Can We Predict the Financial Markets Based on Google's Search Queries?

Abstract

We look into the interaction of Google's search queries and several aspects of international equity markets. Using a novel methodology for selecting words and a vector autoregressive modeling approach, we study whether the search queries of finance‐related words can have an impact on returns, volatility of returns and traded volume in four different English‐speaking countries. We identify several words whose search frequency is associated with changes in the dependent variables. In particular, we find that increases in search queries including the word stock predict increased volatility and decreased index returns over the next week. On top of that, we investigate the performance of a market‐timing strategy based on the search frequency of this word and benchmark it against random words from the Word‐Net database and a naive buy‐and‐hold strategy. The results of this empirical application are positive and particularly stronger during the global crisis of 2009. Copyright © 2016 John Wiley & Sons, Ltd.

The Forecasting Power of Internet Search Queries in the Brazilian Financial Market

“…According to Carneiro and Mylonakis (2009), information regarding flu provided by Google Flu Trends platform can detect regional surges faster (up to seven to ten days before) than the Centers for Disease Control and Prevention (CDC). Polgreen et al (2008) used search query volume extracted from Yahoo and showed evidence confirming the possibility of predicting infectious diseases. Ginsberg, Mohebbi, Patel, Brammer, Smolinski and Brilliant (2009) have conducted another similar research using data provided by Google.…”

“…This is a relevant source of data, since online search may indicate an ongoing situation, event or a population's bias in many fields. For instance, a significant increase in search volume about flu may indicate the occurrence of a disease outbreak in a specific region (Carneiro & Mylonakis, 2009;Polgreen, Chen, Pennock, Nelson, & Weinstein, 2008). Likewise, a raise in online search queries of given types of cars may precede future sales (Kristoufek, 2013).…”

“…This study brings evidences for classical issues on financial literature such as return predictability and, in a broader sense, the efficient markets hypothesis. Moreover, there is no knowledge of other studies approaching this issue in an emerging market such as Brazil, neither employing the models presented by Perlin, Caldeira, Santos and Pontuschka (2016) and Vozlyublennaia (2014) for the fixed income market 2 . Aiming a better understanding, the study is organized as follows: current literature related to the subject is presented.…”

“…Both stock and fixed income markets will have their respective returns, volatilities and traded volumes related to the search query levels from Google. This method was chosen since these variables have already been related to search query data from Google in studies conducted outside Brazil (Arditi, Yechiam, & Zahavi, 2015;Bordino, Battiston, Caldarelli, Cristelli, Ukkonen, & Weber, 2012;Perlin et al, 2016;Vozlyublennaia, 2014). These measures will be used both as dependent variables as regressors in the econometric framework.…”

Forecasting economic indicators using a consumer sentiment index: Survey‐based versus text‐based data

“…As unstructured textual information in social media, search queries, news, blogs, and forums has become an interesting topic of research, there has also emerged in the economic research area, for example, sentiment analysis for stock market prediction (Nguyen, Shirai, & Velcin, 2015;Oliveira, Cortez, & Areal, 2017;Perlin, Caldeira, Santos, & Pontuschka, 2017;Schumaker, Zhang, Huang, & Chen, 2012;Yu, Wu, Chang, & Chu, 2013). In addition, news and social media have been used to measure the economic sentiment index in several economic research studies (Daas & Puts, 2014;Shapiro, Kanjoya, & Wilson, 2017;Uhl, 2011).…”