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Latent factor model for asset pricing

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  • Uddin, Ajim
  • Yu, Dantong

Abstract

One of the fundamental questions in asset pricing is ‘Why different assets earn different average returns?’ In this paper, we designed an autoencoder based asset pricing model to explain the return difference among the stocks in an index. The trained autoencoder generates a set of latent representations that constitutes a combined -‘communal’- factor to better explains a large portion of the return differences among the stocks in an index. After analyzing all the stocks in S&P-500, Russel-3000, and NASDAQ-100, we found that our proposed latent factor model outperforms many other factor models in predicting the next day’s return. Notably, the experiment results show that on average non-communal stocks earn 0.05% over communal stocks. However, the risk associated with this non-communal stock is also 0.8% higher than communal stocks. The experiments confirm that the superior performance comes from the compensation of high risk associated with these non-communal stocks. Investors will benefit from our latent factor model to identify these communal and non-communal stocks for a high return while diversifying their asset portfolio.

Suggested Citation

  • Uddin, Ajim & Yu, Dantong, 2020. "Latent factor model for asset pricing," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
  • Handle: RePEc:eee:beexfi:v:27:y:2020:i:c:s2214635019302333
    DOI: 10.1016/j.jbef.2020.100353
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    Cited by:

    1. Chowdhury, Mohammad Ashraful Ferdous & Meo, Muhammad Saeed & Uddin, Ajim & Haque, Md. Mahmudul, 2021. "Asymmetric effect of energy price on commodity price: New evidence from NARDL and time frequency wavelet approaches," Energy, Elsevier, vol. 231(C).
    2. Uddin, Ajim & Tao, Xinyuan & Yu, Dantong, 2023. "Attention based dynamic graph neural network for asset pricing," Global Finance Journal, Elsevier, vol. 58(C).
    3. Junyi Ye & Bhaskar Goswami & Jingyi Gu & Ajim Uddin & Guiling Wang, 2024. "From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing," Papers 2403.06779, arXiv.org.

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    More about this item

    Keywords

    Asset pricing; Nonlinear factor model; Machine learning; Autoencoders; Fintech;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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