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

46 Pages Posted: 14 May 2003

See all articles by Lorne D. Johnson

Lorne D. Johnson

Caxton Associates, L.L.C.

Georgios Sakoulis

Quantitative Management Associates (QMA) LLC

Date Written: 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.

Note: Previously titled "Maximizing Equity Market Sector Predictability in a Dynamic Time Varying Parameter Model"

Keywords: Asset Pricing, Kalman Filter, Capital Markets, Time Variation

JEL Classification: G1

Suggested Citation

Johnson, Lorne D. and Sakoulis, Georgios, Maximizing Equity Market Sector Predictability in a Bayesian Time Varying Parameter Model (July 2003). Available at SSRN: https://ssrn.com/abstract=396642 or http://dx.doi.org/10.2139/ssrn.396642

Lorne D. Johnson (Contact Author)

Caxton Associates, L.L.C. ( email )

667 Madison Avenue
New York, NY 10021
United States
718-499-7295 (Phone)

Georgios Sakoulis

Quantitative Management Associates (QMA) LLC ( email )

100 Mulberry Street
Gateway Center 2
Newark, NJ 07102
United States

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