Dynamic Stock Selection Strategies: A Structured Factor Model Framework

• Activity Number: 214
• Type: Invited
• Date/Time: Monday, August 2, 2010 : 2:00 PM to 3:50 PM
• Sponsor: Business and Economic Statistics Section
• Author(s): Hedibert Freitas Lopes*+ and Carlos Marinho Carvalho and Omar Aguilar
• Companies: The University of Chicago Booth School of Business and The University of Chicago Booth School of Business and Financial Engines
• Keywords: bayesian inference ; factor analysis ; factor loadings ; stochastic volatility ; portfolio allocation ; financial econometrics

Abstract

We propose a novel framework for dynamic portfolio selection. 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. Building on the work of Aguilar and West (2001), Carvalho et al. (2009) and Lopes et al. (2009), our models incorporate stock specific information and deliver economically interpretable factors that are used both, as a vehicle to estimate a potentially very large time-varying covariance matrix, and as a tool for stock selection. In a high-dimensional case study, we showcase the use of our methodology in the development of successful trading strategies and highlight the flexibility and power of dynamic factor models in these set-ups.