Equity-Market-Neutral Strategy Portfolio Construction Using LSTM-Based Stock Prediction and Selection: An Application to S&P500 Consumer Staples Stocks

Abstract

In recent years, a great deal of attention has been devoted to the use of neural networks in portfolio management, particularly in the prediction of stock prices. Building a more profitable portfolio with less risk has always been a challenging task. In this study, we propose a model to build a portfolio according to an equity-market-neutral (EMN) investment strategy. In this portfolio, the selection of stocks comprises two steps: a prediction of the individual returns of stocks using LSTM neural network, followed by a ranking of these stocks according to their predicted returns. The stocks with the best predicted returns and those with the worst predicted returns constitute, respectively, the long side and the short side of the portfolio to be built. The proposed model has two key benefits. First, data from historical quotes and technical and fundamental indicators are used in the LSTM network to provide good predictions. Second, the EMN strategy allows for the funding of long-position stocks by short-sell-position stocks, thus hedging the market risk. The results show that the built portfolios performed better compared to the benchmarks. Nonetheless, performance slowed down during the COVID-19 pandemic.

Stock Selection by Using Fundamental Analysis and Optimal Portfolio Construction: A Case Study of Equity Market of India

“…The Sharpe single index model, the capital asset price model, and the Markowitz model can all be used to make portfolios. When the stock makes the most money, the amount will be big [10]. ❖ Revising the portfolio: The owner must modify the mix of investments based on market and economic conditions after creating the ideal Portfolio.…”

Portfolio Optimization by LSTM with a Selection of Six Stocks

“…Its ability to model complex relationships can be beneficial in forecasting asset prices or returns, which are crucial inputs for portfolio optimization [7]. However, it is worth noting that the integration of LSTM with portfolio optimization is still in its early stages, with limited research available [8]. Further exploration is needed to fully understand the potential benefits and challenges associated with combining LSTM and portfolio optimization techniques effectively.…”