The Resource Empirically identified industry classification, by Michael Gibbs

Empirically identified industry classification, by Michael Gibbs

Label
Empirically identified industry classification
Title
Empirically identified industry classification
Statement of responsibility
by Michael Gibbs
Creator
Contributor
Author
Thesis advisor
Subject
Genre
Language
eng
Summary
This study examines return based correlations between industry returns and firm returns to create more objective and comparable industry classifications. In my first essay I model a market with firms that invest in one or more categories of assets. Firms that invest in assets with similar return correlations are grouped into categories that are comparable to industry groups in the standard scheme of classifying firms into industries based on offering a common product or service. Because these categories are based on objective rather than subjective criteria, use of these categories by investors might have advantages when using industry information to make investment decisions and construct portfolios. I also derive estimable equations to measure firms' exposures to category risk thereby identifying in which category or categories the firm belongs, and we use simulation to explore the efficacy of three different estimation methods. In my second essay I evaluate the question does the number of industry exposures (i.e. diversification level) affect corporate value. I find an unconditional diversification premium. However, there is substantial time series variation in the relation between diversification and valuation. This variation is able to reconcile many of the conflicting conclusions in the prior literature. In my third essay I perform empirical tests to determine whether industry returns can be refined by applying an iterative regression of firm returns on industry returns to create returns than are more inter-correlated but also more orthogonal to other industries' returns. I find strong evidence that an iterative process of return generation provides benefits to researchers as well as practitioners
Cataloging source
MUU
http://library.link/vocab/creatorDate
1984-
http://library.link/vocab/creatorName
Gibbs, Michael
Degree
Ph. D.
Dissertation note
Thesis
Dissertation year
2016.
Government publication
government publication of a state province territory dependency etc
Granting institution
University of Missouri--Columbia
Illustrations
illustrations
Index
no index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
  • theses
http://library.link/vocab/relatedWorkOrContributorName
French, Dan W.
http://library.link/vocab/subjectName
  • Industries
  • Diversification in industry
  • Stocks
Label
Empirically identified industry classification, by Michael Gibbs
Instantiates
Publication
Note
  • Dissertation supervisor: Dr. Dan French
  • Includes vita
Bibliography note
Includes bibliographical references (pages 140-143)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Control code
988338412
Extent
1 online resource (ix, 150 pages)
Form of item
online
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations
Specific material designation
remote
System control number
(OCoLC)988338412
Label
Empirically identified industry classification, by Michael Gibbs
Publication
Note
  • Dissertation supervisor: Dr. Dan French
  • Includes vita
Bibliography note
Includes bibliographical references (pages 140-143)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Control code
988338412
Extent
1 online resource (ix, 150 pages)
Form of item
online
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations
Specific material designation
remote
System control number
(OCoLC)988338412

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