Predicting Crashes, a Model Comparison

In this paper, the crash prediction performances of the Price Earnings ratio (PE) model and Bond Stock Earnings Yield Differential (BSEYD) model are tested against the crash prediction performance of the Discrete Time Disorder Detection (DTDD) model. The PE and BSEYD models are economic models, depending on economic indicators and backed by asset pricing theory, therefore having a firm theoretical backing. Meanwhile the DTDD model is a purely stochastic model, not depending on any economic data. All models were applied to (subsamples of) the S&P 500 and to the Nasdaq Composite Index. This research showed that, over all, the DTDD model with a model horizon of 1500 trading days was the best performing, most consistent and most robust predictor of the considered models. Moreover, the DTDD model was easier to use due to the lack of dependency on underlying data such as earnings data or treasury notes.