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Cited by 948

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Quantifying Trading Behavior in Financial Markets Using Google Trends

Open access
Preis, Tobias, Moat, Helen Susannah, Stanley, H. Eugene·2013·Scientific Reports
DOI 10.1038/srep01684
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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.

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Metadata

Authors
Preis, T., Moat, H. S., & Stanley, H. E.
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2013-04-25
Volume
3
Issue
1
Pages
1684-1684
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/srep01684
OpenAlex
W2101618658
Cited by
948
References
2
FWCI
204.04
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Quantifying Trading Behavior in Financial Markets Using Google Trends. https://app.cochat.ai/papers/10.1038/srep01684
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Preis, T., Moat, H. S., & Stanley, H. E. (2013). Quantifying Trading Behavior in Financial Markets Using Google Trends. Scientific Reports. https://doi.org/10.1038/srep01684

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.