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RVRAE: A Dynamic Factor Model Based on Variational Recurrent Autoencoder for Stock Returns Prediction

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  • Yilun Wang
  • Shengjie Guo

Abstract

In recent years, the dynamic factor model has emerged as a dominant tool in economics and finance, particularly for investment strategies. This model offers improved handling of complex, nonlinear, and noisy market conditions compared to traditional static factor models. The advancement of machine learning, especially in dealing with nonlinear data, has further enhanced asset pricing methodologies. This paper introduces a groundbreaking dynamic factor model named RVRAE. This model is a probabilistic approach that addresses the temporal dependencies and noise in market data. RVRAE ingeniously combines the principles of dynamic factor modeling with the variational recurrent autoencoder (VRAE) from deep learning. A key feature of RVRAE is its use of a prior-posterior learning method. This method fine-tunes the model's learning process by seeking an optimal posterior factor model informed by future data. Notably, RVRAE is adept at risk modeling in volatile stock markets, estimating variances from latent space distributions while also predicting returns. Our empirical tests with real stock market data underscore RVRAE's superior performance compared to various established baseline methods.

Suggested Citation

  • Yilun Wang & Shengjie Guo, 2024. "RVRAE: A Dynamic Factor Model Based on Variational Recurrent Autoencoder for Stock Returns Prediction," Papers 2403.02500, arXiv.org.
  • Handle: RePEc:arx:papers:2403.02500
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    References listed on IDEAS

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    1. Ng, Victor & Engle, Robert F. & Rothschild, Michael, 1992. "A multi-dynamic-factor model for stock returns," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 245-266.
    2. Laurent Callot & Mehmet Caner & A. Özlem Önder & Esra Ulaşan, 2021. "A Nodewise Regression Approach to Estimating Large Portfolios," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(2), pages 520-531, March.
    3. Gu, Shihao & Kelly, Bryan & Xiu, Dacheng, 2021. "Autoencoder asset pricing models," Journal of Econometrics, Elsevier, vol. 222(1), pages 429-450.
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