Machine Learning and Modelling Techniques for Financial Data Sciences: A Comparative Study and Hybrid Model Framework for Stock Market Prediction

Abstract

The presence of linear temporal dependencies and non-linear market behaviour poses a significant challenge in the field of financial time-series forecasting. The following study presents an in-depth comparison between statistical time-series models, ensemble machine learning models and a hybrid forecasting framework trained on historical NIFTY-50 stock price data. Baseline and statistical forecasting models, including Random Walk, AutoRegressive (5), and Moving Average, were initially used to understand the linear structure. Machine learning models, Random Forest and Gradient Boosting, were later implemented to learn the non-linear patterns that arose in the data. This led to a hybrid residual-learning approach, developed in conjunction with testing, where AR(5) captured linear structures in the data and machine learning models corrected the prediction errors caused by AR(5). The models used were evaluated using error metrics like RMSE, MAPE, MAE and directional accuracy by implementing a walk-forward validation framework. The experimental results indicate that the hybrid forecasting framework demonstrated comparatively stable forecasting behaviour under walk-forward validation while producing meaningful forecasts under realistic sequential prediction settings.