Fundamental Anomalies

dc.contributor.author Ma, Guoliang
dc.contributor.department Department of Statistics (LAS)
dc.contributor.majorProfessor Cindy Yu
dc.date 2020-08-28T18:44:18.000
dc.date.accessioned 2021-02-25T00:02:33Z
dc.date.available 2021-02-25T00:02:33Z
dc.date.copyright Wed Jan 01 00:00:00 UTC 2020
dc.date.embargo 2020-07-16
dc.date.issued 2020-01-01
dc.description.abstract <p>This paper quantifies to what extent stock market anomalies are driven by firm funda- mentals. We estimate the parameters of a 2-capital q-model (Congalves, Xue and Zhang, 2019) by matching the entire time series of stock returns at firm level using Markov Chain Monte Carlo (MCMC), instead of matching the average anomaly returns as prior studies (Liu, Whited and Zhang, 2009; Congalves, Xue and Zhang, 2019) do. Our paper resolves the critique of the prior studies that the parameter values of the model are chosen to fit a specific set of anomalies and different values are required for different anomalies. Because anomaly premiums are not the moment conditions of our estimation, our methodology provides a true test on the capability of the q-theory in explaining anomalies. We show that the model is able to generate sizable premiums for Momentum, ROE, and Asset growth.</p>
dc.format.mimetype PDF
dc.identifier archive/lib.dr.iastate.edu/creativecomponents/599/
dc.identifier.articleid 1639
dc.identifier.contextkey 18550132
dc.identifier.doi https://doi.org/10.31274/cc-20240624-773
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath creativecomponents/599
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/93719
dc.source.bitstream archive/lib.dr.iastate.edu/creativecomponents/599/CC_Guoliang_Ma.pdf|||Sat Jan 15 01:04:41 UTC 2022
dc.subject.disciplines Statistics and Probability
dc.subject.keywords q-theory
dc.subject.keywords Investment
dc.subject.keywords BMCMC estimation
dc.title Fundamental Anomalies
dc.type creative component
dc.type.genre creative component
dspace.entity.type Publication
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
thesis.degree.discipline Statistics
thesis.degree.level creativecomponent
File
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
CC_Guoliang_Ma.pdf
Size:
549.9 KB
Format:
Adobe Portable Document Format
Description: