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.

We estimate the stock’s likelihood of extreme returns by measuring the extent to which the stock’s trades are correlated with market-wide and industry-wide trades during normal times, referred to as herding. We find that stocks whose trades herd most with aggregate-level trades experience most negative (positive) returns during market crashes (booms). While herding generates extreme returns in both sides, investors appear to demand compensation for the possibility of extreme low returns. This is the case even when we control for standard asset pricing variables and other tail risk proxies.

In this article we develop a model to analyze patent-protected R&D investment projects when there is (imperfect) competition in the development and marketing of the resulting product. The competitive interactions that occur substantially complicate the solution of the problem since the decision maker has to take into account not only the factors that affect her/his own decisions, but also the factors that affect the decisions of the other investors. The real options framework utilized to deal with investments under uncertainty is extended to incorporate the game theoretic concepts required to deal with these interactions. Implementation of the model shows that competition in R&D, in general, not only increases production and reduces prices, but also shortens the time of developing the product and increases the probability of a successful development. These benefits to society are countered by increased total investment costs in R&D and lower aggregate value of the R&D investment projects.