Stock Portfolio Optimization Using a Deep Learning LSTM Model

TL;DR

This paper employs an LSTM deep learning model to predict stock prices and optimize portfolios across Indian market sectors, demonstrating high prediction accuracy and effective risk-return trade-offs.

Contribution

It introduces a sector-wise portfolio optimization approach using LSTM-based stock price predictions, which is a novel application in the Indian stock market context.

Findings

High accuracy in predicted stock returns

Effective risk-return trade-offs achieved

LSTM model outperforms traditional methods

Abstract

Predicting future stock prices and their movement patterns is a complex problem. Hence, building a portfolio of capital assets using the predicted prices to achieve the optimization between its return and risk is an even more difficult task. This work has carried out an analysis of the time series of the historical prices of the top five stocks from the nine different sectors of the Indian stock market from January 1, 2016, to December 31, 2020. Optimum portfolios are built for each of these sectors. For predicting future stock prices, a long-and-short-term memory (LSTM) model is also designed and fine-tuned. After five months of the portfolio construction, the actual and the predicted returns and risks of each portfolio are computed. The predicted and the actual returns of each portfolio are found to be high, indicating the high precision of the LSTM model.