The Hype Index: an NLP-driven Measure of Market News Attention

ArXiv ID: 2506.06329 “View on arXiv”

Authors: Zheng Cao, Wanchaloem Wunkaew, Helyette Geman

Abstract

This paper introduces the Hype Index as a novel metric to quantify media attention toward large-cap equities, leveraging advances in Natural Language Processing (NLP) for extracting predictive signals from financial news. Using the S&P 100 as the focus universe, we first construct a News Count-Based Hype Index, which measures relative media exposure by computing the share of news articles referencing each stock or sector. We then extend it to the Capitalization Adjusted Hype Index, adjusts for economic size by taking the ratio of a stock’s or sector’s media weight to its market capitalization weight within its industry or sector. We compute both versions of the Hype Index at the stock and sector levels, and evaluate them through multiple lenses: (1) their classification into different hype groups, (2) their associations with returns, volatility, and VIX index at various lags, (3) their signaling power for short-term market movements, and (4) their empirical properties including correlations, samplings, and trends. Our findings suggest that the Hype Index family provides a valuable set of tools for stock volatility analysis, market signaling, and NLP extensions in Finance.

Keywords: Natural Language Processing (NLP), Hype Index, Sentiment Analysis, Volatility Analysis, Media Attention, Equities

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The math is relatively straightforward, relying on basic ratios and simple statistical measures like correlations and lag analysis rather than advanced theorems, while the empirical work is substantial, featuring detailed data collection from Refinitiv, backtesting on S&P 100 with 326 days of news, and multiple hypothesis tests across returns, volatility, and VIX.

  flowchart TD
    A["Research Goal<br>Determine the predictive value of<br>NLP-based Media Attention"] --> B["Data Collection<br>S&P 100 Stocks &<br>Financial News Articles"]
    
    B --> C["Key Methodology<br>NLP Processing &<br>Media Count Analysis"]
    
    C --> D{"Computation"}
    
    D --> E["News Count-Based Hype Index<br>Relative Media Exposure"]
    D --> F["Capitalization Adjusted Hype Index<br>Media Weight / Mkt Cap Weight"]
    
    E --> G["Analysis & Evaluation"]
    F --> G
    
    G --> H["Key Outcomes<br>Volatility Analysis<br>Market Signaling<br>NLP Finance Extension"]