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Testing for Multiple-Horizon Predictability: Direct Regression Based versus Implication Based

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  • Ke-Li Xu
  • Lauren Cohen

Abstract

Research in finance and macroeconomics has routinely employed multiple horizons to test asset return predictability. In a simple predictive regression model, we find the popular scaled test can have zero power when the predictor is not sufficiently persistent. A new test based on implication of the short-run model is suggested and is shown to be uniformly more powerful than the scaled test. The new test can accommodate multiple predictors. Compared with various other widely used tests, simulation experiments demonstrate remarkable finite-sample performance. We reexamine the predictive ability of various popular predictors for aggregate equity premium.Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Suggested Citation

  • Ke-Li Xu & Lauren Cohen, 2020. "Testing for Multiple-Horizon Predictability: Direct Regression Based versus Implication Based," The Review of Financial Studies, Society for Financial Studies, vol. 33(9), pages 4403-4443.
    • Handle: RePEc:oup:rfinst:v:33:y:2020:i:9:p:4403-4443.
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    File URL: http://hdl.handle.net/10.1093/rfs/hhz135
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    Citations

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    Cited by:

    1. Deshui Yu & Yayi Yan, 2023. "Joint dynamics of stock returns and cash flows: A time‐varying present‐value framework," Financial Management, Financial Management Association International, vol. 52(3), pages 513-541, September.
    2. Demetrescu, Matei & Rodrigues, Paulo M.M. & Taylor, A.M. Robert, 2023. "Transformed regression-based long-horizon predictability tests," Journal of Econometrics, Elsevier, vol. 237(2).
    3. Yu, Deshui & Huang, Difang & Chen, Li & Li, Luyang, 2023. "Forecasting dividend growth: The role of adjusted earnings yield," Economic Modelling, Elsevier, vol. 120(C).
    4. Zhan Gao & Ji Hyung Lee & Ziwei Mei & Zhentao Shi, 2024. "Econometric Inference for High Dimensional Predictive Regressions," Papers 2409.10030, arXiv.org, revised Nov 2024.
    5. Yu, Deshui & Chen, Li & Li, Luyang, 2023. "Time-varying predictability of the long horizon equity premium based on semiparametric regressions," Economics Letters, Elsevier, vol. 224(C).
    6. Yu, Deshui & Chen, Li, 2024. "Local predictability of stock returns and cash flows," Journal of Empirical Finance, Elsevier, vol. 77(C).
    7. Coqueret, Guillaume & Deguest, Romain, 2024. "Unexpected opportunities in misspecified predictive regressions," European Journal of Operational Research, Elsevier, vol. 318(2), pages 686-700.
    8. Christis Katsouris, 2023. "Predictability Tests Robust against Parameter Instability," Papers 2307.15151, arXiv.org.
    9. Erik Hjalmarsson & Tamas Kiss, 2022. "Long‐run predictability tests are even worse than you thought," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1334-1355, November.
    10. Yu, Deshui & Huang, Difang, 2023. "Cross-sectional uncertainty and expected stock returns," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 321-340.
    11. Xu, Ke-Li, 2021. "On the serial correlation in multi-horizon predictive quantile regression," Economics Letters, Elsevier, vol. 200(C).
    12. Jeong, Minsoo, 2022. "Modelling persistent stationary processes in continuous time," Economic Modelling, Elsevier, vol. 109(C).
    13. Guillaume Coqueret & Romain Deguest, 2024. "Unexpected opportunities in misspecified predictive regressions," Post-Print hal-04595355, HAL.

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