This study seeks to analyze the influence of major economic events, including recessions and interest rate hikes, on stock market performance. The primary tool utilized is the Market Crisis Prediction System, which was developed and is available on GitHub. The analysis is based on a dataset obtained from Kaggle, which encompasses stock data from over 9,000 companies, spanning from 1962 to 2024. The research employs time series methods, event studies, and regression models to detect patterns and assess the impact of these events. Furthermore, machine learning models, such as XGBoost and Long Short-Term Memory (LSTM), are incorporated to enhance the predictive accuracy of the system. Evaluation metrics such as R-squared, mean squared error (MSE), and various visualizations are employed to assess the models' effectiveness. Ultimately, the goal is to provide insights that can help investors and policymakers better anticipate and respond to future market crises.
