A Hybrid Model for Portfolio Optimization Based on Stock Price Forecasting with LSTM Recurrent Neural Network using Cardinality Constraints and Multi-Criteria Decision Making Methods (Case study of Tehran Stock Exchange)

Document Type : Original Article

Authors

1 Ph.D. Candidate in Financial Engineering, Department of Management, Karaj Branch, Islamic Azad University, Karaj, Iran.

2 Associate Prof, Department of Accounting, Karaj Branch, Islamic Azad University, Karaj, Iran.

3 *** Assistant prof, Department of Accounting, Karaj Branch, Islamic Azad University, Karaj, Iran.

4 Assistant prof, Department of Management, Karaj Branch, Islamic Azad University, Karaj, Iran.

Abstract

Due to the dynamic trend of stock prices and the volatile nature of the market, asset price forecasting plays a key role in creating an efficient strategy, and the results of price forecasting are a prerequisite for creating an optimal stock portfolio. The purpose of this study is to provide a hybrid model to help investors in selecting the optimal portfolio. Therefore, ten top industries have been selected among the active industries of the Tehran Stock Exchange using IAHP method, Then, the stock price of companies listed on the Tehran Stock Exchange from 2016 to 2021 are forecast at the considered time horizons using LSTM. In the next step, three portfolios with different time horizons are selected by using the CoCoSo method, and finally, optimal weights have been determined and an efficient frontier has been drawn using Mixed-Integer Quadratic Program and Branch and Cut Algorithm based on LAM Model. According to the results of this research, the proposed model gives higher returns to investors due to the risk of constituting portfolios with specified time horizons in contrast to traditional approaches

Keywords

References

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Financial Management Perspective
Volume 11, Issue 36
December 2021
Pages 119-143

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