用NLP量化财经新闻热度，预测股市波动与短期走势。
The Hype Index: an NLP-driven Measure of Market News Attention
- 基于新闻数量和市值调整，构建股票与板块的媒体关注度指数。
- 高热度指数与未来波动率上升、市场情绪变化显著相关。
- 适合金融量化研究者和市场情绪分析从业者使用。
本文提出一种名为Hype Index的新指标，用于量化大型上市公司在媒体中的关注度，借助自然语言处理技术从财经新闻中提取预测信号。以标普100指数为研究对象，首先构建基于新闻数的媒体关注度指数，通过计算每只股票或行业相关新闻占比来衡量相对曝光度；随后引入市值调整版的Hype Index，即个股或行业媒体权重与市值权重之比。该指数在股票与行业层面均可计算，并从多个维度进行评估：（1）不同热度分组的分类表现；（2）与收益、波动率及VIX指数在多时滞下的关联性；（3）对短期市场变动的预警能力；（4）相关性、抽样特性与趋势特征。结果表明，该指数族可有效支持股价波动分析、市场信号识别以及金融领域NLP应用拓展。
原文摘要 · Abstract (English)
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.
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