A Behavioral Defense of the Fed Model

Posted: 23 Jan 2007

Date Written: January 22, 2007


In any balanced portfolio, investors need to assess the relative attractiveness of equities and bonds, the usual asset classes "competing" for funds. A tool widespread used in asset allocation decisions is the so-called FED. In my view, the critique of the FED model has not always been fair and this paper therefore presents a behavioral defense of the FED model. By combining the FED model and the CAPM model, it becomes evident that the FED model is able to detect time variation in the equity risk premium and behavioral biases in long-term earnings growth expectations. Assuming that share prices are the sum of a fundamental value element and a noise/sentiment element, then the use of statistical tools such as confidence intervals will reduce potential decision biases caused by noise/sentiment and thereby improve the predictive power of the FED model. The results in this paper suggest that the FED model does a better job at predicting relative returns of stocks versus bonds than at predicting absolute stock returns. By basing decisions only on data points outside a predetermined confidence interval, the predictive power is increased manifold, enhancing potential gross returns and reducing transaction costs. The optimal prediction horizon for the FED model appears to be 12-36 months, somewhat shorter than the 5-10 year horizon found for the P/E mean-reversion model a la Campbell-Shiller. Thus, the FED model and the long-term P/E mean reversion model are complementary models of return prediction, not competing model.

Keywords: Behavioral Finance, FED model, Return Predictability

JEL Classification: C53, G11, G12

Suggested Citation

Clemens, Michael, A Behavioral Defense of the Fed Model (January 22, 2007). Available at SSRN: https://ssrn.com/abstract=958800 or http://dx.doi.org/10.2139/ssrn.958800

Michael Clemens (Contact Author)

BankInvest Group ( email )


Do you have negative results from your research you’d like to share?

Paper statistics

Abstract Views
PlumX Metrics