Trading the stock market using Google search volumes: a long short-term memory approach Online publication date: Fri, 07-Dec-2018
by Joseph St. Pierre; Mateusz Klimkiewicz; Adonay Resom; Nikolaos Kalampalikis
International Journal of Financial Engineering and Risk Management (IJFERM), Vol. 3, No. 1, 2019
Abstract: In this paper, we present a methodology for utilising Google search indices obtained from the Google trends website as a means for measuring potential investor interest in stocks listed on the Dow Jones index (Dow 30). We accomplish this task by utilising a long short-term memory network that correlates changes in the search volume for a given asset with changes in the actual trade volume for said asset. Additionally, by using these predictions, we formulate a concise trading strategy in the hopes of being able to outperform the market and analyse the results of this new strategy by backtesting across weekly closing price data for the last six months of 2016. Furthermore, with a success rate of 43%, we believe our algorithm to be scalable beyond the narrow scope of this study and could in fact be applicable to numerous other assets on the market.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Financial Engineering and Risk Management (IJFERM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook