A Complete Nonparametric Event Study Approach

Jonathan Dombrow, Mauricio Rodriguez () and C F Sirmans

Review of Quantitative Finance and Accounting, 2000, vol. 14, issue 4, 80 pages

Abstract: Event studies have been used to examine the direction, magnitude, and speed of security price reactions to various phenomenon. Concerns over the lack of normality in stock return distributions motivated the introduction of nonparametric test statistics in the event study literature. A parametric procedure (OLS), however, has been extensively employed in the estimation of parameters for the market model. This paper, in contrast, applies Theil's nonparametric regression in the estimation of abnormal returns; an approach which is distribution free and provides a complete nonparametric approach for the detection of abnormal performance. Simulation results indicate Theil's estimation procedure offers a slight improvement in power in the detection of abnormal performance over the traditionally employed methodology. The results suggest employing Theil's nonparametric estimation procedure combined with the rank statistic. This complete nonparametric combination offers similar power with fewer underlying assumptions. Copyright 2000 by Kluwer Academic Publishers

Date: 2000
References: Add references at CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://journals.kluweronline.com/issn/0924-865X/contents link to full text (text/html)
Access to full text is restricted to subscribers.

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:kap:rqfnac:v:14:y:2000:i:4:p:361-80

Ordering information: This journal article can be ordered from
http://www.springer.com/finance/journal/11156/PS2

Access Statistics for this article

Review of Quantitative Finance and Accounting is currently edited by Cheng-Few Lee

More articles in Review of Quantitative Finance and Accounting from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().