Disequilibrating macro shocks affect different firms' prospects differently, increasing idiosyncratic variation in forward-looking stock returns before affecting economic growth. Consistent with most such shocks from 1947 to 2020 enhancing productivity, increased idiosyncratic stock return...

Cross-sectional forecasts of conservative and optimistic biases in analyst earnings estimates predict a stock's future returns, especially for firms that are hard to value. Trading strategies--whether based on the component of analyst bias that is correlated with major return anomalies or the...

We study how the social transmission of public news influences investors' beliefs and securities markets. Using an extensive dataset to measure investor social networks, we find that earnings announcements from firms in higher-centrality locations generate stronger immediate price and trading...

Trading in cryptocurrencies has grown rapidly over the last decade, primarily dominated by retail investors. Using a dataset of 200,000 retail traders from eToro, we show that they have a different model of the underlying price dynamics in cryptocurrencies relative to other assets. Retail...

We examine the impacts of ethical declarations on market transactions through a controlled laboratory experiment, where privately-informed sellers issue a public report prior to a first-price auction. We find that while signing an ethical statement does not reduce misreporting by sellers, it...

Using transaction data from a large non-fungible token (NFT) trading platform, this paper examines how the behavioral bias of selection-neglect interacts with extrapolative beliefs, accelerating the boom and delaying the crash in the recent NFT bubble. We show that the price-volume relationship...

We address the joint hypothesis problem in cross-sectional asset pricing by using measured analyst expectations of earnings growth. We construct a firm-level measure of Expectations Based Returns (EBRs) that uses analyst forecast errors and revisions and shuts down any cross-sectional...

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance--in terms of SDF Sharpe ratio and test asset pricing errors--is improving in model parameterization (or "complexity"). Our empirical findings verify the...

This paper studies the predictability of ultra high-frequency stock returns and durations to relevant price, volume and transactions events, using machine learning methods. We find that, contrary to low frequency and long horizon returns, where predictability is rare and inconsistent,...

Using friendship data from Facebook, we study the effects of three aspects of social capital on household financial behavior. We find that the most important measure of social capital in explaining stock market and saving participation is Economic Connectedness, defined as the fraction of one's...