We study the out-of-sample and post-publication return-predictability of 97 variables that academic studies show to predict cross-sectional stock returns. Portfolio returns are 26% lower out-of-sample and 58% lower post-publication. The out-of-sample decline is an upper bound estimate of data...

In this paper, we provide evidence on two alternative mechanisms of interaction between returns and volatilities: the leverage effect and the volatility feedback effect. We stress the importance of distinguishing between realized volatility and implied volatility, and find that implied...

Prediction market prices are often used as estimates of the probability of outcomes in future elections and referendums. I argue that this practice is often flawed, and I develop a model that empiricists can use to partially identify probabilities from prediction market prices. In the special...

Firm-level variables that predict cross-sectional stock returns, such as price-to-earnings and short interest, are often averaged and used to predict the time series of market returns. We extend this literature and limit the data-snooping bias by using a large population of the literature's...

We propose a nonparametric Bayesian approach for conducting inference on probabilistic surveys. We use this approach to study whether U.S. Survey of Professional Forecasters density projections for output growth and inflation are consistent with the noisy rational expectations hypothesis. We...

We show that log-periodic power-law (LPPL) functions are intrinsically very hard to fit to time series. This comes from their sloppiness, the squared residuals depending very much on some combinations of parameters and very little on other ones. The time of singularity that is supposed to give...

Using monthly data from 01/1985 to 12/2012, we find that the accounting valuation-based predictor introduced in Lee, Myers, and Swaminathan (1999) has excellent in-sample and out-of-sample predictive performance. Our finding suggests that the accounting valuation-based predictor does not suffer...

In this paper we investigate the predictive power of cross-sectional volatility, skewness and kurtosis for future stock returns. Adding to the work of Maio (2016), who finds cross-sectional volatility to forecast a decline in the equity premium with high predictive power in-sample as well as...

Existing research indicates that it is possible to forecast potential long-term returns in the S&P 500 for periods of more than 10 years using the cyclically adjusted price-to-earnings ratio (CAPE). This paper concludes that this relationship has also existed internationally in 17 MSCI Country...