Towards Earnings Call and Stock Price Movement
This work addresses stock price prediction for investors and analysts, but it is incremental as it applies existing deep learning techniques to a specific financial dataset.
The paper tackled predicting stock price movements using earnings call transcripts by applying a deep learning framework with attention mechanisms, showing that the model outperforms traditional machine learning baselines and that earnings call information improves prediction performance.
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.