Olympic news and attitudes towards the Olympics: A compositional time-series analysis of how sentiment is affected by events

Terence Mills, Peter Dawson and Paul Downward
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Terence Mills: University of Loughborough
Paul Downward: University of Loughborough

No 46, University of East Anglia Applied and Financial Economics Working Paper Series from School of Economics, University of East Anglia, Norwich, UK.

Abstract: The Olympic Games are considered to be the most prestigious multi-sport event in the world. However, with growing costs associated with hosting such events against a backdrop of questionable economic benefits, and yet elation that follows from sporting success, a number of studies have started to address its intangible or softer impacts as a justification for the investment. It is well known that sentiment plays a part in the evolving economic valuation of companies through the stock market. What is less well known is how ‘news’ affects the sentiment towards major public investments like the Olympics. In this paper we consider, from the context of the pre-event stage of the 30th Olympiad, the relationship between attitudes towards the Olympics and Olympic-related news; specifically the bad news associated with an increase in the cost of provision, and the good news associated with Team GB’s medal success in 2008. Using a unique data set and an event-study approach that involves compositional time-series analysis, it is found that ‘good’ news affects sentiments much more than ‘bad’ but that the distribution of such sentiment varies widely. For example, a much more pronounced effect of good news is identified for females than males, but ‘bad’ news has less of an impact on the young and older age groups. The paper consequently argues that extreme caution should be exercised in policy pronouncements that are based on such sentiments.

Date: 2013-05
New Economics Papers: this item is included in nep-cbe, nep-spo and nep-tur
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Journal Article: Olympic news and attitudes towards the Olympics: a compositional time-series analysis of how sentiment is affected by events (2014)
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