We empirically test the prediction of that green firms outperform brown firms when concerns
about climate change increase unexpectedly, using data for S&P 500 companies from …

We construct a robust stochastic discount factor (SDF) summarizing the joint explanatory
power of a large number of cross-sectional stock return predictors. Our method achieves …

We propose a theoretically motivated factor model based on investor psychology and
assess its ability to explain the cross-section of US equity returns. Our factor model …

We study the after-trading-cost performance of anomalies and the effectiveness of
transaction cost mitigation techniques. Introducing a buy/hold spread, with more stringent …

Hundreds of papers and factors attempt to explain the cross-section of expected returns.
Given this extensive data mining, it does not make sense to use the usual criteria for …

Are anomalies strongest when investor sentiment or limits of arbitrage are considered to be
greatest? We empirically explore these theoretically deducted predictions. We first identify …

Using novel data from a crowdsourcing platform for ranking stocks, we investigate how
investors form expectations about stock returns over the next week. We find that investors …

We present evidence on the asset pricing implications of salience theory. In our model,
investors overweight salient past returns when forming expectations about future returns …

By applying machine learning to the accurate and cost-effective classification of photos
based on sentiment, we introduce a daily market-level investor sentiment index (Photo …

Factors display strong cross-sectional momentum that subsumes momentum in industries
and other portfolio characteristics. The profits of all these momentum strategies—based on …