Contrastive Learning of Asset Embeddings from Financial Time Series

ArXiv ID: 2407.18645 “View on arXiv”

Authors: Unknown

Abstract

Representation learning has emerged as a powerful paradigm for extracting valuable latent features from complex, high-dimensional data. In financial domains, learning informative representations for assets can be used for tasks like sector classification, and risk management. However, the complex and stochastic nature of financial markets poses unique challenges. We propose a novel contrastive learning framework to generate asset embeddings from financial time series data. Our approach leverages the similarity of asset returns over many subwindows to generate informative positive and negative samples, using a statistical sampling strategy based on hypothesis testing to address the noisy nature of financial data. We explore various contrastive loss functions that capture the relationships between assets in different ways to learn a discriminative representation space. Experiments on real-world datasets demonstrate the effectiveness of the learned asset embeddings on benchmark industry classification and portfolio optimization tasks. In each case our novel approaches significantly outperform existing baselines highlighting the potential for contrastive learning to capture meaningful and actionable relationships in financial data.

Keywords: Contrastive Learning, Representation Learning, Time Series Analysis, Asset Embeddings, Hypothesis Testing, Equities

Complexity vs Empirical Score

• Math Complexity: 5.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper applies advanced machine learning concepts like contrastive learning and statistical hypothesis testing for sampling, representing moderate math complexity. It demonstrates empirical rigor by testing the approach on real-world datasets for industry classification and risk hedging tasks, showing improvements over baselines.

  flowchart TD
    A["Research Goal: Learn Asset Embeddings<br>from Financial Time Series"] --> B["Input: Historical Equity Return Data"]
    B --> C["Core Method: Contrastive Learning Framework"]
    C --> D{"Sampling Strategy<br>via Hypothesis Testing"}
    D --> E["Construct Positive/Negative Sample Pairs"]
    E --> F["Train Model using<br>Contrastive Loss Functions"]
    F --> G["Output: Learned Asset Embeddings"]
    G --> H["Key Findings: Superior Performance<br>on Industry Classification & Portfolio Optimization"]