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The Journal of Portfolio Management

The Journal of Portfolio Management

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Article

Enhanced Mean–Variance Portfolios:
A Controlled Integration of Quantitative Predictors

Lars Kaiser, Marco J. Menichetti and Aron Veress
The Journal of Portfolio Management Summer 2014, 40 (4) 28-41; DOI: https://doi.org/10.3905/jpm.2014.40.4.028
Lars Kaiser
is a doctoral candidate at the University of Liechtenstein in Vaduz, Liechtenstein. lars.kaiser@uni.li
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Marco J. Menichetti
is a professor of business administration, banking and financial management at the University of Liechtenstein in Vaduz, Liechtenstein. marco.menichetti@uni.li
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Aron Veress
is head of risk and operations at Loviit AG in Ruggell, Liechtenstein. aron.veress@alumni.uni.li
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Abstract

The intuitiveness and practicality of mean–variance portfolios largely depend on the accuracy of moment estimates, which are subject to large estimation errors and are conditional on time. The authors propose a model that accounts for factor dynamics in a Bayesian setting, in which they endogenously derive the effect of estimation accuracy on the posterior distribution from a linear predictive regression model. By doing so, they capture upside return potential for periods of high factor-explained variance, while constraining downside risk for periods of low predictive quality. Results are robust in a simulation and an empirical setting.

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The Journal of Portfolio Management: 40 (4)
The Journal of Portfolio Management
Vol. 40, Issue 4
Summer 2014
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Enhanced Mean–Variance Portfolios:
A Controlled Integration of Quantitative Predictors
Lars Kaiser, Marco J. Menichetti, Aron Veress
The Journal of Portfolio Management Jul 2014, 40 (4) 28-41; DOI: 10.3905/jpm.2014.40.4.028

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Enhanced Mean–Variance Portfolios:
A Controlled Integration of Quantitative Predictors
Lars Kaiser, Marco J. Menichetti, Aron Veress
The Journal of Portfolio Management Jul 2014, 40 (4) 28-41; DOI: 10.3905/jpm.2014.40.4.028
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