We analyze a two-country model of trade in both legitimate and counterfeit products. Domestic firms own trademarks and establish reputations for delivering high-quality products in a steady-state equilibrium. Foreign suppliers export legitimate low-quality merchandise and counterfeits of...

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...

This paper develops and implements a new test to investigate whether sell-side analysts herd around the consensus when they make stock recommendations. Our empirical results support the herding hypothesis. Stock price reactions following recommendation revisions are stronger when the new...

A number of theories have been proposed to explain the medium-term momentum in stock returns identified by Jegadeesh and Titman (1993). We test one such theory--based on the gradual-information-diffusion model of Hong and Stein (1997)--and establish three key results. First, once one moves past...

We develop a dynamic equilibrium model of complex asset markets with endogenous entry and exit in which the investment technology of investors with more expertise is subject to less asset-specific risk. The joint equilibrium distribution of financial expertise and wealth then determines risk...

Historical data suggest that the base rate for a severe, single-day stock market crash is relatively low. Surveys of individual and institutional investors, conducted regularly over a 26-year period in the United States, show that they assess the probability to be much higher. We examine factors...

The literature has not unambiguously established that a positive alpha, as traditionally measured, means that an investor would want to buy a fund. However, when alpha is defined using the client's marginal utility function, a client faced with a positive alpha would generally want to buy. When...

We use stock exchange message data to quantify the negative aspect of high-frequency trading, known as "latency arbitrage." The key difference between message data and widely-familiar limit order book data is that message data contain attempts to trade or cancel that fail. This allows the...

Financial regulations and clientele segmentation explain the proliferation of order types on stock exchanges. Plain market and limit orders lose money, indicating that informed traders use complex orders. Fifty-seven percent of trading volume comes from non-routable orders, which are designed to...

Derivatives exposures across large financial institutions often contribute to - if not necessarily create - systemic risk. Current reporting standards for derivatives exposures are nevertheless inadequate for assessing these systemic risk contributions. In this paper, I explain how a...