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Quantifying macroeconomic expectations in stock markets using Google Trends

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  • Johannes Bock

Abstract

Among other macroeconomic indicators, the monthly release of U.S. unemployment rate figures in the Employment Situation report by the U.S. Bureau of Labour Statistics gets a lot of media attention and strongly affects the stock markets. I investigate whether a profitable investment strategy can be constructed by predicting the likely changes in U.S. unemployment before the official news release using Google query volumes for related search terms. I find that massive new data sources of human interaction with the Internet not only improves U.S. unemployment rate predictability, but can also enhance market timing of trading strategies when considered jointly with macroeconomic data. My results illustrate the potential of combining extensive behavioural data sets with economic data to anticipate investor expectations and stock market moves.

Suggested Citation

  • Johannes Bock, 2018. "Quantifying macroeconomic expectations in stock markets using Google Trends," Papers 1805.00268, arXiv.org.
    • Handle: RePEc:arx:papers:1805.00268
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    References listed on IDEAS

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    Cited by:

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    5. Ramchandra Rimal & Binod Rimal & Hum Nath Bhandari & Nawa Raj Pokhrel & Keshab R. Dahal, 2025. "Real Estate Market Prediction Using Deep Learning Models," Annals of Data Science, Springer, vol. 12(4), pages 1113-1156, August.

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