AI-Enhanced Factor Analysis for Predicting S&P 500 Stock Dynamics

2024/12/17 by Jiajun Gu, Gu, Jiajun, Zhirui Yang +6
Decision Sciences · Psychology · #Computer science #Dynamics (music) #Econometrics #Economics #Factor (programming language) #Geography #Programming language #Psychology #Stock (firearms) #Stock Market Forecasting Methods

paper · pdf · doi:10.48550/arxiv.2412.12438

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2024/12/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This project investigates the interplay of technical, market, and statistical factors in predicting stock market performance, with a primary focus on S&P 500 companies. Utilizing a comprehensive dataset spanning multiple years, the analysis constructs advanced financial metrics, such as momentum indicators, volatility measures, and liquidity adjustments. The machine learning framework is employed to identify patterns, relationships, and predictive capabilities of these factors. The integration of traditional financial analytics with machine learning enables enhanced predictive accuracy, offering valuable insights into market behavior and guiding investment strategies. This research highlights the potential of combining domain-specific financial expertise with modern computational tools to address complex market dynamics.

Related

• Hunting Tomorrow's Leaders: Using Machine Learning to Forecast S&P 500 Additions & Removal
• Enhancing Financial Decision-Making: Machine Learning and AI-Powered Predictions and Analysis
• S&P 500 Trend Prediction
• The role of artificial intelligence in the decision-making process: a study on the financial analysis and movement forecasting of the world’s largest stock exchanges
• Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis