, the weights of the industries were needed to be changed (the weight of the sugar and pharmaceutical industries are recommended to be increased). The second phase approved that the company was still far from the efficient frontier and the efficient frontier had become smaller and moved downwards and to the right (the weight of the sugar and pharmaceutical industry are recommended to be increased more than the weight of others). The third step showed that the company was still far from the efficient frontier and the efficient frontier had become smaller and moved downwards and to the right (the weight of the metal and chemical industry are required to be higher than others).
Conclusion: Applying real-world constraints may end in different consequences.

Keywords


Akbari, F., Mahdavi, I., Ashna, M. (2012). Presenting a hybrid model using fuzzy network analysis process and fuzzy dimtel for optimal stock portfolio selection in the Iranian stock market. Regional Conference on New Topics in Accounting. (in Persian)
Alavi, Q., Baghbani, M., Gorgizadeh, M., Bahraini, V. (2014). Presenting a hybrid model for selecting a portfolio in the Tehran Stock Exchange using the multi-criteria decision-making technique. First National Conference on Accounting, Auditing and Management. (in Persian)
Asgharpor. H., Rezazade.A (2016). Determining the stock optimal portfolio using value at risk. Applied Theories of Economics, 3(4), 93-118. (in Persian)
Bayat, A., Abcher, B. (2015). The relationship between decision-making model and investors' expectations of risk and return on investment in financial instruments: The Markowitz model, Quarterly Journal of Investment Knowledge, 4(16), 173 – 190. (in Persian)
Bayat, A., Shokri, A. (2015). The process of selecting the optimal portfolio by the method of value at risk, Regional Conference on New Ideas in Accounting and Financial Management, Department of Accounting, Zanjan Branch, Islamic Azad University, Zanjan. (in Persian)
Bertrand, P. (2010). Another look at portfolio optimization under tracking error constraints, Financial Analysts Journal, 66, 3.
Charnes, A., Cooper, W. W. (1959). Chance-constrained programming. Management Science, 6(1):73-79.
Erica, E., Handari, B. & Hertono, C. (2018). Agglomerative clustering and genetic algorithm in portfolio optimization. AIP Conference Proceedings, 2023, 02017, https://doi.org/10.1063/1.506421.
Feiring, B. R., Lee, S. W. (1996). A chance-constrained approach to stock selection in Hong Kong. International Journal of Systems Science, 27(1), 33-41.
Grinold, R. C. and Kahn, R. N. (2000). Active portfolio management (2nd edition), McGraw-Hill.
Hu, J-L., Change, T –P., Chou, R. (2014). Market conditions and the effect of diversification on mutual fund performance: should fund be more concentrative under crisis? Journal of Productivity Analysis, 41(1), 141-151.                                                    
Huang, X. (2008). Risk curve and fuzzy portfolio selection. Computers & Mathematics with Applications, 55(6), 1102-1112.
Kandasamy, H. (2008). Portfolio selection under unequal prioritized downside risk. Advisor: Kostreva, Michael M., the Degree Doctor of Philosophy Mathematical Sciences, Department of Mathematical Science, Clemson University.
Kang, T., B. Wade B., and Brian D. A., (1996). A new efficiency criterion: The mean-separated target deviations risk model. Journal of Economics and Business, 48:47–66.
Kargar, M. (2011). Development and modification of Markowitz model to form an optimal portfolio according to the criterion of liquidity and its solution with genetic algorithm. Master Thesis. Faculty of Management, Azad University of Tehran, Markaz. (in Persian)
Khanjarpanah H., Pishvaee M. S., Jabbarzadeh A. (2017). Optimizing a flexible constrained portfolio in Stock Exchange with fuzzy programming, Journal of Operational Research and Its Applications, 13 (4), 39-54. (in Persian)
Kumar, C., Najmud, D. M. (2018). A novel framework for portfolio selection model using modified ANFIS and fuzzy sets. Journal of Computers, 185 (3), 453-485.
Liu, Y. J., Zhang, W. G., Zhang, Q. (2016). Credibilistic multi-period portfolio optimization model with bankruptcy control and affine recourse. Applied Soft Computing, 38: 890-906.
Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.
Markowitz, H. (1959). Efficient Diversification of Investment. John Wiley and sons, 12, 26-31.
Markowitz, H. (1991). Foundations of portfolio theory. Journal of Finance, 46(2):469-477.
