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Earnings call (EC), as a periodic teleconference of a publicly-tradedcompany, has been extensively studied as an essential market indicator becauseof its high analytical value in corporate fundamentals. The recent emergence ofdeep learning techniques has shown great promise in creating automatedpipelines to benefit the EC-supported financial applications. However, thesemethods presume all included contents to be informative without refiningvaluable semantics from long-text transcript and suffer from EC scarcity issue.Meanwhile, these black-box methods possess inherent difficulties in providinghuman-understandable explanations. To this end, in this paper, we propose aMulti-Domain Transformer-Based Counterfactual Augmentation, named MTCA, toaddress the above problems. Specifically, we first propose a transformer-basedEC encoder to attentively quantify the task-inspired significance of criticalEC content for market inference. Then, a multi-domain counterfactual learningframework is developed to evaluate the gradient-based variations after weperturb limited EC informative texts with plentiful cross-domain documents,enabling MTCA to perform unsupervised data augmentation. As a bonus, wediscover a way to use non-training data as instance-based explanations forwhich we show the result with case studies. Extensive experiments on thereal-world financial datasets demonstrate the effectiveness of interpretableMTCA for improving the volatility evaluation ability of the state-of-the-art by14.2\% in accuracy.

Multi-Domain Transformer-Based Counterfactual Augmentation for Earnings Call Analysis