Artikel

Non-parametric statistic for testing cumulative abnormal stock returns

Due to the non-normality of stock returns, nonparametric rank tests are gaining accceptance relative to parametric tests in financial economics event studies. In rank tests, financial assets' multiple day cumulative abnormal returns (CARs) are replaced by cumulated ranks. This paper proposes modifications to the existing approaches to improve robustness to cross-sectional correlation of returns arising from calendar time overlapping event windows. Simulations show that the proposed rank test is well specified in testing CARs and is robust towards both complete and partial overlapping event windows.

Language: Englisch

Bibliographic citation: Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 15 ; Year: 2022 ; Issue: 4 ; Pages: 1-13 ; Basel: MDPI

Classification: Wirtschaft
Information and Market Efficiency; Event Studies; Insider Trading
Econometric and Statistical Methods and Methodology: General
Statistical Simulation Methods: General

Subject: finance
economics
event study
clustered event days
cross-sectional correlation
cumulated ranks
rank test
standardized abnormal returns

Event: Geistige Schöpfung

(who): Pynnönen, Seppo

Event: Veröffentlichung

(who): MDPI

(where): Basel

(when): 2022

DOI: doi:10.3390/jrfm15040149

Handle: http://hdl.handle.net/10419/258872

Last update: 10.03.2025, 11:42 AM CET

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Pynnönen, Seppo
MDPI

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2022

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Non-parametric statistic for testing cumulative abnormal stock returns

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