A portfolio construction framework using LSTM‐based stock markets forecasting

Zeynep Cipiloglu Yildiz and Selim Baha Yildiz

International Journal of Finance & Economics, 2022, vol. 27, issue 2, 2356-2366

Abstract: A novel framework that injects future return predictions into portfolio constructionstrategies is proposed in this study. First, a long–short‐term‐memory (LSTM) model is trained to learn the monthly closing prices of the stocks. Then these predictions are used in the calculation of portfolio weights. Five different portfolio construction strategies are introduced including modifications to smart‐beta strategies. The suggested methods are compared to a number of baseline methods, using the stocks of BIST30 Turkey index. Our strategies yield a very high mean annualized return (25%) which is almost 50% higher than the baseline approaches. The mean Sharpe ratio of our strategies is 0.57, whereas the compared methods’ are 0.29 and −0.32. Comprehensive analysis of the results demonstrates that utilizing predicted returns in portfolio construction enables a significant improvement on the performance of the portfolios.

Date: 2022
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