Market Volatility Timing with Google Query

Authors

Wei Feng, Lynn University
Robert W. Reich, Lynn University

Document Type

Article

Publication Date

4-2018

Abstract

This paper explores the use of Google trending data as a indicator for market sentiment. The Google query record on keywords including stock, market, correction, and crash are incorporated into an event based trading model for S&P 500 index in an attempt to identify significantly enhanced risk-profile of the trading results. Our study showed that the collective Google query can be an effective measure of market perception of risk. Furthermore, the collective perception on market risk, either over or under-reacted, can be a prelude indicator of immediate market volatility.

Publication

Journal of Applied Financial Research

Publisher

Academy of Business Research

Volume

1

Pages

29-36

Department

College of Business and Management

Recommended Citation

Feng, W. & Reich, R. W. (2018). Market Volatility Timing with Google Query. Journal of Applied Financial Research, 1, 29-36.