A Comprehensive Review of Stock Index Prediction Methods: From Traditional Econometrics to Deep Learning with Attention Mechanisms

Abstract

In the era of rapid digitalization and data-driven decision-making, financial forecasting has emerged as a critical area of research and application. Stock index prediction, in particular, plays a vital role in guiding investment strategies, managing risks, and shaping economic policies. With the increasing availability of high-frequency and high-dimensional financial data, selecting appropriate modeling techniques has become both more challenging and more essential. This paper provides a systematic and thorough review of the research of the stock index forecasting models, including traditional econometric models (ARIMA, GARCH, etc) and machine learning (LSTM, Attention mechanism model, etc). The article highlights the fact that traditional approaches are able to provide limited solutions for coping with nonlinear, nonstationary, and high-dimensional data while ML and DL models excel in capturing complex patterns in the data. Especially, attention-based models can be used to enhance the accuracy and interpretability of predictions. These are, however, sophisticated techniques that require intensive data and computation. Future research is encouraged to focus on integrating interdisciplinary perspectives, enhancing model transparency, and advancing the practical application of predictive models in real-world financial decision-making.