Seminar Details

Seminar Details


Dec 9

4:00 pm

Maximizing Equity Market Sector Predictability in a Bayesian Time-Varying Parameter Model

Georgios Sakoulis


UBS O\'Connor

Computational Finance Seminar

A large body of evidence has emerged in recent studies confirming that macroeconomic factors play an important role in determining investor risk premia and the ultimate path of equity returns. This paper illustrates how widely tested financial and economic variables from these studies can be employed in a time varying dynamic sector allocation model for U.S. equities. The model developed here is evaluated using Bayesian parameter estimation and model selection criteria. We find that using the Kalman filter to estimate time varying sensitivities to predetermined risk factors results in significantly improved sector return predictability over static or rolling parameter specifications. A simple trading strategy developed here using Kalman filter predicted returns as input provides for potentially robust long run profit opportunities.

* Georgios is a Director in the Global Quantitative Strategies team at UBS O'Connor based in Chicago. He joined the team in December 2003. He is responsible for developing quantitative, equity-based strategies and is currently co-managing a global managed futures trading book. Prior to joining UBS, Georgios was an associate quantitative portfolio manager with J.P. Morgan Fleming Asset Management in London, where he managed a tactical asset allocation product overlaid on a sector allocation strategy? Georgios earned a BSc in Statistics and a BA in Economics from San Francisco State University and an MA and PhD in Economics from the University of Washington.