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Mining the Web for the Voice of the Herd to Track Stock Market Bubbles

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  • Aaron Gerow
  • Mark Keane

Abstract

We show that power-law analyses of financial commentaries from newspaper web-sites can be used to identify stock market bubbles, supplementing traditional volatility analyses. Using a four-year corpus of 17,713 online, finance-related articles (10M+ words) from the Financial Times, the New York Times, and the BBC, we show that week-to-week changes in power-law distributions reflect market movements of the Dow Jones Industrial Average (DJI), the FTSE-100, and the NIKKEI-225. Notably, the statistical regularities in language track the 2007 stock market bubble, showing emerging structure in the language of commentators, as progressively greater agreement arose in their positive perceptions of the market. Furthermore, during the bubble period, a marked divergence in positive language occurs as revealed by a Kullback-Leibler analysis.

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  • Aaron Gerow & Mark Keane, 2012. "Mining the Web for the Voice of the Herd to Track Stock Market Bubbles," Papers 1212.2676, arXiv.org.
    • Handle: RePEc:arx:papers:1212.2676
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    References listed on IDEAS

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    1. Wanfeng YAN & Ryan WOODARD & Didier SORNETTE, 2010. "Diagnosis and Prediction of Market Rebounds in Financial Markets," Swiss Finance Institute Research Paper Series 10-15, Swiss Finance Institute.
    2. Bernardo A. Huberman & Lada A. Adamic, 1999. "Growth dynamics of the World-Wide Web," Nature, Nature, vol. 401(6749), pages 131-131, September.
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