PLEASE put your names here Antonina Anastasova Yifan Duan Chiayu Yang
We can add and overview here. THIS IS JUST A DRAFT, we are brainstorming on working with 2.6 dataset given in the assignment.
This project analyzes earnings call transcripts using NLP techniques. It includes sentiment analysis, summarization, and visualizations.
The dataset contains earnings call transcripts with metadata such as date, ticker, and exchange.
-
Open your terminal.
-
Clone the repository to your local machine:
git clone https://github.com/aanastasova/AI-Seminar-Project.git
and give your username and password is access token: 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
- Qin, Yu., & Yang, Yi. (2019). What You Say and How You Say It Matters: Predicting Stock Volatility Using Verbal and Vocal Cues. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.
Predicting financial risk is an essential task in financial market. Prior research has shown that textual information in a firm's financial statement can be used to predict its stock's risk level. Nowadays, firm CEOs communicate information not only verbally through press releases and financial reports, but also nonverbally through investor meetings and earnings conference calls. There are anecdotal evidences that CEO's vocal features, such as emotions and voice tones, can reveal the firm's performance. However, how vocal features can be used to predict risk levels, and to what extent, is still unknown. To fill the gap, we obtain earnings call audio recordings and textual transcripts for S&P 500 companies in recent years. We propose a multimodal deep regression model (MDRM) that jointly model CEO's verbal (from text) and vocal (from audio) information in a conference call. Empirical results show that our model that jointly considers verbal and vocal features achieves significant and substantial prediction error reduction. We also discuss several interesting findings and the implications to financial markets. The processed earnings conference calls data (text and audio) are released for readers who are interested in reproducing the results or designing trading strategy.
-
Huang, Y., Tai, W., Zhou, F., Gao, Q., & Zhong, T. (2025). Extracting key insights from earnings call transcript via information-theoretic contrastive learning. Information Processing & Management, 62(3), 103998. https://doi.org/10.1016/j.ipm.2024.103998
-
Shimon Kogan, Dimitry Levin, Bryan R. Routledge, Jacob S. Sagi, and Noah A. Smith. 2009. Predicting risk from financial reports with regression. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL '09). Association for Computational Linguistics, USA, 272–280.