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

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Deep learning models improve prediction accuracy in stock index forecasting, capturing complex data patterns effectively.
The review covers traditional econometric models like ARIMA and modern methods such as attention mechanisms in machine learning.
Assessment of high-frequency financial data highlights the need for sophisticated analytical techniques to manage complexity.
Future research should aim to improve model transparency and practical applications, integrating various interdisciplinary insights.

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This review explores stock index prediction methods, highlighting deep learning and attention models' effectiveness in finance.

Zihang Sun (2025) studied this question.

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