  Earnings call (EC), as a periodic teleconference of a publicly-traded
company, has been extensively studied as an essential market indicator because
of its high analytical value in corporate fundamentals. The recent emergence of
deep learning techniques has shown great promise in creating automated
pipelines to benefit the EC-supported financial applications. However, these
methods presume all included contents to be informative without refining
valuable semantics from long-text transcript and suffer from EC scarcity issue.
Meanwhile, these black-box methods possess inherent difficulties in providing
human-understandable explanations. To this end, in this paper, we propose a
Multi-Domain Transformer-Based Counterfactual Augmentation, named MTCA, to
address the above problems. Specifically, we first propose a transformer-based
EC encoder to attentively quantify the task-inspired significance of critical
EC content for market inference. Then, a multi-domain counterfactual learning
framework is developed to evaluate the gradient-based variations after we
perturb limited EC informative texts with plentiful cross-domain documents,
enabling MTCA to perform unsupervised data augmentation. As a bonus, we
discover a way to use non-training data as instance-based explanations for
which we show the result with case studies. Extensive experiments on the
real-world financial datasets demonstrate the effectiveness of interpretable
MTCA for improving the volatility evaluation ability of the state-of-the-art by
14.2\% in accuracy.
