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Can random matrix theory resolve Markowitz optimization enigma?

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TitleInfo
Title
Can random matrix theory resolve Markowitz optimization enigma?
SubTitle
the impact of "noise" filtered covariance matrix on portfolio selection
Name (type = personal)
NamePart (type = family)
Ng
NamePart (type = given)
Kim Wah
NamePart (type = date)
1956-
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Kim Ng
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Long
NamePart (type = given)
Michael S.
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Michael S. Long
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Advisory Committee
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chair
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Sopranzetti
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Ben
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Ben Sopranzetti
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Advisory Committee
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internal member
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NamePart (type = family)
Wu
NamePart (type = given)
Yangru
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Yangru Wu
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Advisory Committee
Role
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internal member
Name (type = personal)
NamePart (type = family)
Chen
NamePart (type = given)
Ren-Raw
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Ren-Raw Chen
Affiliation
Advisory Committee
Role
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outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - Newark
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school
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Text
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theses
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DateCreated (qualifier = exact)
2014
DateOther (qualifier = exact); (type = degree)
2014-05
Place
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xx
Language
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eng
Abstract (type = abstract)
Modern finance theory is based on the simple concept of risk and return trade-off. Risk is based upon one holding a diversified portfolio to get the lowest level of risk for a given expected return. This is the foundation of Markowitz’s mean-variance (MV) efficient portfolio. For nearly six decades since Markowitz’s pioneering work, it is still a puzzle as to why there are persistent doubts about the performance of MV approach to portfolio selection and the lack of acceptance as a viable tool in the investment community. This puzzle is coined as the “Markowitz optimization enigma”. The major problem with MV optimization is its tendency to maximize the effects of estimation errors in the risk and return estimates. iii The latest attempt to reduce the noise in covariance estimates is a branch from physics that uses Random Matrix Theory (RMT) prediction. The prediction is that when the number of securities is large relative to the number of observations, the eigenvalues of the covariance matrix within a predicted band closely resemble the distribution as if they were generated from purely random returns. These studies believe that by modifying the eigenvalues within the predicted band, the “filtered” covariance matrix would contain better information than the raw sample matrix. One proprietary commercial product, called the Neutron QuantumApp which was released in mid-2013, based its filtration technique on RMT prediction. The motivation of this dissertation is to examine the effectiveness of the Neutron product in terms of risk measurement, mean-variance efficiency and portfolio performance. More specifically, does the filtered covariance contain superior information as compared to the raw covariance? The evidence shows that the efficient frontier, generated from filtered covariance, indeed dominates the raw efficient frontier for the unconstrained case. When short-sale constraint is imposed, the result is similar except for the minimum variance portfolio (MVP). The MVP from the raw matrix dominates the MVP from the filtered matrix. In general, the filtered covariance appears to be better for the purpose of risk measurement and risk management. The filtered correlation structure is considerably higher. iv However, more efficient portfolios do not translate into better performers. For the period 2006 to 2013, one cannot reject the null hypothesis that the filtered portfolios perform similarly to the raw portfolios. Therefore, my conclusion is that the Neutron product cannot resolve Markowitz optimization enigma.
Subject (authority = RUETD)
Topic
Management
Subject (authority = ETD-LCSH)
Topic
Risk assessment
Subject (authority = ETD-LCSH)
Topic
Risk-return relationships
RelatedItem (type = host)
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Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_5650
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Note
Supplementary File: Appendices
Extent
xv, 165 p. : ill.
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Kim Wah Ng
Subject (authority = ETD-LCSH)
Topic
Matrices--Computer programs
RelatedItem (type = host)
TitleInfo
Title
Graduate School - Newark Electronic Theses and Dissertations
Identifier (type = local)
rucore10002600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3959FT5
Genre (authority = ExL-Esploro)
ETD doctoral
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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
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Ng
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Kim
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RightsEvent
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Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2014-05-01 17:23:26
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Kim Ng
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Affiliation
Rutgers University. Graduate School - Newark
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
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Copyright protected
Availability
Status
Open
Reason
Permission or license
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windows xp
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