Quantifying Macroeconomic Expectations in Stock Markets Using Google Trends

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.

Identyfikacja Zainteresowania Dotacjami Jako Zjawiska Sezonowego

“…Zaletą wykorzystania zapytań z wyszukiwarek internetowych jest szybkość i bezwarunkowa dostępność [Bulczak 2014]. Istotę i precyzję informacji pochodzących z wyszukiwarek internetowych w identyfikowaniu i prognozowaniu procesów społecznych i gospodarczych ma pokrycie w systematycznie zwiększającym się dorobku naukowym w tym zakresie [Bock 2018;Boone i in. 2018;Jegorow 2017b;Jun i in.…”

Long Short-Term Memory Network for Predicting Exchange Rate of the Ghanaian Cedi

“…Google Trends index is selected as one of our input features for this study because it has been proven to be a good predictor in financial analysis. For example, in exchange rate [22], sck market analysis [42][43][44][45] and exchange rate prediction [46,47]. The GT dataset is represented by a vector G ∈ L×B , where B = features of GTI {G ID, d, I}, GID = unique ID assigned to each GTI record, d = GTI date, I = quantitative value of GT.…”