Prediction of Financial Bubbles and Back-Testing of a Trading Strategy

The prediction of financial bubbles and subsequent market crashes is critical in financial markets. This study uses the Log-Periodic Power Law Singularity (LPPLS) model to analyze Amazon stock prices from 1994 to 2000, confirming the model’s effectiveness in detecting and forecasting financial bubbles. By fitting the LPPLS model to historical data, we caught the super-exponential growth and log-periodic oscillations that characterize bubble behavior, allowing us to predict when market corrections are likely. The back-testing of the LPPLS model demonstrates its potential as a useful tool in financial analysis and trading strategy creation. Our findings show that the LPPLS model can effectively function as an early warning system, allowing traders to change their portfolios proactively to reduce risks and maximize profits. The LPPLS model’s successful application to Amazon stock prices justifies its future usage in real-time bubble detection and risk management. It provides traders with the knowledge they need to navigate tumultuous markets and make smart investment decisions.