A Hybrid Deep Learning Framework for Stock Volatility Prediction Using Technical, Fundamental and Financial News Sentiment Features

Abstract

Forecasting stock volatilities is a tough problem because of the nonlinear and dynamic character of the financial markets. If you want to make good decisions about investments, about how to organize a portfolio and about risk, you need good forecasts. This paper presents a hybrid deep learning approach for predicting stock volatility by merging technical indicators, underlying financial factors, and financial news emotion. Historical stock market data from 29 May 2013 to 28 March 2024 were acquired from Yahoo Finance. Daily sentiment ratings were extracted from financial news items using natural language processing algorithms. After data pre-processing, feature engineering and standardization, a hybrid dataset was built including both quantitative and qualitative financial data. We used Random Forest (RF) and XGBoost as baseline machine learning models and created a LSTM-based hybrid architecture to capture the temporal dependencies and nonlinear interactions in financial time-series data. To avoid data leaks and get realistic forecasts, a chronological train-validation-test technique was used. Several regression metrics such as MAE, MSE, RMSE, MAPE and R2 were used to evaluate the proposed framework. Experimental results indicate that the integration of financial news sentiment with technical and fundamental features can improve the prediction accuracy and provide a more comprehensive understanding of market dynamics compared to traditional approaches. The suggested methodology provides a realistic and comprehensive solution for intelligent stock volatility forecasting, and may enable investors, portfolio managers, and financial analysts to make educated financial choices.