This paper presents a novel framework for adaptive portfolio management that
combines a dynamic Black-Litterman optimization with the general factor model
and Elastic Net regression. This integrated approach allows us to
systematically generate investors' views and mitigate potential estimation
errors. Our empirical results demonstrate that this combined approach can lead
to computational advantages as well as promising trading performances.Comment: 9 pages, 6 figure