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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Abdellilah Nafia & Abdellah Yousfi & Abdellah Echaoui, 2023. "Equity-Market-Neutral Strategy Portfolio Construction Using LSTM-Based Stock Prediction and Selection: An Application to S&P500 Consumer Staples Stocks," IJFS, MDPI, vol. 11(2), pages 1-48, March.
    • Handle: RePEc:gam:jijfss:v:11:y:2023:i:2:p:57-:d:1110005
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    References listed on IDEAS

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    4. Zexin Hu & Yiqi Zhao & Matloob Khushi, 2021. "A Survey of Forex and Stock Price Prediction Using Deep Learning," Papers 2103.09750, arXiv.org.
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