Portfolio optimization model with uncertain returns based on prospect theory

Li, Yufeng; Zhou, Bing; Tan, Yingxue

doi:10.1007/s40747-021-00493-9

Cited by 11 publications

(7 citation statements)

References 53 publications

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“…It is challenging to make broad predictions about returns and hazards when investing in new stocks, as discussed by (Li, Zhou, & Tan, 2022). Therefore, we present a model for optimizing portfolios in the face of a non-deterministic rate of return.…”

Section: Literature Reviewmentioning

confidence: 99%

Asset Allocation through Grey Wolf Optimization: A Case of KSE-30 Index

Iqbal

Mehmood

Mirza

et al. 2023

Pak. J. Humanit. Soc. Sci

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This article is an effort to aid investors by drawing attention to select investment items. Grey Wolf Optimization (GWO), TOPSIS with Eigenvector, Market Capitalization, and the Equal Weighted Technique are the four main methods discussed in this paper. This study uses the KSE-30 Index as its sample size; however, because to a lack of data, only 26 businesses are chosen for analysis using 10 criteria. All four methods are implemented and weights are determined based on these criteria. These weights are then utilized in conjunction with MATLAB's in-built tools to construct a portfolio. Based on its ability to generate the greatest possible portfolio, GWO appears to be a powerful resource for affluent investors. Equal-weighted portfolios performed the worst, followed by the Eigenvector-TOPSIS technique, then Market Capitalization.

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Section: Literature Reviewmentioning

confidence: 99%

Asset Allocation through Grey Wolf Optimization: A Case of KSE-30 Index

Iqbal

Mehmood

Mirza

et al. 2023

Pak. J. Humanit. Soc. Sci

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“…The multiobjective optimization techniques [15], principal component analysis [16], deep learning LSTM models [17][18][19], future risk estimation methods [20], and swarm intelligence-based approaches [21][22] are some of the very popular portfolio optimization methods. Various other approaches such as the use of genetic algorithms [23], fuzzy sets [24], prospect theory [25], quantum evolutionary algorithms [26], and time series decomposition [27] for robust portfolio design are also proposed in the literature.…”

Section: Related Workmentioning

confidence: 99%

Design and Analysis of Optimized Portfolios for Selected Sectors of the Indian Stock Market

Sen¹,

Dutta²

2022

2022 International Conference on Decision Aid Sciences and Applications (DASA)

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Portfolio optimization is a challenging problem that has attracted considerable attention and effort from researchers. The optimization of stock portfolios is a particularly hard problem since the stock prices are volatile and estimation of their future volatilities and values, in most cases, is very difficult, if not impossible. This work uses three ratios, the Sharpe ratio, the Sortino ratio, and the Calmar ratio, for designing the mean-variance optimized portfolios for six important sectors listed in the National Stock Exchange (NSE) of India. Three portfolios are designed for each sector maximizing the ratios based on the historical prices of the ten most important stocks of each sector from Jan 1. 2017 to Dec 31. 2020. The evaluation of the portfolios is done based on their cumulative returns over the test period from Jan 1, 2021, to Dec 31, 2021. The ratio that yields the maximum cumulative returns for both the training and the test periods for the majority of the sectors is identified. Additionally, the sectors which exhibit the maximum cumulative returns for the same ratio are also identified. The results provide useful insights for the investors in the stock market in making their investment decisions based on the current return and risks associated with the six sectors and their stocks.

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“…Considering the uncertainty of order ready time and customer satisfaction level, the authors in the fourth paper, "Hybrid evolutionary optimization for takeaway order selection and delivery path planning utilizing habit data" [4] studied an integrated order selection and delivery problem for deliverymen. They estimated the uncertain order ready time and customer satisfaction level based on historical habit data of stores and customers using a machine learning approach, and devised a hybrid evolutionary algorithm combing water wave optimization metaheuristic and tabu search to solve the problem.…”

Section: Data-driven Supply Chain Managementmentioning

confidence: 99%