Bali, Turin G., Stephen J. Brown, and Yi Tang, “Cross-Sectional Dispersion in Economic Forecasts and Expected Stock Returns,” The* American Finance Association 75th Annual Meeting, Boston (2015).*

**Purpose:** To show that economic uncertainty is an economically and statistically significant driver of the cross-section of stock returns.

Motivation: In the ICAPM world, investors care not only about the expected payoff of their investments, but also about their portfolios’ covariances with state variables affecting both future consumption and opportunities for investment.

Data/Methods:

- Measure economic uncertainty using
- the dispersion of forecasts from the Survey of Professional Forecasters
- real GDP growth and real GDP level
- log (75th pctl forecast / 25th pctl forecast) * 100

- cross-sectional dispersion in forecasts for output, inflation, and unemployment

- the dispersion of forecasts from the Survey of Professional Forecasters
- Fama-MacBeth regressions
- Sort into deciles based on market beta.
- Find time-varying “uncertainty betas” of stocks using rolling regressions of stock excess returns on the uncertainty measure, and sort into subdeciles.

- Economic Uncertainty Index
- Use Principal Component Analysis to find the common component among seven different proxies for economic uncertainty.

Results:

- Covariance with economic uncertainty is significantly negatively correlated with higher returns, after controlling for market beta, size, book-to-market, momentum, short-term reversal, illiquidity, co-skewness, idiosyncratic volatility, and the dispersion of analyst forecasts.
- The beta of the proposed “uncertainty index” appears able to significantly predict future stock returns.