Novel methods for selecting stock portfolio in conditions of uncertainty and forecasting with RR-DEA, ANFIS, FGP: A case study of Tehran stock exchange

Mohammadmahdi Taheri, Amir Azizi, Emran Mohammadi and Abbas Saghaei

PLOS ONE, 2025, vol. 20, issue 7, 1-41

Abstract: Portfolio selection and management are two of the most important decisions in the financial field. The existence of uncontrollable factors affects the decision-making process, which is a problem for investors who are responsible for the final financial decisions on how to allocate their budgets to financial assets in their investment portfolios. To overcome the challenges involved in the selection of a stock portfolio, this article presents a three-stage optimization model. In the first stage, the pharmaceutical industry data collected from the Tehran Stock Exchange (TSE) website is used to apply the robust ratio data envelopment analysis (RR-DEA) in GAMS software with respect to some specific financial indicators to determine efficient stocks in conditions of data uncertainty. These selected stocks are then moved to the second stage, where the ANFIS algorithm is employed in MATLAB to predict the final closing prices and calculate the prediction error (RMSE). In the third stage, the fuzzy goal programming (FGP) method is applied, incorporating the prediction errors from the previous stage. The model is optimized in GAMS software, considering each Index’s objectives in a fuzzy context, with the results presented separately for different objectives. For this problem, in the first stage 27 stocks were selected as samples from the (TSE) website using the proposed methods, and 23 stocks were entered into the price prediction stage. Finally, in the FGP stage, optimization and purchase amount of each share was done. Illustrative results show that the proposed approach is effective for portfolio selection and optimization in the presence of uncertain data.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0321370

DOI: 10.1371/journal.pone.0321370

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