An Extreme Firm-Specific News Sentiment Asymmetry Based Trading Strategy

7 Pages Posted: 17 Jul 2015

See all articles by Qiang Song

Qiang Song

Stevens Institute of Technology

Anqi Liu

Cardiff University - School of Mathematics

Steve Y. Yang

Stevens Institute of Technology

Anil Deane

Northrop Grumman Corporation

Kaushik Datta

Northrop Grumman Corporation

Date Written: July 15, 2015

Abstract

News sentiment has been empirically observed to have impact on financial market returns. In this study, we investigate firm-specific news from the Thomson Reuters News Analytics data from 2003 to 2014 and propose an optimal trading strategy based on a sentiment shock score and a sentiment trend score which measure extreme positive and negative sentiment levels for individual stocks. The intuition behind this approach is that the impact of events that generate extreme investor sentiment changes tends to have long and lasting effects to market movement and hence provides better prediction to market returns. We document that there exists an optimal signal region for both indicators. And we also show extreme positive sentiment provides better a signal than extreme negative sentiment, which presents an asymmetric market behavior in terms of news sentiment impact. The backtest results show that extreme positive sentiment generates robust and superior trading signals in all market conditions, and its risk-adjusted returns significantly outperform the S&P 500 index over the same time period.

Keywords: News Sentiment; Thomson Reuters News Analytics; Extreme Sentiment Shock and Sentiment Trend; Trading Strategy.

JEL Classification: G14, G17, G11, D83, D84

Suggested Citation

Song, Qiang and Liu, Anqi and Yang, Steve Y. and Deane, Anil and Datta, Kaushik, An Extreme Firm-Specific News Sentiment Asymmetry Based Trading Strategy (July 15, 2015). Available at SSRN: https://ssrn.com/abstract=2631135 or http://dx.doi.org/10.2139/ssrn.2631135

Qiang Song

Stevens Institute of Technology ( email )

Hoboken, NJ 07030
United States

Anqi Liu

Cardiff University - School of Mathematics ( email )

Senghennydd Road
Cardiff, CF24 4AG
United Kingdom

Steve Y. Yang (Contact Author)

Stevens Institute of Technology ( email )

Hoboken, NJ 07030
United States

Anil Deane

Northrop Grumman Corporation ( email )

2980 Fairview Park Drive
Falls Church, VA 22042
United States

Kaushik Datta

Northrop Grumman Corporation ( email )

2980 Fairview Park Drive
Falls Church, VA 22042
United States

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