# Predictive Power of the Gap Between Stock Earnings Yield and T-note Yield

April 2, 2009 • **Posted in** Fed Model

Does the gap between the aggregate stock market forward-looking earnings yield and the yield on 10-year Treasury notes (T-note) predict future stock market and bond returns? In the November 2008 update to his paper entitled “The FED Model and Expected Asset Returns”, Paulo Maio examines the statistical and economic significance of the Fed model as an indicator of future stock market and bond returns. Said differently, he investigates whether mean reversion in stock and bond yields results in mean reversion of the yield gap. Using monthly data for a broad U.S. stock index and T-notes, and for contemporaneous benchmark indicators, over the period July 1954 through December 2003, *he concludes that:*

- The yield gap reasonably outperforms simple extrapolations and alternative forecasting variables (earnings yield, bond yield and dividend yield) in out-of-sample predictions of both stock market returns and T-note returns.
- Yield gap predictive power is strongest for stock returns at forecast horizons up to a year, declining gradually at longer horizons.
- The yield gap has greater predictive power for equal-weighted than value-weighted excess stock market returns, suggesting greater applicability to small capitalization than large capitalization stocks.
- The yield gap is most useful for predicting positive (negative) stock market (T-note) returns.
- Out-of-sample yield gap predictive power is economically meaningful, as indicated by an boost in Sharpe ratio, when used in active trading strategies at one month forecast horizons. Its value comes from reduced portfolio volatility, not higher returns.

In summary, *investors may be able to use the Fed Model to enhance risk-adjusted returns via reduced portfolio volatility.*

It would have been interesting to extend the analysis to 2004-2008, during which period many variable relationships shifted.

It costs less than a single trading commission. Learn more here.