The timing of the popping: using the log-periodic power law model to predict the bursting of bubbles on financial markets

The occurrence and unpredictability of speculative bubbles on financial markets, and their accompanying crashes, have confounded economists and economic historians worldwide. We examine the ability of the log-periodic power law model (LPPL-model) to accurately predict the end dates of speculative bubbles on financial markets through modeling of asset price dynamics on a selection of historical bubbles. The method is based on a nonlinear least squares estimation that yields predictions of when the bubble will change regime. Previous studies have only presented results where the predictions turn out to be successful. This study is the first to highlight both the potential and the limitations of the LPPL-model. We find evidence that supports the characteristic patterns as proposed by the LPPL-framework leading up to the change in regime; asset prices during bubble periods seem to oscillate around a faster-than-exponential growth. In most cases the estimation yields accurate predictions, although we conclude that the predictions are quite dependent on the point in time at which they are conducted. We also find that the end of a speculative bubble seems to be influenced by both endogenous speculative growth and exogenous factors. For this reason we propose a new way of interpreting the predictions of the model, where the end dates should be interpreted as the start of a time period where the asset prices are especially sensitive to exogenous events. We propose that negative news during this time period results in a regime shift and the bursting of the bubble. Thus, the model has the ability to predict sensitivity to exogenous events ex ante.