Paper: arXiv 2302.03694
Authors: Jean Marie Tshimula, D’Jeff K. Nkashama, Patrick Owusu, Marc Frappier, Pierre-Martin Tardif, Froduald Kabanza, Armelle Brun, Jean-Marc Patenaude, Shengrui Wang, Belkacem Chikhaoui
Abstract
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.
Complexity vs Empirical Score
- Math Complexity: 2.0/10
- Empirical Rigor: 5.5/10
- Quadrant: Street Traders — practical and empirical, lighter on theory
Why this score: The paper provides an applied NLP exploration of financial multimedia data using modern speech-to-text tools, offering high practical readability with minimal mathematical formulation. While the dataset size is respectable, the methodology relies on off-the-shelf models rather than novel technical or quantitative derivations.
Research Flowchart
flowchart TD
A[Research Goal: Characterize Financial Market Coverage on YouTube] --> B[(Data Input: 4900+ Bloomberg & Yahoo Finance Videos)]
B --> C[Whisper AI Model: Speech-to-Text Transcription]
C --> D[Text Corpus Creation]
D --> E[NLP Analysis: Language Use, Topics & Influencers]
E --> F[Findings: Identified Market Dynamics, Trending Topics & Economic Insights]