Quantifying Trading Behavior in Financial Markets Using Google Trends

Abstract

Crises in financial markets affect humans worldwide. Detailed market data on trading decisions reflect some of the complex human behavior that has led to these crises. We suggest that massive new data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market movements. By analyzing changes in Google query volumes for search terms related to finance, we find patterns that may be interpreted as ‘‘early warning signs’’ of stock market moves. Our results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior.

Keywords: Big Data, Financial Markets, Predictive Analytics, Search Volume, Data Mining, Google, Computational Social Science, Digital Traces, Dow Jones Industrial Average

JEL Classification: A10, B40, C10, C20, C22, C53, C90, D70, D79, D83, J10, J11, O40, O47