Dynamic Stock Selection Strategies: A Structured Factor Model Framework

We propose a novel framework for estimating the time-varying covariation among stocks. Our work is inspired by asset pricing theory and associated developments in Financial Index Models. We work with a family of highly structured dynamic factor models that seek the extraction of the latent structure responsible for the cross-sectional covariation in a large set of financial securities. Our models incorporate stock specific information in the estimation of commonalities and deliver economically interpretable factors that are used both, as a vehicle to estimate large time-varying covariance matrix, and as a potential tool for stock selection in portfolio allocation problems. In an empirically oriented, high-dimensional case study, we showcase the use of our methodology and highlight the flexibility and power of the dynamic factor model framework in financial econometrics.