Applying a well-established neuroscience framework to the issue of investor perception of volatility, we propose that after prolonged exposure to high volatility, investors tend to underestimate volatility due to adaptation to the high volatility, and vice versa. Using a combination of field and laboratory tests, we find strong support for this hypothesis. The evidence suggests that this neurobiologically-grounded perceptual bias can cause distortions of asset prices in sophisticated and liquid financial markets.

We examine returns, order flow, and market conditions in the minutes before, during, and after NYSE and Nasdaq short sales. We find two distinct types of short sales: those that provide liquidity, and those that demand it. Liquidity-supplying shorts are strongly contrarian at intraday horizons. They trade when spreads are unusually wide, facing greater adverse selection. Liquidity-demanding shorts trade when spreads are narrow and tend to follow short-term price declines. These results support a competitive rational expectations model where both market-makers and informed traders short, indicating that these two shorting types are integral to both price discovery and liquidity provision.

Cryptocurrencies are among the largest unregulated markets in the world. We find that approximately one-quarter of bitcoin users are involved in illegal activity. We estimate that around $76 billion of illegal activity per year involve bitcoin (46% of bitcoin transactions), which is close to the scale of the U.S. and European markets for illegal drugs. The illegal share of bitcoin activity declines with mainstream interest in bitcoin and with the emergence of more opaque cryptocurrencies. The techniques developed in this paper have applications in cryptocurrency surveillance. Our findings suggest that cryptocurrencies are transforming the black markets by enabling “black e-commerce.” Received June 1, 2017; editorial decision December 8, 2018 by Editor Andrew Karolyi.

We investigate the new reality of exchange-traded funds (ETFs). We show that most ETFs are active investments in form (designed to generate alpha) or function (serve as building blocks of active portfolios). We define a new activeness index to capture these dimensions, finding that the cross-section of ETFs is now increasingly characterized by highly active investment vehicles. Active-in-form ETFs have positive flow-performance sensitivity, charge the highest fees among ETFs, and have high within-portfolio turnover. Active-in-function ETFs have more concentrated holdings, less within-portfolio turnover, but higher turnover in the secondary market. We show how more active ETFs are gaining market share over less active ETFs, leading to competitive fee pressure both within the ETF space and across the investment management industry. We suggest that the growing activeness of ETFs may assuage concerns about ETFs harming price discovery.

We develop a return variance decomposition model to distinguish the roles of different types of information and noise in stock price movements. We disentangle four components: noise, private firm-specific information revealed through trading, firm-specific information revealed through public sources and market-wide information. Overall, we find that 31% of the return variance is from noise, 24% from private firm-specific information, 37% from public firm-specific information and 8% from market-wide information. Since the mid-1990s, there has been a dramatic decline in noise and an increase in firm-specific information, consistent with increasing market efficiency. The Internet Appendix that accompanies this paper can be obtained here: https://bit.ly/3FcV9UR