Efficient Prediction of Excess Returns

50 Pages Posted: 16 Jul 2008 Last revised: 21 Mar 2021

See all articles by Jon Faust

Jon Faust

Board of Governors of the Federal Reserve System

Jonathan H. Wright

Johns Hopkins University - Department of Economics

Date Written: July 2008

Abstract

It is well known that augmenting a standard linear regression model with variables that are correlated with the error term but uncorrelated with the original regressors will increase asymptotic efficiency of the original coefficients. We argue that in the context of predicting excess returns, valid augmenting variables exist and are likely to yield substantial gains in estimation efficiency and, hence, predictive accuracy. The proposed augmenting variables are ex post measures of an unforecastable component of excess returns: ex post errors from macroeconomic survey forecasts and the surprise components of asset price movements around macroeconomic news announcements. These "surprises" cannot be used directly in forecasting--they are not observed at the time that the forecast is made--but can nonetheless improve forecasting accuracy by reducing parameter estimation uncertainty. We derive formal results about the benefits and limits of this approach and apply it to standard examples of forecasting excess bond and equity returns. We find substantial improvements in out-of-sample forecast accuracy for standard excess bond return regressions; gains for forecasting excess stock returns are much smaller.

Suggested Citation

Faust, Jon and Wright, Jonathan H., Efficient Prediction of Excess Returns (July 2008). NBER Working Paper No. w14169, Available at SSRN: https://ssrn.com/abstract=1161092

Jon Faust (Contact Author)

Board of Governors of the Federal Reserve System ( email )

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Jonathan H. Wright

Johns Hopkins University - Department of Economics ( email )

3400 Charles Street
Baltimore, MD 21218-2685
United States

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