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

TL;DR

This study demonstrates that analyzing power-law patterns in online financial commentaries can effectively identify and track stock market bubbles by revealing collective sentiment shifts before traditional indicators.

Contribution

It introduces a novel method using power-law analysis of financial news to detect market bubbles, supplementing existing volatility-based approaches.

Findings

Language regularities track market movements

Emerging consensus in commentary signals bubble formation

Divergence in positive language indicates bubble bursting

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.