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The influence of twitter sentiment in the EU emissions trading scheme

Deeney, Peter orcid logoORCID: 0000-0002-8112-8692, Cummins, Mark orcid logoORCID: 0000-0002-3539-8843, Dowling, Michael orcid logoORCID: 0000-0002-8093-9039 and Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 (2016) The influence of twitter sentiment in the EU emissions trading scheme. In: Energy and Commodity Finance Conference, 23-24 June 2016, Paris, France.

Abstract
We test sentiment measured from Tweets concerning climate change and the EU emissions market, on high frequency price data. Our first main finding is that changes in the sentiment of Tweets specifically concerned with the EU emissions market predict changes in EUA prices two hours ahead with evidence of bi-directional Granger causality between changes in sentiment and changes in EUA prices. Further, we establish that periods of above (below) average sentiment correspond with periods of high (low) EUA return volatility. These findings show that sentiment does indeed have an influence in the EU emissions market. Our second finding is that while energy commodity prices, particularly NBP gas and to a lesser extent Brent oil, can account for some of the movement of contemporaneous EUA prices they are not useful at predicting these changes. This indicates that the emissions market assimilates new information from the energy market quickly. Our third main finding is that there is no evidence that Twitter sentiment concerning the general topics of climate change and global warming (rather than specifically the EU emissions market) is associated with EUA returns. This indicates that the principal means by which the EU is addressing climate change, namely the use of emissions trading, does not seem to register in the general Tweeting of Europeans about climate change and global warming
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:behavioural finance; sentiment; EU ETS; market effciency
Subjects:Business > Finance
Business > Economic policy
Computer Science > Algorithms
DCU Faculties and Centres:DCU Faculties and Schools > DCU Business School
Research Institutes and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Official URL:http://easychair.org/smart-program/ECOMFIN2016/ind...
Funders:Science Foundation ireland
ID Code:21322
Deposited On:10 Oct 2016 10:19 by Alan Smeaton . Last Modified 21 Feb 2022 13:34
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