UC Berkeley

Studies of stock market crashes are as sparse as the occurrence of

crashes. The mainstream theoretical models on stock market crashes are rooted in rational

expectations equilibrium models and classical

market microstructure models. Compared to theoretical works, there are

even fewer works done on the empirical side. This is because most of

the theoretical models do not provide straightforward tests against

empirical data. Secondly, the relatively small

sample size (rare occurrence) of stock market crashes is always an obstacle for

empirical testing.

In this dissertation, we build a strategic trading model to link two major US stock market

crashes: the 1987 crash and the 2010 Flash Crash. We then provide

cross-sectional empirical evidence to verify our model hypothesis and evaluate price impact due

to the information asymmetry effect and the limited risk-bearing capacity effect. We use statistical learning

methods to compare our model based predictors with other predictors

for the maximum cross-sectional price drawdown of SP500 stocks during the 2010 Flash Crash and

check the robustness of our

findings.