Size and power in tests of return predictability

Stephen LeRoy and Rish Singhania

Quantitative Finance, 2022, vol. 22, issue 6, 1153-1167

Abstract: We study the size-power tradeoff of commonly employed tests of return predictability. For short horizon tests, we show analytically that the indirect dividend test is asymptotically more powerful than the direct return test when dividend growth is less volatile than returns, as appears to be true in the data. The asymptotic power advantages of the dividend test carry over to small samples. Asymptotically, the relative power of the short vs long-horizon return test may depend on size. For empirically relevant parameter values the short-horizon return test is asymptotically more powerful than the long-horizon test at the 1% level but the reverse is true at the 5% and 10% levels. Monte Carlo analysis indicates that, in small samples, the long-horizon return test is more powerful than the short-horizon return test for all sizes. The differences in the relative power of the tests in the small sample case is traced back to the correlation structure of the underlying shocks.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/14697688.2021.2020888 (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:taf:quantf:v:22:y:2022:i:6:p:1153-1167

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RQUF20

DOI: 10.1080/14697688.2021.2020888

Access Statistics for this article

Quantitative Finance is currently edited by Michael Dempster and Jim Gatheral

More articles in Quantitative Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().