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.

本文研究了4900多个YouTube视频数据库，以表征金融市场报道。我们使用OpenAI的语音转文本模型Whisper，通过自然语言处理技术从市场报道视频中提取洞察力，同时研究趋势话题的突出存在及其随时间的演变，了解某些个人和组织对金融市场的影响。我们的研究强调了金融市场报道的动态，提供了有关近期金融事件和世界经济的广泛讨论的有价值的视角。

利用人工智能对金融市场覆盖率进行表征