Deep Learning for Short Term Equity Trend Forecasting: A Behavior Driven Multi Factor Approach

ArXiv ID: 2508.14656 “View on arXiv”

Authors: Yuqi Luan

Abstract

This study proposes a behaviorally-informed multi-factor stock selection framework that integrates short-cycle technical alpha signals with deep learning. We design a dual-task multilayer perceptron (MLP) that jointly predicts five-day future returns and directional price movements, thereby capturing nonlinear market behaviors such as volume-price divergence, momentum-driven herding, and bottom reversals. The model is trained on 40 carefully constructed factors derived from price-volume patterns and behavioral finance insights. Empirical evaluation demonstrates that the dual-task MLP achieves superior and stable performance across both predictive accuracy and economic relevance, as measured by information coefficient (IC), information ratio (IR), and portfolio backtesting results. Comparative experiments further show that deep learning methods outperform linear baselines by effectively capturing structural interactions between factors. This work highlights the potential of structure-aware deep learning in enhancing multi-factor modeling and provides a practical framework for short-horizon quantitative investment strategies.

Keywords: Deep Learning, Multi-Factor Model, Dual-Task MLP, Technical Analysis Signals, Information Coefficient (IC), Equities

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper presents advanced deep learning architectures with detailed mathematical formulations (MLP, CNN, loss functions), indicating high math complexity. It includes a comprehensive empirical evaluation using 40 factors, specific metrics (IC, IR), and portfolio backtesting results, making it backtest-ready with substantial data implementation.

  flowchart TD
    A["Research Goal:<br>Forecast Short-Term Equity Trends<br>using Behavioral Factors"] --> B["Data: 40 Price-Volume &<br>Behavioral Finance Factors"]
    B --> C["Model: Dual-Task Deep MLP"]
    C --> D["Task 1: Predict 5-Day<br>Future Returns"]
    C --> E["Task 2: Predict Directional<br>Price Movement"]
    D & E --> F["Outcome: Superior Performance<br>High IC/IR & Stable Returns"]
    F --> G["Conclusion:<br>Structure-Aware Deep Learning<br>Outperforms Linear Baselines"]