A prediction-based portfolio optimization model using CNN neural network and MSAD criterion in Tehran Stock Exchange

Document Type : Original Article

Authors

1 Department of management, Faculty of Economics and Social Sciences, Ahvaz, Iran

2 , Department of Management, Faculty of Economics and Social Sciences, Shahid Chamran University of Ahvaz. Ahvaz. Iran

3 Management department - Faculty of economics and social sciences - Shahid Chamran University of Ahvaz - Ahvaz - Iran

Abstract

Portfolio optimization as a popular research field has received many attention from researchers in recent decades. Quality of portfolio optimization helps investors generate more sustainable returns. In this research, Convolutional Neural Network (CNN) is used to build a portfolio optimization model based on prediction. This model not only benefits from deep learning technology, but also benefits from modern portfolio theory.
In this approach, CNN is first used to predict the future return of each stock. Then, the prediction error of CNN is used as the risk measure of each stock. Integrating the predicted return with the semi-absolute deviation of the prediction error leads to the construction of the portfolio optimization model. This model is compared with an equally weighted portfolio whose stocks are selected with CNN. Also, two prediction based portfolio models with support vector regression (SVR) are used as benchmark portfolios. The empirical data of this research includes the companies in the index of 50 most active companies of Tehran Stock Exchange. The experimental results show that the prediction-based portfolio model with CNN shows a superior performance compared to SVR in the conditions of different returns. Also, the increase in the expected return can improve the performance of this model. This research clearly states the positive role of deep neural networks (DNNs) in creating portfolio optimization models.

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Main Subjects

References

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Budget and Finance Strategic Research
Volume 5, Issue 3 - Serial Number 19
Autumn Quarterly
October 2024
Pages 11-30

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History

  • Receive Date: 12 June 2024
  • Revise Date: 22 July 2024
  • Accept Date: 09 October 2024

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