Machine Learning Approaches to Stock Index Prediction

In response to the stock market's volatile nature, this research examines stock index forecasting evolution from traditional econometric models to advanced machine learning techniques. Market volatility, influenced by economic conditions, investor sentiment, and market interconnectedness, often renders linear models inadequate. While fundamental, conventional methods like multiple regression and ARMA face limitations with non-linear, noisy data, prompting development of machine learning approaches such as BP neural networks, SVM, and attention-enhanced CNN-LSTM models. These advanced techniques better capture data complexity, significantly improving prediction accuracy. The study explores both macro factors (economic linkages) and micro elements (herding behavior, loss aversion), alongside innovations like social media sentiment analysis that incorporate emotional and behavioral insights. Despite progress, challenges remain in balancing model complexity with accuracy and overcoming traditional statistical constraints in non-linear environments. This review emphasizes the necessity for integrated, fuzzy prediction models that consider multiple influences, with potential applications extending to other time series like commodity prices. These findings underscore the need for flexible, accurate forecasting methodologies to help authorities and investors navigate the unpredictable financial landscape.