Volume 11, Issue 3 (12-2025)
mmr 2025, 11(3): 73-103 |
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Najaf Najafi A, Najaf Najafi M. A Data-Driven Approach for Portfolio Optimization Using Machine Learning and Deep Learning Algorithms. mmr 2025; 11 (3) :73-103
URL: http://mmr.khu.ac.ir/article-1-3411-en.html
URL: http://mmr.khu.ac.ir/article-1-3411-en.html
1- School of Industrial Engineering, Iran University of Sci ence and Technology (IUST)
2- Khorasan Razavi Agricultural and Natural Resources Research and Education Center ,mnajafi.mhd@gmail.com
2- Khorasan Razavi Agricultural and Natural Resources Research and Education Center ,
Abstract: (9 Views)
In today's complex and dynamic financial markets, portfolio optimization presents a significant challenge for investors. As such, capital market investors grapple with fundamental questions regarding which stocks to buy, at what time, and in what quantities. This research aims to provide a novel approach to portfolio optimization using a mean-variance model based on predictions from traditional machine learning and deep learning algorithms, offering solutions to these crucial questions. Drawing on the emergence of data-driven methods, this study compares the performance of various machine learning and deep learning algorithms in forecasting stock prices on the Tehran Stock Exchange. The dataset comprises the closing prices of nine major symbols from the Tehran Stock Exchange over a 1000-day period. The findings suggest that traditional machine learning models, particularly linear regression, outperform deep learning models in predicting prices. Furthermore, the mean-variance portfolio optimization approach leverages optimal stock selection and allocation to maximize returns while minimizing risk. This research serves as a practical tool for portfolio managers and risk analysts, facilitating improved risk management and investment portfolio performance.
Type of Study: S |
Subject:
stat
Received: 2024/08/18 | Revised: 2025/12/18 | Accepted: 2025/11/16 | Published: 2025/12/17 | ePublished: 2025/12/17
Received: 2024/08/18 | Revised: 2025/12/18 | Accepted: 2025/11/16 | Published: 2025/12/17 | ePublished: 2025/12/17
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