Forecasting Stock Market Volatility Using XGBoost: A Time Series Analysis
Tripti Sharma, Sanjeev Kumar Prasad, Shreeya Prasad, Indu Verma, Anupama Sharma · 2024
Forecasting stock market volatility presents significant challenges and opportunities for both practitioners and researchers in the financial sector. This paper explores the application of eXtreme Gradient Boosting (XGBoost), an advanced ensemble machine learning technique, to predict stock market volatility using time series data from the National Stock Exchange of India (NSEI). Our methodology utilizes past price data containing open, high, low, and close prices, along with capacity and adjusted close values, to capture the underlying patterns indicative of future market movements. We preprocess this data using techniques like normalization and feature engineering to enhance the predictive capabilities of our model. The XGBoost model is upgraded through extensive hyperparameter tuning and is considered in contrast to customary measurements like Mean Squared Error (MSE), Root Mean Squared Blunder (RMSE), and the R2Score to survey its precision and adequacy. Our results demonstrate that XGBoost can effectively capture the complexities and non-linear relationships inherent in stock price movements, outperforming standard benchmark models. The findings suggest that machine learning models, particularly those based on tree ensemble methods like XGBoost, provide a robust tool for financial analysts seeking to mitigate risks and capture opportunities in volatile markets. This paper contributes to the ongoing discourse in financial econometrics by confirming the viability of advanced machine learning techniques in forecasting market dynamics, thus offering valuable insights for both theoretical advancement and practical application in financial forecasting.