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

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.

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Publication Details

Journal
Annals of Operations Research
Published
2024-11-22
DOI
https://doi.org/10.1007/s10479-024-06355-0
Primary Topic
Financial Markets and Investment Strategies
Type
article
Field-Weighted Citation Impact
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article

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

Richard Ashley, Faezeh Najafi
Annals of Operations Research
Financial Markets and Investment Strategies
article

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

Richard Ashley, Faezeh Najafi
article en

Abstract

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.

Annals of Operations ResearchVol. 346(1)
Virginia Tech (US)
Openalex Percentile: Top 71%
Financial Markets and Investment Strategies
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The Ashley and Patterson (1986) test for serial independence in daily stock returns, revisited — Richard Ashley, Faezeh Najafi · Annals of Operations Research (2024) | TGRS Research Map | TGRS