Quantifying Trading Behavior in Financial Markets Using Google Trends

By Tobias Preis, H. S. Moat, H. E. Stanley. 2013

964 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Epidemiology, Sociology and Political Science

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Citations in the corpus

Long short‐term memory networks for CSI300 volatility prediction with Baidu search volume (2018, background): “al(2013), found that detectable increases in Google search volumes for keywords relating to financial markets before stock market falls [16].”
Deep Learning Stock Volatility with Google Domestic Trends (2015, methodology): “Recent study has shown correlations between Google trends and the equity market [13] and we use them as a representation of the public interest and macroeconomics in this work.”
Quantifying China's Regional Economic Complexity (2017, background): “Moreover, online social networks [3,16,73], mobile phone data [1,11,74], satellite imagery [12,75], geo-tagged images [13] and web queries [76,77] have also been applied to reveal economic status, infer economic development, forecast unemployment, predict poverty, map inequality, quantify trading be”
Accurate estimation of influenza epidemics using Google search data via ARGO (2015, background): “ARGO can be easily generalized to any temporal and spatial scales for a variety of diseases or social events amenable to be tracked by internet searches or services [3, 4, 8, 9, 30, 31, 39, 40].”
Constructing a Global Fear Index for the COVID-19 Pandemic (2020, background): “…(for relevant literature see Adachi, Masuda, and Takeda 2017; Aouadi, Arouri, and Teulon 2013; Bank, Larch, and Peter 2011; Bodnaruk and Ostberg 2009; Da, Engelberg, and Gao 2011; Jacobs and Hillert 2016; Joseph, Wintoki, and Zhang 2011; Preis, Moat, and Stanley 2013; Zhu and Jiang 2018).”
Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review (2018, background): “The analysis of online search queries has been of notable popularity in the field of big data analytics in academic research [8,9].”
Ten years of research change using Google Trends: From the perspective of big data utilizations and applications (2017, background): “(2013) analyzed the changes in the number of Google searches using financial search terms and suggested patterns that could be interpreted as "early warning signals" of stock market movements (Preis et al., 2013).”
Stock Market Prediction through Technical and Public Sentiment Analysis (2015, methodology): “More recently, research has shown that public sentiment is correlated with stock market events [1], [2], [3].”
Corporate Social Responsibility and Market Efficiency: Evidence from ESG and Misvaluation Measures (2020, methodology): “These data have been used in several studies investigating asset prices in a more general context (e.g. Da et al., 2014; Preis et al., 2013) and the impact of sustainability in a corporate finance and asset management setting (e.”
Trading Network Predicts Stock Price (2014, background): “By analyzing Google search volume for 98 terms of varying financial relevance, they found that the increases in search volume for financially relevant search terms tend to precede large losses in financial markets 10,11 .”
Mapping the NFT revolution: market trends, trade networks, and visual features (2021, background): “[37] Tobias Preis, Helen Susannah Moat, and H Eugene Stanley.”
Big data techniques in auditing research and practice: Current trends and future opportunities (2018, positive): “Alanyali, Moat, and Preis (2013) find the daily number of mentions of a stock in the Financial Times is positively correlated with daily volume, both before and on the day of the news release.”