  Earnings calls are hosted by management of public companies to discuss the
company's financial performance with analysts and investors. Information
disclosed during an earnings call is an essential source of data for analysts
and investors to make investment decisions. Thus, we leverage earnings call
transcripts to predict future stock price dynamics. We propose to model the
language in transcripts using a deep learning framework, where an attention
mechanism is applied to encode the text data into vectors for the
discriminative network classifier to predict stock price movements. Our
empirical experiments show that the proposed model is superior to the
traditional machine learning baselines and earnings call information can boost
the stock price prediction performance.
