Some market crashes occur because of significant imbalances in demand and supply. Conventional models fail to explain the large magnitudes of price declines. We propose a unified structural framework for explaining crashes, based on the insights of market microstructure invariance. A proper adjustment for differences in business time across markets leads to predictions which are different from conventional wisdom and consistent with observed price changes during the 1987 market crash and the 2008 sales by Société Générale. Somewhat larger-than-predicted price drops during 1987 and 2010 flash crashes may have been exacerbated by too rapid selling. Somewhat smaller-than-predicted price decline during the 1929 crash may be due to slower selling and perhaps better resiliency of less integrated markets.

This paper estimates an equilibrium model of stock price behaviour in which changes in exponentially de-trended dividends and prices are normally distributed and exogenous “noise traders” interact with “smart-money” investors who have constant absolute risk aversion. The model can explain the volatility and predictability of U.S. stock returns in the period 1871–1986 using either a low discount rate (4% or below) and a large constant risk discount on the stock price, or a higher discount rate (5% or above) and noise trading correlated with fundamentals. The data are not well able to distinguish between these explanations.

We describe a symmetric continuous-time model of trading among relatively overconfident, oligopolistic informed traders with exponential utility. Traders agree to disagree about the precisions of their continuous flows of Gaussian private information. The price depends on a trader’s inventory (permanent price impact) and the derivative of a trader’s inventory (temporary price impact). More disagreement makes the market more liquid; without enough disagreement, there is no trade. Target inventories mean-revert at the same rate as private signals. Actual inventories smoothly adjust towards target inventories at an endogenous rate which increases with disagreement. Faster-than-equilibrium trading generates “flash crashes” by increasing temporary price impact. A “Keynesian beauty contest” dampens price fluctuations.

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.

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.