Quantifying macroeconomic expectations in stock markets using Google Trends

TL;DR

This paper demonstrates that Google Trends data, combined with macroeconomic information, can improve predictions of U.S. unemployment and enhance stock market trading strategies by capturing investor expectations.

Contribution

It introduces a novel approach of integrating Google search query data with macroeconomic indicators to predict unemployment and inform trading strategies.

Findings

Google Trends data improves unemployment rate prediction accuracy.

Combining behavioral and economic data enhances market timing strategies.

The approach can anticipate stock market movements based on investor expectations.

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.