Modeling Portfolio Optimization based on behavioral Preferences and Investor’s Memory
ObjectiveThe optimization of asset portfolios, taking into account market advancements, has emerged as a pivotal subject in financial economics. Constructing asset portfolios is acknowledged as a critical decision for investors. Consequently, researchers focus on identifying factors that influence t...
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University of Tehran
2024-03-01
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| Series: | تحقیقات مالی |
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| Online Access: | https://jfr.ut.ac.ir/article_96669_fa9915fe77f6e69fa04239a81a130af5.pdf |
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| author | vahideh mousavi kakhki Sanaz Khatabi |
| author_facet | vahideh mousavi kakhki Sanaz Khatabi |
| author_sort | vahideh mousavi kakhki |
| collection | DOAJ |
| description | ObjectiveThe optimization of asset portfolios, taking into account market advancements, has emerged as a pivotal subject in financial economics. Constructing asset portfolios is acknowledged as a critical decision for investors. Consequently, researchers focus on identifying factors that influence the selection of portfolios with high returns and controlled risk. Portfolio optimization, a cornerstone of financial economics, has gained prominence in the face of ever-evolving market dynamics. Investors' decision-making plays a pivotal role in portfolio construction, prompting researchers to explore factors that influence the selection of portfolios with high returns and controlled risk. Numerous models have addressed the optimization problem of stock portfolio management, each tailored to specific conditions and constraints. This research focuses on developing a multi-objective optimization model that incorporates investor memory and behavioral preferences. The literature on portfolio optimization is vast and diverse, encompassing various approaches and methodologies. Traditional optimization models, such as Markowitz's Mean-Variance model, aim to maximize expected returns while minimizing risk. However, these models often fail to capture the complexities of real-world investment decisions, which are often influenced by behavioral factors. Investor memory refers to the tendency of investors to base their current investment decisions on past experiences. This can lead to biases and suboptimal outcomes. Behavioral preferences, on the other hand, encompass a range of psychological factors that influence investor behavior, such as risk aversion, overconfidence, and herding. MethodsWe perceive stock portfolio optimization as a multi-objective challenge, considering two primary criteria. The first criterion involves investor memory, which encompasses utilizing historical price data and market trends to anticipate future performance. The second criterion pertains to behavioral preferences, which involves integrating investor risk aversion, overconfidence, and herding behavior into the model. We employ a Genetic Algorithm (GA) to optimize portfolios under both criteria. GA is a robust optimization technique that can effectively handle complex problems with multiple constraints. The study population comprises companies listed on the Tehran Stock Exchange for the year 2021. ResultsThe achieved results suggest that investor memory serves as a more suitable criterion for optimal portfolio construction compared to behavioral preferences because investor memory incorporates market data and trends, providing a more objective basis for decision-making. Additionally, incorporating market return alongside investor memory data yielded superior results than using behavioral preferences with market return. This indicates that the combination of investor memory and market data can lead to more efficient and profitable portfolios. Pairwise comparisons of portfolios created using investor memory and behavioral preference criteria revealed that investor memory consistently outperformed behavioral preferences across different risk levels. This finding highlights the importance of considering investor memory when constructing optimal stock portfolios. ConclusionThis study contributes to the literature on portfolio optimization by demonstrating the effectiveness of incorporating investor memory and behavioral preferences into the decision-making process. The findings suggest that investor memory is a more suitable criterion for portfolio optimization than behavioral preferences. Moreover, the combination of investor memory and market return data can lead to more efficient and profitable portfolios. The findings of this study have important implications for investors and portfolio managers. Investors should consider incorporating investor memory into their decision-making process when constructing stock portfolios. Additionally, portfolio managers can use the proposed multi-objective optimization model to create more efficient and profitable portfolios for their clients. This study provides a foundation for future research on portfolio optimization. Future studies can explore other factors that influence investor behavior, such as social media sentiment and news sentiment. Additionally, researchers can investigate the application of other optimization techniques, such as machine learning algorithms, to portfolio optimization. |
| format | Article |
| id | doaj-art-f01400098be949d693163f8faf785c8d |
| institution | Kabale University |
| issn | 1024-8153 2423-5377 |
| language | fas |
| publishDate | 2024-03-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | تحقیقات مالی |
