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.

翻译：本文对包含超过4900个YouTube视频的数据库进行深入研究，以刻画金融市场报道的特征。金融市场报道会产生大量视频，因此通过观看这些视频获取可操作见解可能具有挑战性和复杂性。本文利用OpenAI开发的语音转文本模型Whisper，从彭博社和雅虎财经的市场报道视频中生成文本语料库。我们采用自然语言处理技术来提取市场报道中关于语言使用的见解。此外，我们考察了热门话题的显著存在及其随时间的演变，以及某些个人和组织对金融市场的影响。我们的特征刻画凸显了金融市场报道的动态特性，并提供了反映近期金融事件和世界经济广泛讨论的宝贵见解。