The Most Predictive Energy Search Terms

24 Pages Posted: 17 Oct 2016 Last revised: 16 Jan 2017

See all articles by Mohamad Afkhami

Mohamad Afkhami

Stevens Institute of Technology - School of Business

Lindsey Cormack

Stevens Institute of Technology - College of Arts and Letters

Hamed Ghoddusi

Economic Research Forum; California State Polytechnic University, San Luis Obispo

Date Written: October 16, 2016

Abstract

Internet search activity data has been widely used as an instrument to approximate trader attention in different markets. This method has proven effective in predicting market indices in the short-term. However, little attention has been paid to comparing various search keywords and finding the most effective terms representing attention in different markets. This study attempts to build the best practically possible proxy for attention in the market for major energy commodities using Google search data.

Specifically, first we confirm that Google search activity for energy-related keywords are significant predictors of energy price volatility. We show that search trends data have incremental predictive power beyond the conventional GARCH models. Next, starting with a set of ninety terms used in the energy sector, the study uses a multistage filtering process to create combinations of keywords that best predict the volatility of crude oil (Brent and West Texas Intermediate), conventional gasoline (New York Harbor and US Gulf Coast), Heating Oil (New York Harbor), and natural gas prices. For each commodity, combinations that enhance GARCH most effectively are established as proxies of attention. The results indicate investor attention is widely reflected in internet search activities. The results also demonstrate search data for what keywords best reveal the direction of concern and attention in energy markets.

Keywords: Google Search Activity, Energy Market, Volatility Prediction, Energy Price Volatility

JEL Classification: C53, Q02, Q47

Suggested Citation

Afkhami, Mohamad and Cormack, Lindsey and Ghoddusi, Hamed, The Most Predictive Energy Search Terms (October 16, 2016). Stevens Institute of Technology School of Business Research Paper No. 2853004, Available at SSRN: https://ssrn.com/abstract=2853004 or http://dx.doi.org/10.2139/ssrn.2853004

Mohamad Afkhami

Stevens Institute of Technology - School of Business ( email )

Hoboken, NJ 07030
United States

Lindsey Cormack

Stevens Institute of Technology - College of Arts and Letters ( email )

Hoboken, NJ 07030
United States
4028179330 (Phone)
4028179330 (Fax)

Hamed Ghoddusi (Contact Author)

Economic Research Forum ( email )

Cairo
Egypt

California State Polytechnic University, San Luis Obispo ( email )

San Luis Obispo, CA 93407
United States

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