Predicting the Equity Premium Out of Sample: Can Anything Beat the Historical Average?
John Campbell () and
Samuel B. Thompson
No 11468, NBER Working Papers from National Bureau of Economic Research, Inc
A number of variables are correlated with subsequent returns on the aggregate US stock market in the 20th Century. Some of these variables are stock market valuation ratios, others reflect patterns in corporate finance or the levels of short- and long-term interest rates. Amit Goyal and Ivo Welch (2004) have argued that in-sample correlations conceal a systematic failure of these variables out of sample: None are able to beat a simple forecast based on the historical average stock return. In this note we show that forecasting variables with significant forecasting power in-sample generally have a better out-of-sample performance than a forecast based on the historical average return, once sensible restrictions are imposed on the signs of coefficients and return forecasts. The out-of-sample predictive power is small, but we find that it is economically meaningful. We also show that a variable is quite likely to have poor out-of-sample performance for an extended period of time even when the variable genuinely predicts returns with a stable coefficient.
JEL-codes: G1 (search for similar items in EconPapers)
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Published as Campbell, John Y. and Samuel B. Thompson. "Predicting Excess Stock Returns Out of Sample: Can anything Beat the Historical Average?" Review of Financial Studies 21 (July 2008): 1509-1531.
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