用自监督学习提升财报数据在选股中的长期有效性
Trading through Earnings Seasons using Self-Supervised Contrastive Representation Learning
- 基于对比预测编码构建财报表征模型，自动捕捉财报的深层价值
- 在多个行业上优于基准模型，尤其在财报发布后仍保持预测力
- 适合做中频量化交易的投资者，解决财报时效性衰减问题
财报发布是金融市场关键经济事件，对预测股价走势至关重要。然而，财报发布周期不规律，且信息价值随时间快速衰减，给中频算法交易模型带来挑战。为此，本文提出对比财报变压器（CET）模型，一种基于对比预测编码（CPC）的自监督学习方法，旨在优化财报数据的利用效率。通过在不同行业间与基准模型进行对比研究，结果表明，CET能有效捕捉财报数据的内在价值，并在发布后长时间内保持稳定预测能力。其基于CPC的表征学习机制，使模型能够适应财报信息的动态衰减特性，显著提升长期预测性能。该研究为算法交易中高效使用财报数据提供了新思路。
原文摘要 · Abstract (English)
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.
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