| spelling | doaj-art-f01400098be949d693163f8faf785c8d2025-02-11T14:02:14ZfasUniversity of Tehranتحقیقات مالی1024-81532423-53772024-03-0126114017010.22059/frj.2023.354113.100743896669Modeling Portfolio Optimization based on behavioral Preferences and Investor’s Memoryvahideh mousavi kakhki0Sanaz Khatabi1PhD Candidate, Department of Financial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.Assistant Prof., Department of Financial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.ObjectiveThe optimization of asset portfolios, taking into account market advancements, has emerged as a pivotal subject in financial economics. Constructing asset portfolios is acknowledged as a critical decision for investors. Consequently, researchers focus on identifying factors that influence the selection of portfolios with high returns and controlled risk. Portfolio optimization, a cornerstone of financial economics, has gained prominence in the face of ever-evolving market dynamics. Investors' decision-making plays a pivotal role in portfolio construction, prompting researchers to explore factors that influence the selection of portfolios with high returns and controlled risk. Numerous models have addressed the optimization problem of stock portfolio management, each tailored to specific conditions and constraints. This research focuses on developing a multi-objective optimization model that incorporates investor memory and behavioral preferences. The literature on portfolio optimization is vast and diverse, encompassing various approaches and methodologies. Traditional optimization models, such as Markowitz's Mean-Variance model, aim to maximize expected returns while minimizing risk. However, these models often fail to capture the complexities of real-world investment decisions, which are often influenced by behavioral factors. Investor memory refers to the tendency of investors to base their current investment decisions on past experiences. This can lead to biases and suboptimal outcomes. Behavioral preferences, on the other hand, encompass a range of psychological factors that influence investor behavior, such as risk aversion, overconfidence, and herding. MethodsWe perceive stock portfolio optimization as a multi-objective challenge, considering two primary criteria. The first criterion involves investor memory, which encompasses utilizing historical price data and market trends to anticipate future performance. The second criterion pertains to behavioral preferences, which involves integrating investor risk aversion, overconfidence, and herding behavior into the model. We employ a Genetic Algorithm (GA) to optimize portfolios under both criteria. GA is a robust optimization technique that can effectively handle complex problems with multiple constraints. The study population comprises companies listed on the Tehran Stock Exchange for the year 2021. ResultsThe achieved results suggest that investor memory serves as a more suitable criterion for optimal portfolio construction compared to behavioral preferences because investor memory incorporates market data and trends, providing a more objective basis for decision-making. Additionally, incorporating market return alongside investor memory data yielded superior results than using behavioral preferences with market return. This indicates that the combination of investor memory and market data can lead to more efficient and profitable portfolios. Pairwise comparisons of portfolios created using investor memory and behavioral preference criteria revealed that investor memory consistently outperformed behavioral preferences across different risk levels. This finding highlights the importance of considering investor memory when constructing optimal stock portfolios. ConclusionThis study contributes to the literature on portfolio optimization by demonstrating the effectiveness of incorporating investor memory and behavioral preferences into the decision-making process. The findings suggest that investor memory is a more suitable criterion for portfolio optimization than behavioral preferences. Moreover, the combination of investor memory and market return data can lead to more efficient and profitable portfolios. The findings of this study have important implications for investors and portfolio managers. Investors should consider incorporating investor memory into their decision-making process when constructing stock portfolios. Additionally, portfolio managers can use the proposed multi-objective optimization model to create more efficient and profitable portfolios for their clients. This study provides a foundation for future research on portfolio optimization. Future studies can explore other factors that influence investor behavior, such as social media sentiment and news sentiment. Additionally, researchers can investigate the application of other optimization techniques, such as machine learning algorithms, to portfolio optimization.https://jfr.ut.ac.ir/article_96669_fa9915fe77f6e69fa04239a81a130af5.pdfstock portfolio optimization modelbehavioral preferencesinvestor memorygenetic algorithmtehran stock exchange |
| spellingShingle | vahideh mousavi kakhki Sanaz Khatabi Modeling Portfolio Optimization based on behavioral Preferences and Investor’s Memory تحقیقات مالی stock portfolio optimization model behavioral preferences investor memory genetic algorithm tehran stock exchange |
| title | Modeling Portfolio Optimization based on behavioral Preferences and Investor’s Memory |
| title_full | Modeling Portfolio Optimization based on behavioral Preferences and Investor’s Memory |
| title_fullStr | Modeling Portfolio Optimization based on behavioral Preferences and Investor’s Memory |
| title_full_unstemmed | Modeling Portfolio Optimization based on behavioral Preferences and Investor’s Memory |
| title_short | Modeling Portfolio Optimization based on behavioral Preferences and Investor’s Memory |
| title_sort | modeling portfolio optimization based on behavioral preferences and investor s memory |
| topic | stock portfolio optimization model behavioral preferences investor memory genetic algorithm tehran stock exchange |
| url | https://jfr.ut.ac.ir/article_96669_fa9915fe77f6e69fa04239a81a130af5.pdf |
| work_keys_str_mv | AT vahidehmousavikakhki modelingportfoliooptimizationbasedonbehavioralpreferencesandinvestorsmemory AT sanazkhatabi modelingportfoliooptimizationbasedonbehavioralpreferencesandinvestorsmemory |