The Flash Crash: High‐Frequency Trading in an Electronic Market

We study intraday market intermediation in an electronic market before and during a period of large and temporary selling pressure. On May 6, 2010, U.S. financial markets experienced a systemic intraday event—the Flash Crash—where a large automated selling program was rapidly executed in the E‐mini S&P 500 stock index futures market. Using audit trail transaction‐level data for the E‐mini on May 6 and the previous three days, we find that the trading pattern of the most active nondesignated intraday intermediaries (classified as High‐Frequency Traders) did not change when prices fell during the Flash Crash.     

Beliefs Aggregation and Return Predictability

We study return predictability using a model of speculative trading among competitive traders who agree to disagree about the precision of private information. Although traders apply Bayes' Law consistently, returns are predictable. In addition to trading on long-term fundamental value, traders also trade on perceived short-term opportunities arising from foreseen future disagreement, as in a Keynesian beauty contest. Contradicting conventional wisdom, this short-term speculation dampens price fluctuations and generates time-series momentum. Model calibration shows quantitatively realistic patterns of return dynamics. Consistent with empirical evidence, our model predicts more pronounced momentum for stocks with higher trading volume.     

Equilibrium Bitcoin Pricing

We offer a general equilibrium analysis of cryptocurrency pricing. The fundamental value of the cryptocurrency is its stream of net transactional benefits, which depend on its future prices. This implies that, in addition to fundamentals, equilibrium prices reflect sunspots. This in turn implies multiple equilibria and extrinsic volatility, that is, cryptocurrency prices fluctuate even when fundamentals are constant. To match our model to the data, we construct indices measuring the net transactional benefits of Bitcoin. In our calibration, part of the variations in Bitcoin returns reflects changes in net transactional benefits, but a larger share reflects extrinsic volatility.