More than ex-post fitting: log-periodic power law and its AI-based classification

The log-periodic power law (LPPL) model can be used to model the formation and evolution of endogenous bubbles in asset prices to predict the critical time of bubble bursts; however, it has a limitation in that the parameter interpretation and reliability are contingent on the researchers’ subjective judgments. We introduce an artificial intelligence-based framework to address the reliability issue of the LPPL model. Specifically, we use an AI classification model to produce a reliability score, which is then incorporated into a novel risk metric designed to provide a more reliable early warning indicator of potential financial crashes. To train classification models, we created over 13 million labeled LPPL parameter sets estimated for daily closing prices of large-cap stocks in the United States (covering 24 years). We consider a simple formula to reformulate the crash information provided by the LPPL outcomes to create our risk metric, distance-to-crash with AI (DTCAI). When interpreting the LPPL results through our proposed risk metric, greater confidence can be placed on the predicted crash time as the metric inherently incorporates reliability information. Finally, the economic significance of the DTCAI metric is subsequently demonstrated through its application in dynamic portfolio-rebalancing strategies. The results show that strategies guided by our DTCAI metric significantly outperform the benchmark buy-and-hold strategy, regardless of the investor’s risk preference. This study suggests that integrating artificial intelligence into financial models like LPPL can enhance the accuracy of bubble detection and risk quantification, offering a more robust approach to anticipating financial crises and optimizing investment strategies.