Providing the Optimal Model for Stock Selection Based on Momentum, Reverse and Hybrid Trading Strategies Using GWO Algorithm

Document Type : Research Paper

Authors

1 Ph.D. Candidate., Department of Finance, Faculty of Economics and Accounting, South Tehran Branch, Islamic Azad University, Tehran, Iran.

2 Associate Prof., Department of Accounting, South Tehran Branch, Islamic Azad University, Tehran, Iran.

3 Assistant Prof., Department of Accounting, South Tehran Branch, Islamic Azad University, Tehran, Iran.

Abstract

Objective: Contratum strategies are hybrid strategies in which, like the reverse strategy, the portfolio ranking is based on a long-term time horizon. However, the time horizon of their maintenance is like medium-term momentum strategies. Momentarian or momentary strategies are among the combined strategies whose portfolio ranking and stock selection are based on the medium-term time horizon, just like the momentum strategy; however, unlike reverse strategy, they are kept in long term. In this research, an attempt has been made to provide an optimal model for stock selection based on momentum, reverse, and hybrid trading strategies using the GWO algorithm and dynamic panel.
Methods: Considering the fact that the purpose of the current research is to answer the questions and test the existing theories in a specific field, it can be categorized as applied research (research and development), and due to its possibility of obtaining a descriptive explanation of a phenomenon, it can be considered as the pseudotype, experiential, descriptive and post-event. In addition, due to description, inference, and problem-solving using quantitative values, it is in the scope of quantitative research. To estimate the model, the information of 175 companies in the period from 2012 to 2021 was used, as well as the software E-Views 12 and MATLAB 2021. Based on the results of 8 time periods of 3, 6, 9, 12, 24, 36, 48, and 60 months, it was analyzed according to different momentum and reverse and combined strategies in loser, winner, and loser-winner, winner-loser positions. It should be noted that two methods of the dynamic panel and gray wolves were used to estimate the strategies.
Results: The existence of a momentum strategy was confirmed in the Tehran Stock Exchange, which ensures the hypothesis of underreaction in Iran's capital market. Investors in this market show less reaction to the change of the fundamentals affecting the stock price. Accordingly, the correction of the stock price of these companies is done slowly until it reaches its intrinsic value. Of course, factors such as the volatility limit, the base volume, and the trading node law also fuel this slowness and cause the price correction process to take place with a delay by the formation of buying and selling queues. This helps to use the momentum strategy and obtain abnormal returns in this market. As a result, it can be said that the momentum policy and, naturally, based on the research results, combined approaches cannot create abnormal profits for their investors in the capital market of Iran.
Conclusion: Financial market activists and investors are advised to use mixed strategies to improve their buying and selling decisions. Due to the improvement of the results of the gray wolf method compared to the simple panel method, they should use artificial intelligence methods instead of regression methods in the calculations and formation of the optimal portfolio.