Markowitz, H. M. (2000). Mean-variance analysis in portfolio choice and capital markets, Wiley.
Mir Abbasi, Y., Niko M., Saeedi, H., Haghshenas Farideh, A. (2018). Study of portfolio optimization based on downside risk, upside potential and behavioral variables efficiency, Quarterly Journal of Financial Engineering and Securities Management, 9(34), 305- 333. (in Persian)
Mushkhian, S., Najafi, A. A. (2015). Investment portfolio optimization using multipurpose particle swarm algorithm for a possible multi-period mean-half-variance-skew model, Journal of Financial Engineering and Securities Management, (23), 133- 142.
 (in Persian)
Najafi, A. A., Mushkhaneh‌, S.‌ (2014). Modeling and presenting the optimal solution for optimizing the investment portfolio for several periods with genetic algorithm. Journal of Financial Engineering and Securities Management, 5(21), 13- 33. (in Persian)
Neshatizadeh, L., Heidari, H. (2015). Studying of volatility and risk in portfolio-optimization model using of imperialist competitive algorithm, Journsl of Econometric Modeling, (4), 11- 35. (in Persian)
Olsson, R. (2005). Portfolio management under transaction costs: Model development and Swedish evidence. Master of Science, Umeå Studies in Business Administration No. 56 Umeå School of Business Umeå University.
Papahristodoulou, C., Dotzauer, E. (2004). Optimal portfolios using linear programming problems. Journal of the Operations Research Society, 55(11):1169- 1177.
Parker, J. (2001). Portfolio management (investment management). (Mohammad Shah Alizadeh, Trans.) (first edition), University Society Publications, Tehran. (in Persian)
Parsaiyan, N., Shams, S. (2012). Comparison of the performance of the Fama model and the French and artificial neural networks. Quarterly Journal of Financial Engineering and Securities Management, (11), 103-118. (in Persian)
Rahnamay Rood Poshti, F., Niko Maram, H., Toloui Ashlaghi, A., Hosseinzadeh Lotfi, F., Bayat, M. (2017). Evaluation of portfolio optimization efficiency using maximum stable Sharp ratio in comparison with Markowitz optimization. Journal of Management Perespective, 7(2), 125- 145. (in Persian)
Sadeghi, H., Dehghan, E. (2016). Application of Markowitz model in portfolio optimization, Fourth National Conference on Management, Economics and Accounting, Tabriz.
(in Persian)
Sharp, W. F., Alexander, G. J., Bailey, J. W. (2010). Investment management (Majid, Shariat ‌Panahi ‌and Abolfazl Jafari,‌ Trans.) Tehran, Etihad. (in Persian)
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425–442.
Silva, A., Neves, R., Horta, N. (2015). A hybrid approach to portfolio composition based on fundamental and technical indicators. Expert Systems with Applications, 42(4), 2036- 2048.
Soroosh, A., Atarchi, R., Ramtinnia, S. (2017). Portfolio optimization using teaching-learning based optimization algorithm (TLBO) in Tehran Stock Exchange. Journal of Financial Research, 19 (2), 263- 280. (in Persian)
Sortino, F., Meer R. V. d., Plantinga, A. (1999). The Dutch Triangle. Journal of Portfolio Management, 26(1), 50-58.
Speranza, M. G. (1995). A heuristics algorithm for a portfolio optimization model applied to the Milan stock market, Computer and Ops Res, 5, 433-441. (in Persian)
Taghavi Fard, M. T., Mansour, T., Khoshtinat, M. (2007). Presenting a metaheuristic algorithm for stock portfolio selection considering integer constraints. Quarterly Journal of Economic Research, 7(4), 49-69. (in Persian)
Talebania, Q., ‌Fathi, M. (2010). Comparison evaluation of selecting an optimal portfolio ‌in Tehran Stock Exchange by Markowitz model and Value at Risk, Financial Studies, 3(6), 71-94. (in Persian)
Tang, W., Han, Q., Li, G. (2001). The portfolio selection problems with chanceconstrained. Systems, Man, and Cybernetics IEEE International Conference, 4, 2674-2679.
Tehrani, R., Faleh Tafti, S., Asefi, S. (2018). Portfolio optimization krill herd metaheuristic algorithm considering different measures of risk in Tehran Stock Exchange, Financial Research Journal, 20 (4), 409-426. (in Persian)