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Three studies on portfolio optimization and performance appraisal
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Zhang, Huazhu (Researcher in business) (2011) Three studies on portfolio optimization and performance appraisal. PhD thesis, University of Warwick.
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Official URL: http://webcat.warwick.ac.uk/record=b2585156~S1
Abstract
This thesis studies three important issues in portfolio management: the impact of
estimation risk on portfolio optimization, the role of fundamental analysis in portfolio
selection and the power of the bootstrap approach for separating skill from luck across a
sample of portfolio managers.
The first study examines the practical value of the mean-variance portfolio
optimization. This issue arises from the concern that the performance of the meanvariance
portfolio suffers seriously from estimation errors in input parameters. Based on
simulated asset returns, we compare the performance of selected popular portfolios
against the naïve equally weighted portfolio (1/N) in terms of the Sharpe Ratio. We
conclude that given relatively small and persistent anomalies, some sophisticated
portfolio rules can outperform the naïve one at estimation windows of reasonable
lengths. We find that (1) an estimation window of 120 months is needed for the
optimization-based portfolio rules to outperform the 1/N rule when annual abnormal
returns lie between a certain range; (2) given the same abnormal returns, even longer
estimation windows are needed when asset returns exhibit fat tails; (3) our preferred
portfolio rule, which combines optimally the sample tangency portfolio with MacKinlay
and Pástor’s (2000) portfolio, performs well relative to other rules.
Our second study examines the role of fundamental analysis in portfolio
selection. Fundamental analysis assumes implicitly that asset prices mean-revert to their
fundamental values. We solve the instantaneous mean-variance portfolio choice problem
when asset prices mean-revert to their fundamentals and analyze how this meanreversion
feature affects the performance of the optimal portfolio. Our analytical results
show that the expected appraisal ratio of the optimal portfolio is increasing in the meanreversion
speed for a given stationary distribution of the mispricing and it is increasing
in the standard deviation of the stationary distribution for a given level of the meanreversion
speed. The contribution from dividends is positive, increasing in the dividend
yield and is tantamount to increasing the mean-reversion speed. Our numerical examples
indicate that fundamental analysis can be more helpful than practitioners’ performance shows. One implication of this is that it must be very challenging to obtain reasonable
forecasts of the mispricing.
Our third study provides a simulation analysis of the power of the bootstrap
approach for identifying skill among a large population of mutual funds. Unlike the
standard t-test, this approach does not require ex ante parametric assumption on fund
alphas and allows us to infer on the existence of genuine skill across a large sample of
fund managers. Its recent applications in mutual fund performance analysis have
produced strikingly different findings from those documented in the classical literature.
However, as far as we know, its power has not been subject to any rigorous statistical
analysis. We provide a Monte Carlo simulation analysis of the validity and power of this
method by applying it to evaluating the performance of hypothetical funds under
varieties of parameter assumptions. We find that this method can be misleading, which
is true regardless of using alpha estimates or their t-statistics. This makes the recent
findings dubious. The major problem with this method lies in the inappropriate use or
misinterpretation of what Fama and French (2010) call "likelihoods" in testing for
difference between realized and bootstrapped alphas at selected percentiles. We also
show that the variance decomposition and the Kolmogrov-Smirnov test can lead to
correct inferences on fund managers’ skill when likelihoods fail to do so.
Item Type: | Thesis (PhD) | ||||
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Subjects: | H Social Sciences > HG Finance | ||||
Library of Congress Subject Headings (LCSH): | Portfolio management | ||||
Official Date: | June 2011 | ||||
Dates: |
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Institution: | University of Warwick | ||||
Theses Department: | Warwick Business School | ||||
Thesis Type: | PhD | ||||
Publication Status: | Unpublished | ||||
Supervisor(s)/Advisor: | Hodges, S. D. (Stewart Dimont) | ||||
Sponsors: | Warwick Business School | ||||
Extent: | x, 205 leaves : charts | ||||
Language: | eng |
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