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

Lorne D. Johnson** Caxton Associates New York, U.S.A.

Georgios Sakoulis J.P Morgan Fleming Asset Management London, U.K.

This Draft: July 2003

Abstract

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.

JEL classification: G12; C11

Keywords: Asset Pricing, Gibbs Sampling, Markov Switching, Behavioral Finance, Kalman Filter

_______________________________________________________________ *The opinions expressed in this paper are those of the authors and do not necessarily reflect those of Caxton Associates or J.P. Morgan Fleming Asset Management. We have received helpful comments from Eric Zivot and participants at the 10th Annual Forecasting Financial Markets Conference in Paris, France, June 2003. We are responsible for all remaining errors.

**Corresponding author, Caxton Associates, 667 Madison Avenue, New York, New York, 10021, E-mail: ljohnson@caxton.com, Tel.: 212-303-6118

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