Earnings Call Analysis Using a Sparse Attention Based Encoder and Multi-Source Counterfactual Augmentation

Zixuan Yuan, Yada Zhu, Wei Zhang, Hui Xiong

Abstract: Earnings calls are periodically held to disclose the past performance and future plan of a publicly-traded company. The analysis of earnings call transcripts has come under the spotlight of both Artificial Intelligence (AI) and finance communities due to the critical value for understanding corporate fundamentals. However, existing studies have been limited by the challenges inherent in the lengthy, noisy, and scarce transcripts, and thus are difficult in learning to distill salient market-influencing semantics from these transcripts. To this end, in this paper, we propose a text encoder with a sparse self-attention mechanism to address the lengthiness and noisiness issues. Also, a multi-source counterfactual augmentation framework is developed to leverage abundant unlabeled texts (e.g., financial news) to address the data scarcity issue. In particular, the unlabeled texts serve as external perturbations to the original transcripts under strong semantic and task-specific constraints, which helps for efficient model learning. Finally, extensive experiments on real-world financial data demonstrate that our approach can effectively perform both short-term and long-term volatility prediction as well as financial sentiment analysis. Moreover, we showcase how the proposed framework can enhance model interpretability and provide high-quality augmented data.