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Regression and Forecasting of U.S. Stock Returns Based on LSTM

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  • Shicheng Zhou
  • Zizhou Zhang
  • Rong Zhang
  • Yuchen Yin
  • Chia Hong Chang
  • Qinyan Shen

Abstract

This paper analyses the investment returns of three stock sectors, Manuf, Hitec, and Other, in the U.S. stock market, based on the Fama-French three-factor model, the Carhart four-factor model, and the Fama-French five-factor model, in order to test the validity of the Fama-French three-factor model, the Carhart four-factor model, and the Fama-French five-factor model for the three sectors of the market. French five-factor model for the three sectors of the market. Also, the LSTM model is used to explore the additional factors affecting stock returns. The empirical results show that the Fama-French five-factor model has better validity for the three segments of the market under study, and the LSTM model has the ability to capture the factors affecting the returns of certain industries, and can better regress and predict the stock returns of the relevant industries. Keywords- Fama-French model; Carhart model; Factor model; LSTM model.

Suggested Citation

  • Shicheng Zhou & Zizhou Zhang & Rong Zhang & Yuchen Yin & Chia Hong Chang & Qinyan Shen, 2025. "Regression and Forecasting of U.S. Stock Returns Based on LSTM," Papers 2502.05210, arXiv.org, revised Mar 2025.
    • Handle: RePEc:arx:papers:2502.05210
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    References listed on IDEAS

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    1. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    2. Stiven Agusta & Fuad Rakhman & Jogiyanto Hartono Mustakini & Singgih Wijayana, 2024. "Enhancing the accuracy of stock return movement prediction in Indonesia through recent fundamental value incorporation in multilayer perceptron," Asian Journal of Accounting Research, Emerald Group Publishing Limited, vol. 9(4), pages 358-377, July.
    3. Zong Ke & Yuchen Yin, 2024. "Tail Risk Alert Based on Conditional Autoregressive VaR by Regression Quantiles and Machine Learning Algorithms," Papers 2412.06193, arXiv.org.
    4. Zura Kakushadze & Willie Yu, 2016. "Statistical Risk Models," Papers 1602.08070, arXiv.org, revised Jan 2017.
    5. Saqib Muneer & Babar Zaheer Butt & Kashif Ur Rehman, 2011. "A Multifactor Model of Banking Industry Stock Returns: An Emerging Market Perspective," Information Management and Business Review, AMH International, vol. 2(6), pages 267-275.
    6. Ghosh, Pushpendu & Neufeld, Ariel & Sahoo, Jajati Keshari, 2022. "Forecasting directional movements of stock prices for intraday trading using LSTM and random forests," Finance Research Letters, Elsevier, vol. 46(PA).
    7. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
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