Portfolio model with a novel two-parameter coherent fuzzy number based on regret theory

Deng, Xue; Geng, Fengting

doi:10.1007/s00500-023-08978-0

Cited by 2 publications

(1 citation statement)

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“…Kagrecha et al [ 32 ] presented a stochastic multi-armed bandit setting in which constrained regret minimization over a given time frame is studied to solve the problem of constrained regret minimization. Deng and Geng [ 33 ] propose a novel and flexible two-parameter fuzzy number that he proposes which can be used by investors to capture their attitude toward the market (whether they are optimistic, pessimistic, or neutral). Khan et al [ 34 ] investigated the relationship between terrorism and stock market returns and volatility, specifically focusing on the context of Pakistan’s stock exchange.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Larni-Fooeik,

Sadjadi,

Mohammadi

2024

PLoS ONE

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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.

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Section: Literature Reviewmentioning

confidence: 99%