We develop a simulation-based procedure to test for stock return predictability with multiple regressors. The process governing the regressors is left completely free and the test procedure remains valid in small samples even in the presence of non-normalities and GARCH-type effects in the stock...

We build an equilibrium model to explain why stock return predictability concentrates in bad times. The key feature is that investors use different forecasting models, and hence assess uncertainty differently. As economic conditions deteriorate, uncertainty rises and investors' opinions...

We examine whether option prices correct for predictable bias in stock prices associated with accounting anomalies. Evidence from put-call parity violations suggests that they do not. Rather, option prices accurately track contemporaneous stock prices. Further analysis suggests that high costs...

An analysis of about 300000 earnings forecasts, created by 18000 individual forecasters for earnings of over 300 S&P listed firms, shows that these forecasts are predictable to a large extent using a statistical model that includes publicly available information. When we focus on the...

Average skewness, which is defined as the average of monthly skewness values across firms, performs well at predicting future market returns. This result still holds after controlling for the size or liquidity of the firms or for current business cycle conditions. We also find that average...

Sentiment should exhibit its strongest effects on asset prices at times when valuations are most subjective. Consistent with this hypothesis, we show that a one-standard-deviation increase in aggregate uncertainty amplifies the predictive ability of sentiment for market returns by two to four...

In this paper we document the asymmetric role that the U.S. stock market plays in the international predictability of excess stock returns during recession and expansion periods. Most of the positive evidence accrues during the periods of recessions in the United States. During the expansions...

The level of daily stock returns is generally regarded as unpredictable. Instead of the level, we focus on the signs of these returns and generate forecasts using various statistical classification techniques, such as logistic regression, generalized additive models, or neural networks. The...

Aggregate implied volatility spread (IVS), defined as the cross-sectional average difference in the implied volatilities of at-the-money call and put equity options, is significantly and positively related to future stock market returns at daily, weekly, monthly, to semiannual horizons. This...

We use weekly survey data on short-term and medium-term sentiment of German investors in order to study the causal relationship between investors' mood and subsequent stock price changes. In contrast to extant literature for other countries, a tri-variate vector autoregression for short-run...