This thesis shows that sentiment has influence in professionally traded oil and emissions markets. The sentiment index of Baker and Wurgler (2006) is adapted for the oil markets and is used to show that sentiment has a positive effect on WTI and Brent crude oil prices. Having established the value of this index in the oil markets it is extended to include the wider energy markets and used to show that sentiment also has an effect in the EU emissions trading scheme (EU ETS). It is found that there is some evidence that decisions of the European Parliament (EP) are associated with a drop in emission allowance (EUA) prices particularly when these decisions occur at times of low sentiment, low news exposure and when they come from non-party political sources. It is found that an increase in volatility of EUA returns is associated with EP decisions made at these times.
In order to investigate further the effect of sentiment in the EU ETS, sentiment measured from tweets concerning the emissions market is shown to predict price level and volatility using intra-day data. Bi-directional Granger causality is found between changes in emissions market sentiment and EUA returns, this is especially true for negative sentiment. There is only very weak evidence of an association between climate change sentiment and the EUA returns showing that the EU ETS is not very high in the consciousness of people posting tweets about climate change. Finally, there is some evidence that energy commodity prices and stock market returns can explain, but not predict, EUA prices. This suggests that the EU ETS is efficient with regard to this fundamental information but that in general the Efficient Market Hypothesis does not provide a complete description of the market dynamics.
This thesis therefore shows not only that the Efficient Market Hypothesis does not provide a complete description of market dynamics but that sentiment does not rely on uninformed traders to have a real and substantial effect in the emissions and oil markets.
Item Type:
Thesis (PhD)
Date of Award:
November 2017
Refereed:
No
Supervisor(s):
Cummins, Mark and Smeaton, Alan F. and Dowling, Michael