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

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Kaiser, L., Veress, A., & Menichetti, M. J. (2012). Enhanced Mean-Variance Portfolios: A Controlled Integration of Quantitative Predictors. Presented at the Research Seminar at City University Hong Kong, Hong Kong.

Publication type

Presentation at Scholarly Conference


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.


Quantitative Investment Management and Portfolio Optimisation
PhD-Thesis, March 2011 until February 2015 (finished)

Overall, the proposed dissertation project aims to contribute to academic literature by identifying research gaps in the field of quantitative investment management and answering the respective by ... more ...


Organizational Units

  • Institute for Financial Services
  • Chair in Business Administration, Banking and Financial Management
  • Banking and Finance