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A simple nonlinear predictive model for stock returns

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  • Biqing Cai
  • Jiti Gao

Abstract

In this paper, we propose a simple approach to testing and modelling nonlinear predictability of stock returns using Hermite Functions. The proposed test suggests that there exists a kind of nonlinear predictability for the dividend yield. Furthermore, the out-of-sample evaluation results suggest the dividend yield has nonlinear predictive power for stock returns while the book-to-market ratio and earning-price ratio have little predictive power.

Suggested Citation

  • Biqing Cai & Jiti Gao, 2017. "A simple nonlinear predictive model for stock returns," Monash Econometrics and Business Statistics Working Papers 18/17, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2017-18
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    File URL: https://www.monash.edu/business/econometrics-and-business-statistics/research/publications/ebs/wp18-17.pdf
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Hermite functions; out-of-sample forecast; return predictability; series estimator; unit root.;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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