In this paper, we forecast industry returns out-of-sample using the cross-section of book-to-market ratios and investigate whether investors can exploit this predictability in portfolio allocation. Cash-flow and return forecasting regressions show that cross-industry book-to-market ratios...

What predicts returns on assets with "hard-to-value" fundamentals, such as Bitcoin and stocks in new industries? We propose an equilibrium model that shows how rational learning enables return predictability through technical analysis. We document that ratios of prices to their moving averages...

Portfolio optimization often struggles in realistic out-of-sample contexts. We de-constructthis stylized fact, comparing historical forecasts of portfolio optimization inputs withsubsequent out of sample values. We confirm that historical forecasts are imprecise guidesof subsequent values but...

There is a generalized conviction that variation in dividend yields is exclusively related to expected returns and not to expected dividend growth - e.g. Cochrane's presidential address (Cochrane (2011)). We show that this pattern, although valid for the aggregate stock market, is not true for...

We create a market-wide measure of dispersion in options investors' expectations by aggregating across all stocks the dispersion in trading volume across moneynesses (DISP). DISP exhibits strong negative predictive power for future market returns and its information content is not subsumed by...

We examine the predictive ability of the aggregate earnings yield for market returns and earnings growth by estimating variance decompositions at multiple horizons. Based on weighted long-horizon regressions, we find that most of the variation in the earnings yield is due to return...