Keywords

References

Abukari, K. & Otchere, I. (2020). Dominance of hybrid contratum strategies over momentum and contrarian strategies: half a century of evidence. Financial Markets and Portfolio Management, https://doi.org/10.1007/s11408-020-00363-3
Assadi, Gh.H., & Emami, S.A.H. (2019). Designing Trading Strategies Based on Momentum & Reversal Effect, Using Stock’s Major Historical Highs and Lows. Financial Knowledge of Securities Analysis, 12(41), 57-69. (in Persian)
Baltazer, M. & Jank, S. & Smajlbegovic, E. (2014). Who trades on momentum? Discussion Papers, 42/2014, Deutsche Bundesbank.
Balvers, R., Wu, Y. (2006). Momentum and mean reversion across national equity markets. Journal of Empirical Finance, 13, 24 – 48.
Barroso, P. & Santa-clara, P. (2015). Momentum has it’s a moments. Journal of Financial Economics, 116, 111-120.
Bekhradinasab, V., Zholanezhad, F. (2017). The Impact of Earning Quality on Excess Returns with Regard to Momentum. Financial Engineering and Portfolio Management, 8(32), 21-42. (in Persian)
Bell, D.E. (1982). Regret in decision making under uncertainty. Operations Research, 30(5), 961–981.
Cai, J., Wang, Y. & Mao, T. (2017). Tail subadditivity of distortion risk measures and multivariate tail distortion risk measures. Insurance: Mathematics and Economics, 75, 105–116.
Chou, P.H., Wei, K.C.J., Chung, H. (2007). Sources of contrarian profits in the Japanese stock market. Journal of Empirical Finance, 14, 261–286.
Choueifaty, Y. & Coignard Y. (2008). Toward maximum diversification. Journal of Portfolio Management, 35(1), 40-51.
Fadaie Nejad, M., Farahani, R. & Mhoseynabadi, M. (2021). Evaluation of the Profitability of Momentum and Reversal Strategies of Industry in the Capital Market of Iran. Journal of Asset Management and Financing, 9(1), 93-112. (in Persian)
Fama, E.F. & French, K.R. (1996). Multifactor explanations of asset pricing anomalies. Journal of Finance, 51(1), 55–84.
Fama, E.F. & French, K.R. (2008). Dissecting anomalies Journal of Finance, 63(4), 1653–1678.
Hajiannejad, A. & Salvati, A. (2018). Analysis of the effect of momentum on size in companies listed on the Tehran Stock Exchange. The fourth national conference on management, accounting and economics with an emphasis on regional and global marketing, Tehran. (in Persian)
Hon, M.T., Tonks, I. (2003). Momentum in the UK stock market. Journal of Multinational Financial Management, 13(1), 43–70.
Jegadeesh, N. & Titman, S. (2001). Profitability of momentum strategies: an evaluation of alternative explanations. Journal of Finance, 56(2), 699–720.
Jegadeesh, N., Titman, S. (1993).Returns to buying winners and selling losers: implications for market efficiency. Journal of Finance, 48(1), 65–91.
Khani, A., Botshekan, M., Athari, B. (2020). The Evaluation of the Managed Momentum Strategy in the Listed Companies on Tehran Stock Exchange. Financial Management Strategy, 8(4), 23-50. (in Persian)
Kim, D., Roh, T.-Y., Min, B.-K., & Byun, S.-J. (2014). Time-varying expected momentum profits. Journal of Banking & Finance, 49, 191-215.
Loomes, G. & Sugden, R.F. (1982). Regret theory: an alternative theory of rational choice under uncertainty’. Economic Journal, 92(368), 805–824.
Mazumdar, K.,‌ Zhang, D. & Guo, Y. (2020). Portfolio selection and unsystematic risk optimisation using swarm intelligence, Journal of Banking and Financial Technology, https://doi.org/10.1007/s42786-019-00013-x
Mirjalili, S. A., Mirjalili, S.M. & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software Elsevier, 69, 46-61.
Moreira, A. & Muir, T. (2017). Volatility-managed portfolios. Journal of Finance, 72, 1611–1644.
Morli, M.H. (2021). Analysis of the Long-term Relationship Between Macroeconomic Variables and the Chinese Stock Market Using Heteroscedastic Cointegration. Journal Managerial Finance, (11), 744-755.                  
Moskotoiz, D. (2021). Momentum profits and conditionaltime-varying systematic risk. Journal of International Financial Markets, Institutions & Money, 29, 242-255.
Mousavi Shiri, M., Salehi, M., Shakeri, M. A. (2015). Profitability of Momentum Strategies and the Impact of Trading Volume in Tehran Stock Exchange. Financial Engineering and Portfolio Management, 6(25), 107-124. (in Persian)
Saderbog, M., Jank, S., Smajlbegovic, E. (2019). Who trades on momentum? J. Financ. Mark., 42, 56–74
Safari, A. & Ashna, M. (2019). Proposing an optimal model for stock selection based on the momentum trading strategy. Financial Knowledge of Securities Analysis, 12(41), 143- 153. (in Persian)
Samavi, M.I., Aghakochki, M. & and Hosni, A. (1400). Investigating the preference of momentum and reverse strategies based on over-reaction and under-reaction of investors. The second international conference on new challenges and solutions in industrial engineering and management and accounting, Damghan. (in Persian)
Shourt C.A. (2003) Interpreting the macroeconomic time series facts: The effects of monetary policy. European economic review, 36(5), 975–1000.
Sias, R.W. (2004). Institutional herding. The Review of Financial Studies, 17(1), 165- 206.
Vayanos, D., Woolley, P. (2013). An institutional theory of momentum and reversal. The Review of Financial Studies, 26(5), 1087–1145.
World Bank. (2016). Market capitalization of listed domestic companies (current US$) (2016). http://data.world bank.org/indic ator/CM.MKT.LCAP.CD?view=chart. Accessed 6 Oct 2018
Financial Research Journal
Volume 24, Issue 4
2023
Pages 624-654

Files

Share

How to cite

Statistics