Comprehensive Performance Assessment of Machine Learning Techniques in NIFTY-50 Stock Index Prediction

In the ever-changing landscape of global finance, the precise forecasting of stock indices has emerged as a pivotal concern for both investors and financial analysts. This study explores the effectiveness of multiple machine learning (ML) based techniques, including Linear Regression (LR), Support Vector Regression (SVR), Adaboost, Random Forrest (RF) and Feedforward Neural Network (FNN), implemented within the MATLAB framework. MATLAB, a versatile and powerful platform, is employed for deploying and evaluating predictive models. Adaboost, known for its ensemble learning approach, iteratively combines weak learners to construct a robust prediction model. Simultaneously, model optimization is achieved through techniques such as grid search. The study utilizes several technical indicators calculated from the historical NIFTY-50 index data to train and validate the predictive models. Performance metrics such as RMSE, MAE, MAPE, R2 errors are employed to evaluate the efficacy of the algorithms. The research results contribute to the growing knowledge base in financial prediction, showcasing the practical application of ML techniques within the MATLAB Software framework. The results not only provide a basis for improved predictions in the NIFTY-50 index but also offer valuable insights for future research in the domain of algorithmic trading and financial market analysis.