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

Abdellilah Nafia (), Abdellah Yousfi and Abdellah Echaoui
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Abdellilah Nafia: Laboratory of Economic Analysis and Modeling (LEAM), Mohammed V University, Rabat 12000, Morocco
Abdellah Yousfi: Laboratory of Economic Analysis and Modeling (LEAM), Mohammed V University, Rabat 12000, Morocco
Abdellah Echaoui: Laboratory of Economic Analysis and Modeling (LEAM), Mohammed V University, Rabat 12000, Morocco

IJFS, 2023, vol. 11, issue 2, 1-48

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.

Keywords: portfolio performance; stock prediction; stock selection; portfolio construction; long short-term memory (LSTM); portfolio management; equity-market-neutral; investment strategy; stock return; S&P500 index (search for similar items in EconPapers)
JEL-codes: F2 F3 F41 F42 G1 G2 G3 (search for similar items in EconPapers)
Date: 2023
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