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The Ashley and Patterson (1986) test for serial independence in daily stock returns, revisited

Author

Listed:
  • Richard A. Ashley

    (Virginia Tech)

  • Faezeh Najafi

    (Virginia Tech)

Abstract

We update and extend the non-parametric test proposed in Ashley and Patterson (J Financ Quant Anal 21:221–227, 2014) – of the proposition that the (pre-whitened) daily stock returns for a firm are serially independent, and hence unpredictable from their own past. That paper applied this test to daily returns from 1962 to 1981 for several U.S. corporations and aggregate indices, finding mixed evidence against this null hypothesis of serial independence. The returns dataset is updated here to include thirteen firms which are currently more relevant, and the sample is extended through the end of 2023. We also update the simulation methodology here to properly account for the conditional heteroskedasticity in the daily returns data, so that the present results should now be more statistically reliable. The results are broadly in line with our earlier results, but they do suggest further avenues of research in this area.

Suggested Citation

  • Richard A. Ashley & Faezeh Najafi, 2025. "The Ashley and Patterson (1986) test for serial independence in daily stock returns, revisited," Annals of Operations Research, Springer, vol. 346(1), pages 567-584, March.
    • Handle: RePEc:spr:annopr:v:346:y:2025:i:1:d:10.1007_s10479-024-06355-0
    DOI: 10.1007/s10479-024-06355-0
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    Keywords

    Stock returns; Random walks; Serial independence; Bootstrap; Nonparametric testing; Serial independence;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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