Abstract
Abstract: This study examines how effectively volatility indices can forecast stock market movements by applying different machine learning techniques. It uses a dataset from Kaggle for the TCS Company, including variables such as 'Date,' 'Symbol,' 'Series,' 'Prev Close,' 'Open,' 'High,' 'Low,' 'Last,' 'Close,' 'VWAP,' 'Volume,' 'Turnover,' 'Deliverable Volume,' and '%Deliverable.' Various models were tested and evaluated based on metrics like the AUC-ROC Curve, Accuracy, Precision, Recall, F1-Score, Cross-Validation Accuracy, and the Confusion Matrix. The findings reveal that the Linear Regression (Classification model) surpasses other models in forecasting stock market directions with the highest levels of accuracy across all evaluation metrics. This highlights the potential of machine learning methods in leveraging volatility indices to accurately predict stock market trends
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