Liquidity and earnings in event studies: Does data granularity matter?

Bohmann, M; Michayluk, D; Patel, V; Walsh, K

Liquidity and earnings in event studies: Does data granularity matter?

Bohmann, M Michayluk, D

Patel, V

Walsh, K

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Publication Type:: Journal Article
Citation:: Pacific Basin Finance Journal, 2019, 54 pp. 118 - 131
Issue Date:: 2019-04-01

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Field	Value	Language
dc.contributor.author	Bohmann, M	en_US
dc.contributor.author	Michayluk, D https://orcid.org/0000-0001-9020-0855	en_US
dc.contributor.author	Patel, V https://orcid.org/0000-0002-1696-3325	en_US
dc.contributor.author	Walsh, K https://orcid.org/0000-0003-4529-771X	en_US
dc.date.issued	2019-04-01	en_US
dc.identifier.citation	Pacific Basin Finance Journal, 2019, 54 pp. 118 - 131	en_US
dc.identifier.issn	0927-538X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/136151
dc.description.abstract	© 2019 Elsevier B.V. Market microstructure data availability has significantly improved over time and it is now possible to estimate liquidity measures at the nanosecond level. However, this level of data is unavailable in all markets and time periods and there is a significant cost and computational burden of high-frequency data. Goyenko et al. (2009) and Fong et al. (2017) show that various low-frequency liquidity measures can proxy for high-frequency benchmarks and show that the results are robust across countries and time. However, liquidity measures do not always behave in the expected fashion during periods of information asymmetry (Collin-Dufresne and Fos, 2015). Drawing from Ball and Brown (1968), we use an event study methodology to investigate whether the low-frequency measures of liquidity can proxy for high-frequency measures around earnings announcements (i.e., periods of information asymmetry). We find that the Closing-Price-Quoted-Spread is the best proxy for the percent-cost high-frequency benchmarks. In contrast, using cross-sectional, portfolio and individual time-series correlations the most consistent low-frequency cost-per-dollar proxies are the High-Low-Impact and Closing-Price-Quoted-Spread-Impact, however, the performance of these proxies weakens in the pre- and post-announcement periods around the earnings announcement.	en_US
dc.relation.ispartof	Pacific Basin Finance Journal	en_US
dc.relation.isbasedon	10.1016/j.pacfin.2018.12.007	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Finance	en_US
dc.title	Liquidity and earnings in event studies: Does data granularity matter?	en_US
dc.type	Journal Article
utslib.citation.volume	54	en_US
utslib.for	1502 Banking, Finance and Investment	en_US
utslib.for	1501 Accounting, Auditing and Accountability	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Finance Discipline
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	54	en_US

Abstract:

© 2019 Elsevier B.V. Market microstructure data availability has significantly improved over time and it is now possible to estimate liquidity measures at the nanosecond level. However, this level of data is unavailable in all markets and time periods and there is a significant cost and computational burden of high-frequency data. Goyenko et al. (2009) and Fong et al. (2017) show that various low-frequency liquidity measures can proxy for high-frequency benchmarks and show that the results are robust across countries and time. However, liquidity measures do not always behave in the expected fashion during periods of information asymmetry (Collin-Dufresne and Fos, 2015). Drawing from Ball and Brown (1968), we use an event study methodology to investigate whether the low-frequency measures of liquidity can proxy for high-frequency measures around earnings announcements (i.e., periods of information asymmetry). We find that the Closing-Price-Quoted-Spread is the best proxy for the percent-cost high-frequency benchmarks. In contrast, using cross-sectional, portfolio and individual time-series correlations the most consistent low-frequency cost-per-dollar proxies are the High-Low-Impact and Closing-Price-Quoted-Spread-Impact, however, the performance of these proxies weakens in the pre- and post-announcement periods around the earnings announcement.

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http://hdl.handle.net/10453/136151