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 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...

We identify different roles traders play using data with trader identities for all transactions in SENSEX-index stocks on the Bombay Stock Exchange from January 2005 to December 2011. Individual day traders (IDT) are identified as "noise traders", who play an important role in the market...

I show that frequent batch auctions for stocks have the potential to reduce the severity of stock price crashes when they occur. For a given sequence of orders from a continuous electronic limit order book market, matching orders using one second apart batch auctions results in nearly the same...

Using over eight trillion observations of market data, we use a regression discontinuity design to analyze the effect of increasing the minimum price variation (MPV) for quoting equity securities in light of recent proposals to increase the MPV from $0.01 to $0.05. We show that a larger MPV...

This paper studies the welfare consequence of increasing trading speed in financial markets. We build and solve a dynamic trading model, in which traders receive private information of asset value over time and trade strategically with demand schedules in a sequence of double auctions. A...

We propose a model of dynamic trading where a strategic high frequency trader receives an imperfect signal about future order flows, and exploits his speed advantage to optimize his quoting policy. We determine the provision of liquidity, order cancellations, and impact on low frequency traders...

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,...

This paper relates jumps in high frequency stock prices to firm-level, industry and macroeconomic news, in the form of machine-readable releases from Thomson Reuters News Analytics. We find that most relevant news, both idiosyncratic and systematic, lead quickly to price jumps, as market...

We use a two-year panel of individual accounts in an S&P 500 index mutual fund to examine the trading and investment behavior of more than 91 thousand investors who have chosen a low-cost, passively managed vehicle for savings. This allows us to characterize investors' heterogeneity in terms of...