Negativity Bias in the European Emissions Market: Evidence from High-Frequency Twitter Sentiment

38 Pages Posted: 21 May 2018

See all articles by Peter Deeney

Peter Deeney

Dublin City University Business School

Mark Cummins

Dublin City University Business School

Michael M. Dowling

ESC Rennes School of Business

Alan Smeaton

Dublin City University - School of Computing

Date Written: May 6, 2018

Abstract

High-frequency sentiment time series are extracted from Twitter data concerning the European emissions market and are used to explain returns and volatility in European emissions futures. The measures of negative sentiment are shown to Granger-cause EUA futures returns, while positive sentiment does not, indicating the presence of a negativity effect. Using a Threshold GARCH model, it is shown that periods of strong (weak) sentiment correspond with periods of high (low) volatility. Our findings are significant for understanding the role of sentiment in European emissions markets with implications for other markets subject to uncertain political actions and public opinions.

Keywords: Emissions Market, Sentiment, Twitter

JEL Classification: G41, Q52, G13

Suggested Citation

Deeney, Peter and Cummins, Mark and Dowling, Michael M. and Smeaton, Alan, Negativity Bias in the European Emissions Market: Evidence from High-Frequency Twitter Sentiment (May 6, 2018). Available at SSRN: https://ssrn.com/abstract=3174353 or http://dx.doi.org/10.2139/ssrn.3174353

Peter Deeney (Contact Author)

Dublin City University Business School ( email )

Dublin 9
Ireland

Mark Cummins

Dublin City University Business School ( email )

Dublin 9
Ireland

Michael M. Dowling

ESC Rennes School of Business ( email )

Rue Robert d'arbrissel, 2
Rennes, 35000
France

Alan Smeaton

Dublin City University - School of Computing ( email )

Glasnevin, Dublin 9 Co
Dublin
Ireland

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