A Hybrid Model for Stock Price Forecasting Integrating XGBoost and LSTM with Financial Indicators

Abstract This study introduces a strong hybrid ML architecture capable of forecasting stock movements in both short-term and multi-day scenarios. To overcome the difficulties of volatility, non-linearity, and time-dependence in stock markets, this study suggests a hybrid stock price prediction model that combines XGBoost and LSTM. XGBoost uses benchmark trends and structured financial data to predict the closing price for the following day, while LSTM captures long-term patterns for 22-day trend forecasting. Data is automatically retrieved from Yahoo Finance using a ticker-based system mapped through a custom CSV containing over 1,600 Indian stocks. Key features like Open, High, Low, Close, Volume, and index prices are used, alongside calculated financial ratios such as Beta, Sharpe Ratio, EPS, and P/E Ratio. Experimentation showed strong performance XGBoost achieved an R² above 0.99 and 98.25% directional accuracy, while LSTM yielded an RMSE of ₹37.71 and R² of 0.9762. The model successfully identifies short term trends and volatility bands, outperforming traditional methods like ARIMA and Linear Regression, offering a scalable, accurate solution for real-time stock forecasting in Indian equity markets.