Framework for Forecasting and Timing Rare Equity Events

Abstract

Predicting rare events in financial markets is a major challenge, and the traditional models of predicting extreme price Features haven't worked well for drastic price changes. The study presents a novel model for timing and forecasting unusual equity returns, particularly a 30% rise in ten trading days. Additionally, the methodology employs a two-stage pipeline that combines an advanced machine learning classification algorithm or model, such as Gradient-Boosting, Random Forest, or Support Vector Machine, with a Generalized Additive Model (GAM) to interpret nonlinear feature extraction. The most challenging issues in financial prediction are also taken into account by the framework, such as class imbalance using unique prediction metrics like PR-AUC (Area Under the Percision-Recall Curve) and Precision at K and dynamic risk control using quantile-based take-profit and stop-loss strategies. With a PR-AUC of 0.72—much higher than that of conventional techniques—the analysis shows that XGBoost produces better results. By providing a solid, logical framework for forecasting infrequent occurrences in erratic markets, the study advances our understanding and has applications for algorithmic trading patterns and risk management instruments.