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Stochastic portfolio optimization: A regret-based approach on volatility risk measures: An empirical evidence from The New York stock market

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  • AmirMohammad Larni-Fooeik
  • Seyed Jafar Sadjadi
  • Emran Mohammadi

Abstract

Portfolio optimization involves finding the ideal combination of securities and shares to reduce risk and increase profit in an investment. To assess the impact of risk in portfolio optimization, we utilize a significant volatility risk measure series. Behavioral finance biases play a critical role in portfolio optimization and the efficient allocation of stocks. Regret, within the realm of behavioral finance, is the feeling of remorse that causes hesitation in making significant decisions and avoiding actions that could lead to poor investment choices. This behavior often leads investors to hold onto losing investments for extended periods, refusing to acknowledge mistakes and accept losses. Ironically, by evading regret, investors may miss out on potential opportunities. in this paper, our purpose is to compare investment scenarios in the decision-making process and calculate the amount of regret obtained in each scenario. To accomplish this, we consider volatility risk metrics and utilize stochastic optimization to identify the most suitable scenario that not only maximizes yield in the investment portfolio and minimizes risk, but also minimizes resulting regret. To convert each multi-objective model into a single objective, we employ the augmented epsilon constraint (AEC) method to establish the Pareto efficiency frontier. As a means of validating the solution of this method, we analyze data spanning 20, 50, and 100 weeks from 150 selected stocks in the New York market based on fundamental analysis. The results show that the selection of the mad risk measure in the time horizon of 100 weeks with a regret rate of 0.104 is the most appropriate research scenario. this article recommended that investors diversify their portfolios by investing in a variety of assets. This can help reduce risk and increase overall returns and improve financial literacy among investors.

Suggested Citation

  • AmirMohammad Larni-Fooeik & Seyed Jafar Sadjadi & Emran Mohammadi, 2024. "Stochastic portfolio optimization: A regret-based approach on volatility risk measures: An empirical evidence from The New York stock market," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-25, April.
    • Handle: RePEc:plo:pone00:0299699
    DOI: 10.1371/journal.pone.0299699
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    References listed on IDEAS

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    1. Markowitz, Harry M & Perold, Andre F, 1981. "Portfolio Analysis with Factors and Scenarios," Journal of Finance, American Finance Association, vol. 36(4), pages 871-877, September.
    2. Pejman Peykani & Emran Mohammadi & Armin Jabbarzadeh & Mohsen Rostamy-Malkhalifeh & Mir Saman Pishvaee, 2020. "A novel two-phase robust portfolio selection and optimization approach under uncertainty: A case study of Tehran stock exchange," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-43, October.
    3. Pejman Peykani & Mostafa Sargolzaei & Amir Takaloo & Shahla Valizadeh, 2023. "The Effects of Monetary Policy on Macroeconomic Variables through Credit and Balance Sheet Channels: A Dynamic Stochastic General Equilibrium Approach," Sustainability, MDPI, vol. 15(5), pages 1-21, March.
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