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 test and compare the effects of introduction of two new financial information technologies, EDGAR and XBRL, on well-known asset pricing anomalies often attributed to mispricing. EDGAR facilitates easier access to public accounting information about public firms; XBRL reduces the cost of...

We revisit the relationship between firm competition and real efficiency in a novel setting with informational feedback from financial markets. Although intensified competition can decrease market concentration in production, it reduces the value of proprietary information (e.g., market...

Using text from 200 million pages of 13,000 US local newspapers and machine learning methods, we construct a 170-year-long measure of economic sentiment at the country and state levels, that expands existing measures in both the time series (by more than a century) and the cross-section. Our...

Standard theory implies that the discount rates used by firms in investment decisions (i.e., their required returns to capital) determine investment and transmit financial shocks to the real economy. However, there exists little evidence on how firms' discount rates change over time and affect...

We document a causal effect of social interactions on investor behavior using the number of local soccer games as a measure of social interaction intensity. Social transmission is identifiable in buy but not sell trades. The effect of Social Interaction Intensity (SII) on the sensitivity of...

Browser data from an approximately representative sample of individual investors offers a detailed account of their search for information, including how much time they spend on stock research, which stocks they research, what categories of information they seek, and when they gather information...

We measure investors' short- and long-term stock-return expectations using both options and survey data. These expectations at different horizons reveal what investors think their own short-term expectations will be in the future, or forward return expectations. While contemporaneous short-term...

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,...