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US Stock return predictability with high dimensional models

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  • Salisu, Afees A.
  • Tchankam, Jean Paul

Abstract

We examine the role of large information sets in the predictability of US stock using a large data set of over 400 predictors covering macro-, financial-, trade- and commodity-related variables over the period of 1960:Q1 to 2018:Q4. We consider 13 alternative models ranging from autoregressive models with no predictors to 5-factor, 60-factor and high dimensional models with over 400 predictors including assumptions of constant and time varying coefficients. We find that models that incorporate large predictors improve US stock return predictability. The outcome particularly favours models involving Dynamic Variable Selection prior with Variational Bayes (VBDV) for density forecast.

Suggested Citation

  • Salisu, Afees A. & Tchankam, Jean Paul, 2022. "US Stock return predictability with high dimensional models," Finance Research Letters, Elsevier, vol. 45(C).
    • Handle: RePEc:eee:finlet:v:45:y:2022:i:c:s1544612321002646
    DOI: 10.1016/j.frl.2021.102194
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    Cited by:

    1. Shu, Lei & Lu, Feiyang & Chen, Yu, 2023. "Robust forecasting with scaled independent component analysis," Finance Research Letters, Elsevier, vol. 51(C).
    2. Kuppenheimer, Gregory & Shelly, Stuart & Strauss, Jack, 2023. "Can machine learning identify sector-level financial ratios that predict sector returns?," Finance Research Letters, Elsevier, vol. 57(C).
    3. Awijen, Haithem & Ben Zaied, Younes & Ben Lahouel, Béchir & Khlifi, Foued, 2023. "Machine learning for US cross-industry return predictability under information uncertainty," Research in International Business and Finance, Elsevier, vol. 64(C).

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    More about this item

    Keywords

    US stock returns; High-dimensional models; Forecast evaluation;
    All these keywords.

    JEL classification:

    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models
    • O51 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - U.S.; Canada
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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