تقرير
Characterizing Financial Market Coverage using Artificial Intelligence
| العنوان: | Characterizing Financial Market Coverage using Artificial Intelligence |
|---|---|
| المؤلفون: | Tshimula, Jean Marie, Nkashama, D'Jeff K., Owusu, Patrick, Frappier, Marc, Tardif, Pierre-Martin, Kabanza, Froduald, Brun, Armelle, Patenaude, Jean-Marc, Wang, Shengrui, Chikhaoui, Belkacem |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Quantitative Finance |
| مصطلحات موضوعية: | Quantitative Finance - Statistical Finance, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Computers and Society, Computer Science - Machine Learning |
| الوصف: | This paper scrutinizes a database of over 4900 YouTube videos to characterize financial market coverage. Financial market coverage generates a large number of videos. Therefore, watching these videos to derive actionable insights could be challenging and complex. In this paper, we leverage Whisper, a speech-to-text model from OpenAI, to generate a text corpus of market coverage videos from Bloomberg and Yahoo Finance. We employ natural language processing to extract insights regarding language use from the market coverage. Moreover, we examine the prominent presence of trending topics and their evolution over time, and the impacts that some individuals and organizations have on the financial market. Our characterization highlights the dynamics of the financial market coverage and provides valuable insights reflecting broad discussions regarding recent financial events and the world economy. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2302.03694 |
| رقم الأكسشن: | edsarx.2302.03694 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |