Beyond Trend Following: Deep Learning for Market Trend Prediction

ArXiv ID: 2407.13685 “View on arXiv”

Authors: Unknown

Abstract

Trend following and momentum investing are common strategies employed by asset managers. Even though they can be helpful in the proper situations, they are limited in the sense that they work just by looking at past, as if we were driving with our focus on the rearview mirror. In this paper, we advocate for the use of Artificial Intelligence and Machine Learning techniques to predict future market trends. These predictions, when done properly, can improve the performance of asset managers by increasing returns and reducing drawdowns.

Keywords: Artificial Intelligence, Machine Learning, Trend Following, Momentum Investing, Market Trend Prediction, Multi-asset / General

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 3.0/10
• Quadrant: Philosophers
• Why: The paper advocates for deep learning over linear models and discusses concepts like universal approximators, but the excerpt contains no mathematical derivations or formulas. Empirically, it lacks code, backtests, or statistical metrics, relying instead on conceptual discussions and a single chart example.

  flowchart TD
    A["Research Goal:<br>Predict Market Trends using AI"] --> B["Data: Multi-asset Historical Data"]
    B --> C["Methodology: Deep Learning Models<br>e.g., CNN, LSTM, Transformers"]
    C --> D["Computational Process:<br>Training & Hyperparameter Tuning"]
    D --> E["Computational Process:<br>Validation on Out-of-Sample Data"]
    E --> F["Key Findings:<br>AI beats Trend Following"]
    F --> G["Outcomes:<br>Higher Returns & Reduced Drawdowns"]