Abstract
In this chapter we present how non financial data can be used to track financial activity (see [1] and Fig. 10.1). In details we investigate query log volumes, i.e. the volumes of searches for a specific query done by users in a search engine as a proxy for trading volume and we find that users’ activity on Yahoo! search engine anticipate trading volume by one-two days.
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Bordino, I., Battiston, S., Caldarelli, G., Cristelli, M., Ukkonen, A., & Weber, I. (2012). Web search queries can predict stock market volumes. PLoS ONE, 7(7), e40014. doi:10.1371/journal.pone.0040014
Golub, T. (2010). Counterpoint: data first. Nature, 464, 679.
Evans, J., & Rzhetsky, A. (2010). Machine science. Science, 329, 399.
Lazer, D., et al. (2009). Life in the network: the coming age of computational social science. Science, 323, 5915.
Gonzalez, M., Hidalgo, C., & Barabasi, A.-L. (2008). Understanding individual human mobility patterns. Nature, 453, 479.
Choi, H., & Varian, H. (2009). Predicting the present with google trends. Technical report
Goel, S., Hofman, J., Lahaie, S., Pennock, D., & Watts, D. (2010). Predicting consumer behaviour with web search. Proceedings of the National Academy of Sciences U S A, 107, 17486.
Ginzberg, J., Mohebi, M., Patel, R., Brammer, L., Smolinski, M., & Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457, 1012.
Preis, T., Reith, D., & Stanley, H. (2010). Complex dynamics of our economic life on different scales: insights from search engine query data. Philosophical Transactions of the Royal Society of London. Series A, 368, 5707.
Bollen, J., Mao, H., & Zeng, X.-J. (2011). Journal of Computational Science, 2, 1.
Bonanno, G., Caldarelli, G., Lillo, F., Micciche, S., Vandewalle, N., & Mantegna, R. N. (2004). Networks of equities in financial markets. EPJ B, 38, 363.
Granger, C. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37, 424.
Easley, D., & Kleinberg, J. (2010). Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge: Cambridge University Press.
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Cristelli, M. (2014). Web Queries Can Predict Stock Market Volumes. In: Complexity in Financial Markets. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-00723-6_10
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DOI: https://doi.org/10.1007/978-3-319-00723-6_10
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