Trading through Earnings Seasons using Self-Supervised Contrastive Representation Learning

ArXiv ID: 2409.17392 “View on arXiv”

Authors: Unknown

Abstract

Earnings release is a key economic event in the financial markets and crucial for predicting stock movements. Earnings data gives a glimpse into how a company is doing financially and can hint at where its stock might go next. However, the irregularity of its release cycle makes it a challenge to incorporate this data in a medium-frequency algorithmic trading model and the usefulness of this data fades fast after it is released, making it tough for models to stay accurate over time. Addressing this challenge, we introduce the Contrastive Earnings Transformer (CET) model, a self-supervised learning approach rooted in Contrastive Predictive Coding (CPC), aiming to optimise the utilisation of earnings data. To ascertain its effectiveness, we conduct a comparative study of CET against benchmark models across diverse sectors. Our research delves deep into the intricacies of stock data, evaluating how various models, and notably CET, handle the rapidly changing relevance of earnings data over time and over different sectors. The research outcomes shed light on CET’s distinct advantage in extrapolating the inherent value of earnings data over time. Its foundation on CPC allows for a nuanced understanding, facilitating consistent stock predictions even as the earnings data ages. This finding about CET presents a fresh approach to better use earnings data in algorithmic trading for predicting stock price trends.

Keywords: Contrastive Earnings Transformer (CET), Contrastive Predictive Coding (CPC), self-supervised learning, earnings release, algorithmic trading, Equities (Stocks)

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced machine learning concepts like Transformers and Contrastive Predictive Coding, requiring significant mathematical understanding, yet the research is grounded in a practical trading problem with a detailed comparative study across sectors, suggesting strong empirical implementation.

  flowchart TD
    A["Research Goal<br>How to optimise earnings data<br>for medium-frequency trading?"] --> B
    
    subgraph B ["Methodology & Data"]
        direction LR
        B1["Dataset<br>Historical Stock & Earnings Data"] --> B2["Preprocessing<br>Time-series & Event Alignment"]
        B2 --> B3["Model: Contrastive Earnings Transformer<br>Self-Supervised Contrastive Learning"]
    end

    B3 --> C["Computational Process<br>Contrastive Predictive Coding<br>Learning latent representations<br>maximising mutual information"]

    C --> D
    
    subgraph D ["Key Findings/Outcomes"]
        D1["CET outperforms benchmarks<br>in predictive accuracy"]
        D2["Robust to data aging<br>Maintains relevance over time"]
        D3["Sector-agnostic advantage<br>Consistent across industries"]
    end

    D --> E["Conclusion<br>Novel approach for leveraging<br>earnings data in algorithmic trading"]