Web Search Queries Can Predict Stock Market Volumes

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 search engine traffic (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 examples 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 daily trading volumes of stocks traded in NASDAQ-100 are correlated with daily 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.

New approaches in agent-based modeling of complex financial systems

“…6(b), the probability distribution ofF k (t) is obviously asymmetric with a heavier positive tail. This result indicates that the external information usually drives the market to be more active, which is consistent with the previous empirical findings for the internet query data or news [35,32].…”

“…In recent years, exploring the scientific impact of online big-data has attracted much attention of researchers from different fields. The massive new data sources resulting from human interactions with the internet offer a better understanding for the profound influence of external information on complex financial systems [31,32,33,34,35,36,37,38,39].…”

Twitter permeability to financial events: an experiment towards a model for sensing irregularities

“…To sum up, most of the research in this field address the predictive power of social media problem and the differences appear in information sources, their extracted features, and also the predictive methods and the values to be predicted. [7] have shown that trading volumes of stocks traded in NASDAQ-100 are correlated with their query volumes (i.e., the number of users requests submitted to search engines on the Internet). Gunduz and Cataltepe [22] proposed a forecasting method which combines the analysis of news articles from Turkish finance websites, the extraction of feature vectors and stock prices to predict future market movements.…”

Trending Topics on Twitter Improve the Prediction of Google Hot Queries

“…Recent work prove that Web search volume can predict the values of some economic indicators. For instance, Bordino et al [9] show that daily trading volumes of NASDAQ-100 stocks are correlated with daily volumes of queries about the same stocks. Ettredge et al [10] use search logs to predict the job market while Choi and Varian [11] show how Google trends may be used to forecast unemployment levels, car and home sales, and disease prevalence in near real-time [11].…”