Financial and Banking Strategic Studies

Portfolio selection for neural network using energy networks in Tehran Stock Exchange

Document Type : Original Article

Author

Seyyed Mohammad Hadji Molana

Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

https://doi.org/10.22105/fbs.2024.214339
Abstract
Purpose: Optimization problems are one of the most interesting, important, and popular fields of financial mathematics. A better portfolio optimization model can help investors earn more sustainable profits. The existing literature shows that traditional mean-variance portfolio strategies are not suitable. To address this issue, this study uses a multilayer perceptron neural network and a convolutional neural network to predict future stock price movements.
Methodology: We compare the prediction accuracy of these two methods and use the outputs from the higher-accuracy method in the proposed model. Then, given the future direction of stock prices, we propose an efficient stock selection scheme for investors. We also test the proposed stock selection scheme and investment strategies using the components of the Tehran Stock Exchange index as test cases.
Findings: The experimental results show that the proposed stock selection scheme can effectively improve the performance of all investment strategies. In addition, the proposed investment strategy outperforms the traditional minimum global variance investment strategy.
Originality/Value: This research provides an innovative framework for portfolio selection based on deep learning networks. These networks are key to improving investment efficiency, risk management, and decision-making in the Iranian capital market and serve as an advanced model for similar markets.

Keywords

Artificial neural network
Portfolio selection
Deep learning

Subjects

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Financial and Banking Strategic Studies
Volume 2, Issue 3
Autumn 2024
Pages 220-231

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