Information Content of Financial Youtube Channel: Case Study of 3PROTV and Korean Stock Market

ArXiv ID: 2311.15247 “View on arXiv”

Authors: Unknown

Abstract

We investigate the information content of 3PROTV, a south Korean financial youtube channel. In our sample we found evidence for the hypothesis that the channel have information content on stock selection, but only on negative sentiment. Positively mentioned stock had pre-announcement spike followed by steep fall in stock price around announcement period. Negatively mentioned stock started underperforming around the announcement period, with underreaction dynamics in post-announcement period. In the area of market timing, we found that change of sentimental tone of 3PROTV than its historical average predicts the lead value of Korean market portfolio return. Its predictive power cannot be explained by future change in news sentiment, future short term interest rate, and future liquidity risk.

Keywords: sentiment analysis, information content, market timing, social media finance, stock selection, Equities

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail
  • Why: The paper employs advanced econometric methods including Fama-French 5-factor models with lags/leads, event studies with CAAR calculations, and logit regressions, demonstrating substantial mathematical complexity. It also exhibits high empirical rigor through extensive data processing (YouTube scraping, sentiment lexicons), multi-year market data from DataGuide, robustness checks across parameters, and backtested trading strategies showing 11.5%-23.6% outperformance.
  flowchart TD
    A["Research Goal:<br>Assess info content of<br>3PROTV Financial YouTube Channel"] --> B["Data Collection:<br>Video transcripts &<br>Stock price data (KOSPI)"]

    B --> C["Sentiment Analysis:<br>Quantify video tone<br>(Positive/Negative/Sentiment Change)"]
    
    C --> D["Analysis 1: Stock Selection<br>Event Study: Stock price<br>behavior around mentions"]
    C --> E["Analysis 2: Market Timing<br>Regression: Sentiment change<br>vs. Future market returns"]

    D --> F["Outcome A:<br>Asymmetric Price Reaction<br>Pos: Pre-announcement spike, steep fall<br>Neg: Underperformance & underreaction"]
    E --> G["Outcome B:<br>Predictive Power<br>Sentiment change predicts future returns<br>Independent of news, rates, & liquidity"]