Evaluating the Performance of Contrarian Portfolios During the Covid-19 Pandemic: Applied Study in the Iraq Stock Exchange

Luay Ali Mahmood; Hashim Jabbar Hussein; Mohammed Faez Hasan

Luay Ali Mahmood Financial and Banking Sciences Dept., Tikrit University, Iraq
Hashim Jabbar Hussein Financial and Banking Sciences Dept., University of Kerbala, Iraq
Mohammed Faez Hasan Financial and Banking Sciences Dept., University of Kerbala, Iraq

Keywords: Contrarian Portfolios, Covid-19, Iraqi Stock Exchange, Performance

Abstract

The current study aims to evaluate the performance of the inverse portfolio during the Covid-19 pandemic, through a comparative study of the effectiveness of strategies with and without taking into account the transaction cost in the Iraq Stock Exchange for the period from 2020 to 2023. The effects confirmed that the triumphing portfolio throughout the pandemic period, while considering the value of the transaction, accomplished a superb fee on the alpha scale in 31 out of 36 strategies, so it was cited that the portfolio outperformed the general market index. In evaluation, the values have been negative in 35 strategies before the pandemic, highlighting the effectiveness of the strategies at some stage in the excellent instances imposed by using the pandemic, and their low effectiveness in normal instances. The effects additionally confirmed that the winning portfolio via the pandemic, without or with the expenses of transactions, executed wonderful values inside the Information Ratio (IR) metric in 31 out of 36 approach types. These consequences mirror the excellent performance of investment managers and their efficiency in delivering returns in excess of the benchmark, reinforcing the potential of active management to add value in turbulent financial conditions. These findings highlight the significance of adapting funding techniques to converting market and economic situations, specifically in times of crisis. This study confirms that the investment strategies analyzed had been greater effective in the course of the Covid-19 pandemic, which calls for a focused attempt to enhance those techniques and adapt them to destiny conditions to ensure ultimate overall performance always.

References

Abd Halim, N. N., Jusoh, N., Elias, S. M., & Kamil, K. H. (2019). Performance of Contrarian Investing for Shariah Compliant Stock Portfolio. Malaysian Journal of Science, Health & Technology, 3.‏
Acma, M. Q. (2014). Comparative study on performance evaluation of mutual fund schemes in Bangladesh: An analysis of monthly returns. Journal of Business Studies Quarterly, 5(4), 190-209.
AL Abdullah, M. J. M., Alyaseen, A. A. A. M., & Faez Hasan, M. (2023). Role of Company’s Efficiency Measure in achieving return: Iraq’s Private Banks Case. Technium Social Sciences Journal, 39, 377–392. https://doi.org/10.47577/tssj.v39i1.8233
Ansari, S. P. M. A., Odongo, J. O., Nomani, M. Z. M., Salahuddin, G., Hasan, M. F., & Pallathadka, L. K. (2022). Evaluating the role of environment management system based on artificial intelligence. Materials Today: Proceedings, 56, 2240–2244. https://doi.org/10.1016/j.matpr.2021.11.571
Arora, G. D., Hasan, M. F., Sidhu, K. S., Tripathi, V., Misra, D., & Kumar, T. V. (2022). Method of Predicting of Trend in the Stock Exchange using ML and DL Algorithms. Proceedings of 5th International Conference on Contemporary Computing and Informatics, IC3I 2022, 89–95. https://doi.org/10.1109/IC3I56241.2022.10073453
Bodie, Z., Kane, A., & Marcus, A. J. (2021). Essentials of Investments (12th ed.). McGraw–Hill Companies, New York, N.Y.
Brentani, C. (2004). Portfolio Management in Practice (1st ed.). Butterworth-Heinemann, MA.
Chincarini, L. B., & Kim, D. (2007). Another Look at the Information Ratio. Journal of Asset Management, 8, 284-295.‏
Christopherson, J. A., Carino, D. R., & Ferson, W. E. (2009). Portfolio Performance Measurement and Benchmarking (1st ed.). Mcgraw-Hill/ Irwin, Inc.
Damodaran, A. (2012). Value investing: investing for grown ups?. Available at SSRN 2042657.‏
Gupta, J., Pattnaik, M., Makhija, P., Auguskani, P. L., Hasan, M. F., & Jabeen, P. S. (2022). Prediction of Stock Price Fluctuations Based on Artificial Intelligence System. Proceedings of 5th International Conference on Contemporary Computing and Informatics, IC3I 2022, 2126–2130. https://doi.org/10.1109/IC3I56241.2022.10072407
Jones, C. P. (2013). Investment Analysis and Management (12th ed.). John Wiley & Sons, Inc.
Kantos, C., & DiBartolomeo, D. (2020). How the pandemic taught us to turn smart beta into real alpha. Journal of Asset Management, 21(7), 581-590.‏
Liu, Y. S., & Lee, L. (2022). Are Modifications in the ETF's Investment Performance and Risks during the COVID-19 Pandemic Event?. Review of Applied Socio-Economic Research, 23(1), 5-17.
Marios, L. (2014). Evaluation of Mutual Funds Performance Using Multiple Measures. Master of Science (MSc) in Financial Analysis for Executives, University of Piraeus Department of Banking and Financial Management.
McDowell, B. A. (2008). The Art of Trading: Combining the Science of Technical Analysis with The Art of Reality – Based Trading (1st ed.). John Wiley and Sons, Inc.
Nanda, S. K., Krishna, S. H., Ram, S. T., Jena, S. K., Hasan, M. F., & Durga, S. (2024). Smart Finance: Evaluating AI and Machine Learning’s Impact on Investment Strategies and Financial Management. 2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies, 1–5. https://doi.org/10.1109/TQCEBT59414.2024.10545191
Petersen, R. & Arnstedt, P. (2010). Contrarian Investment Strategies: An Assessment of the Value Premium in context to Recessions. Copenhagen Business School.
Reilly, F. K., Brown, K. C., & Leeds, S. J. (2019). Investment Analysis & Portfolio Management (11th ed.). Cengage Learning, Inc.
Sharma, S. (2018). Sharpe Ratio and Information Ratio: Do They Really Help Investment Decisions?. International Research Journal of Management Sociology & Humanities, 9(7), 91-99. https://doi.org/10.32804/IRJMSH
Vysniauskas, P., & Rutkauskas, A. V. (2014). Performance evaluation of investment (mutual) funds. Business: Theory and Practice, 15(4), 398-407.
Zhang, W., & Li, Y. (2023). Liquidity risk and expected cryptocurrency returns. International Journal of Finance & Economics, 28(1), 472-492.‏
Zhang, Z., & Khushi, M. (2020, July). GA-MSSR: Genetic algorithm maximizing sharpe and sterling ratio method for robotrading. In 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.