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

ArXiv ID: 2412.12438 “View on arXiv”

Authors: Unknown

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.

Keywords: Technical Analysis, Factor Prediction, S&P 500, Machine Learning, Financial Metrics

Complexity vs Empirical Score

• Math Complexity: 2.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders
• Why: The paper relies on established financial formulas and basic ML without complex derivations, while it uses a specific real-world dataset (CRSP) with detailed preprocessing and explicit factor definitions suitable for implementation.

  flowchart TD
    A["Research Goal: Predict S&P 500<br>Stock Dynamics using AI-Enhanced<br>Factor Analysis"] --> B["Data Collection & Preprocessing<br>Technical, Market & Statistical Factors"]
    B --> C["Feature Engineering<br>Construct Metrics: Momentum,<br>Volatility, Liquidity"]
    C --> D["Machine Learning Framework<br>Training, Validation & Testing Models"]
    D --> E{"Analysis & Prediction"}
    E --> F["Key Findings/Outcomes"]
    F --> F1["Enhanced Predictive Accuracy<br>via AI Integration"]
    F --> F2["Identification of Key<br>Market Factors"]
    F --> F3["Actionable Insights<br>for Investment Strategies"]