Quantifying Wikipedia Usage Patterns Before Stock Market Moves
By H. S. Moat, C. Curme, Adam Avakian, Dror Y. Kenett, H. E. Stanley, Tobias Preis. Scientific Reports, 2013
332 citations recorded by the source corpus.
Financial crises result from a catastrophic combination of actions. Vast stock market datasets offer us a window into some of the actions that have led to these crises. Here, we investigate whether data generated through Internet usage contain traces of attempts to gather information before trading decisions were taken. We present evidence in line with the intriguing suggestion that data on changes in how often financially related Wikipedia pages were viewed may have contained early signs of stock market moves. Our results suggest that online data may allow us to gain new insight into early information gathering stages of decision making.
Subfields: Economics and Econometrics, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/d15ec38fda09aadfc887bccf94aea205c3e0c6a9
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Citations in the corpus
- Big Data in product lifecycle management (2015, background): “For example, by analyzing changes in search terms related to finance in Google query and Wikipedia pages, stock market moves can be predicted from the vast new data [42, 43].”
- Quantifying Engagement with Citations on Wikipedia (2020, unknown): “Studies on the behavior of Wikipedia readers have mostly considered interest in contents [31, 49, 63], content popularity [8, 47, 56], or event timing [37].”
- Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data (2012, background): “reported on the predictive power of Wikipedia data for financial fluctuations [53].”
- Intraday online investor sentiment and return patterns in the U.S. stock market (2017, methodology): “Then, the sentiment weight for each term t can be computed as in Oliveira et al. (2016) as: SWtf-idf(t) = tf-idf(t, dpos)− tf-idf(t, dneg) tf-idf(t, dpos) + tf-idf(t, dneg) , (6)”
- On the Value of Wikipedia as a Gateway to the Web (2021, background): “For instance, it is of critical importance to Web search engines, such as Google [20], and has also been shown to be useful to improve, or even predict, financial markets [22, 23, 34].”