Web search queries can predict stock market volumes

TL;DR

This study demonstrates that web search query volumes can predict stock trading volumes in NASDAQ-100, showing potential for early warning signals of financial market activity by analyzing collective online user behavior.

Contribution

It provides empirical evidence linking search query volumes to stock trading activity and shows that query trends can anticipate market peaks by over a day.

Findings

Query volumes correlate with stock trading volumes.

Query trends can predict market peaks with a delay of one or more days.

User activity patterns emerge from collective but uncoordinated behavior.

Abstract

We live in a computerized and networked society where many of our actions leave a digital trace and affect other people's actions. This has lead to the emergence of a new data-driven research field: mathematical methods of computer science, statistical physics and sociometry provide insights on a wide range of disciplines ranging from social science to human mobility. A recent important discovery is that query volumes (i.e., the number of requests submitted by users to search engines on the www) can be used to track and, in some cases, to anticipate the dynamics of social phenomena. Successful exemples include unemployment levels, car and home sales, and epidemics spreading. Few recent works applied this approach to stock prices and market sentiment. However, it remains unclear if trends in financial markets can be anticipated by the collective wisdom of on-line users on the web. Here we show that trading volumes of stocks traded in NASDAQ-100 are correlated with the volumes of queries related to the same stocks. In particular, query volumes anticipate in many cases peaks of trading by one day or more. Our analysis is carried out on a unique dataset of queries, submitted to an important web search engine, which enable us to investigate also the user behavior. We show that the query volume dynamics emerges from the collective but seemingly uncoordinated activity of many users. These findings contribute to the debate on the identification of early warnings of financial systemic risk, based on the activity of users of the www.