Trading through Earnings Seasons using Self-Supervised Contrastive Representation Learning

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.

• The paper presents a novel Contrastive Earnings Transformer (CET) that integrates self-supervised learning with earnings data to enhance intra-day trading strategies.
• The paper employs Contrastive Predictive Coding within a Transformer architecture, achieving higher day-one prediction accuracy against established benchmarks.
• The study demonstrates the CET model's robustness across sectors and its ability to mitigate the rapid decay of earnings data's predictive power post-release.

Trading through Earnings Seasons using Self-Supervised Contrastive Representation Learning

"Trading through Earnings Seasons using Self-Supervised Contrastive Representation Learning" by Zhengxin Joseph Ye et al. introduces a novel approach to incorporating the irregular yet critical information from earnings releases into intra-day algorithmic trading models. The paper focuses on self-supervised learning, particularly leveraging the principles of Contrastive Predictive Coding (CPC) within a Transformer-based architecture, termed as the Contrastive Earnings Transformer (CET) model.

The CET model is designed to address the rapid decay in the usefulness of earnings data post-release, a challenge that has historically impeded the accuracy of algorithmic trading models. The paper achieves this by integrating earnings data with high-frequency minutely stock price and volume data, capturing the nuances of stock movements influenced by earnings announcements.

Key Model Components and Approach

Earnings data, being irregular and heterogeneous, presents unique challenges. The authors preprocess this data into a consistent feature set of financial metrics, which are then standardised. A series of experiments demonstrate the efficacy of integrating this processed earnings data with intra-day stock data, thus enhancing the model's predictive capabilities.

The CET model employs a self-supervised learning approach using CPC. This involves the model learning useful representations from unlabeled data by predicting the future context based on current observations. A distinctive feature of CPC is its ability to maximise mutual information between the context vector and future representations—thus capturing the essential temporal dynamics critical for stock price prediction.

Model Architecture and Pre-Training

The CET model utilises separate encoding networks for stock price and volume data and earnings data. The stock data is encoded using a non-linear encoder and fed into a Transformer, while the earnings data is processed through an autoencoder. These representations are combined and optimised using the InfoNCE loss function.

The pre-training phase involves creating context vectors that are then used for predicting future stock price movements, with the models fine-tuned on task-specific data. The integration of CPC enables the CET model to generalise well and adapt to the fast-decaying predictive power of earnings data.

Experimental Evaluation

The paper rigorously evaluates the CET model against both self-supervised and supervised learning benchmarks. Notably, the study illustrates that:

1. Performance on Day 1: The CET model consistently outperforms other self-supervised models like Autoencoder (AE), Transformers with Masked Language Modelling (MLM), and SimCLR. The rigorous pre-training phase enables CET to leverage unlabeled data effectively, providing higher accuracy in stocks' movement predictions on the day of the earnings release.
2. Sector-Specific Performance: When fine-tuned with sector-specific data, CET maintains its superior performance across diverse sectors, demonstrating its robustness.
3. Preserving Predictive Power Post-Release: The model's adaptability is further tested over multiple days post earnings release. CET preserves its predictive performance across this period, highlighting its ability to manage the diminishing relevance of earnings data effectively.

Implications and Future Directions

The CET model presents a significant advancement in the domain of financial time series prediction during earnings seasons. The strength of leveraging CPC within a Transformer framework permits the model to maintain high performance and adaptability even as the predictive utility of earnings data declines.

Practical Implications: For practitioners, this implies more consistent and reliable stock prediction capabilities, enabling better trading strategies during volatile earnings seasons.

Theoretical Implications: The success of this approach demonstrates the value of self-supervised representation learning in financial contexts, encouraging further exploration into more complex and heterogeneous data sources.

Future Work: Future studies could explore reducing the network complexity to optimise the scaling of the model across diverse financial contexts. Moreover, addressing the impact of different market conditions on the model's performance and exploring advanced negative sampling strategies would be crucial next steps.

The CET model, through its innovative integration of earnings data and advanced machine learning techniques, offers a promising pathway for more dynamic and responsive algorithmic trading strategies.