A Nonparametric, Distribution-Free Test for Serial Independence in Stock Returns

Richard Ashley () and Douglas M. Patterson

Journal of Financial and Quantitative Analysis, 1986, vol. 21, issue 2, 221-227

Abstract: A fundamental statistical test of serial independence is developed and applied to daily stock returns. Let xt be the deviation of the daily return on a stock from its sample mean after any autocorrelation present has been removed. If xt is serially independent, then the cumulative sum of xt over time is the position of a one-dimensional random walk on a line. The empirical distribution of step lengths over a large sample allows the distribution of the largest absolute excursion in a T-step walk to be calculated by repeated simulation. The observed maximal excursions are found to be significantly smaller than one would expect, based on serial independence and the observed distribution of step lengths. It is concluded that these daily stock returns are not serially independent and that the market value of the corporations studied has a tendency to return to an interval around the trend value.

Date: 1986
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.cambridge.org/core/product/identifier/ ... type/journal_article link to article abstract page (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cup:jfinqa:v:21:y:1986:i:02:p:221-227_01

Access Statistics for this article

More articles in Journal of Financial and Quantitative Analysis from Cambridge University Press Cambridge University Press, UPH, Shaftesbury Road, Cambridge CB2 8BS UK.
Bibliographic data for series maintained by Kirk Stebbing ().