@article {Kaiser28, author = {Lars Kaiser and Marco J. Menichetti and Aron Veress}, title = {Enhanced Mean{\textendash}Variance Portfolios: A Controlled Integration of Quantitative Predictors }, volume = {40}, number = {4}, pages = {28--41}, year = {2014}, doi = {10.3905/jpm.2014.40.4.028}, publisher = {Institutional Investor Journals Umbrella}, abstract = {The intuitiveness and practicality of mean{\textendash}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.TOPICS: Portfolio construction, statistical methods, performance measurement}, issn = {0095-4918}, URL = {https://jpm.pm-research.com/content/40/4/28}, eprint = {https://jpm.pm-research.com/content/40/4/28.full.pdf}, journal = {The Journal of Portfolio Management} }