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Simple Tests for Stock Return Predictability with Good Size and Power Properties

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  • Harvey, David I
  • Leybourne, Stephen J
  • Taylor, AM Robert

Abstract

We develop easy-to-implement tests for return predictability which, relative to extant tests in the literature, display attractive finite sample size control and power across a wide range of persistence and endogeneity levels for the predictor. Our approach is based on the standard regression t-ratio and a variant where the predictor is quasi-GLS (rather than OLS) demeaned. In the strongly persistent near-unit root environment, the limiting null distributions of these statistics depend on the endogeneity and local-to-unity parameters characterising the predictor. Analysis of the asymptotic local power functions of feasible implementations of these two tests, based on asymptotically conservative critical values, motivates a switching procedure between the two, employing the quasi-GLS demeaned variant unless the magnitude of the estimated endogeneity correlation parameter is small. Additionally, if the data suggests the predictor is weakly persistent, our approach switches into the standard t-ratio test with reference to standard normal critical values.

Suggested Citation

  • Harvey, David I & Leybourne, Stephen J & Taylor, AM Robert, 2021. "Simple Tests for Stock Return Predictability with Good Size and Power Properties," Essex Finance Centre Working Papers 29814, University of Essex, Essex Business School.
    • Handle: RePEc:esy:uefcwp:29814
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    Cited by:

    1. Christis Katsouris, 2023. "Structural Break Detection in Quantile Predictive Regression Models with Persistent Covariates," Papers 2302.05193, arXiv.org.
    2. Yijie Fei & Yiu Lim Lui & Jun Yu, 2024. "Testing Predictability in the Presence of Persistent Errors," Working Papers 202401, University of Macau, Faculty of Business Administration.
    3. Xiaosai Liao & Xinjue Li & Qingliang Fan, 2024. "Robust Inference for Multiple Predictive Regressions with an Application on Bond Risk Premia," Papers 2401.01064, arXiv.org.
    4. Demetrescu, Matei & Georgiev, Iliyan & Rodrigues, Paulo M.M. & Taylor, A.M. Robert, 2023. "Extensions to IVX methods of inference for return predictability," Journal of Econometrics, Elsevier, vol. 237(2).
    5. Sam Astill & David I. Harvey & Stephen J. Leybourne & A. M. Robert Taylor, 2025. "Bonferroni‐Type Tests for Return Predictability With Possibly Trending Predictors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(1), pages 37-56, January.
    6. Tassos Magdalinos & Katerina Petrova, 2022. "Uniform and Distribution-Free Inference with General Autoregressive Processes," Working Papers 1344, Barcelona School of Economics.

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    Keywords

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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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