olympic news and attitudes Compositonal Analysis sept 2013.pdf (162.08 kB)
Olympic news and attitudes towards the Olympics: a compositional time-series analysis of how sentiment is affected by events
journal contribution
posted on 2015-12-18, 14:48 authored by Peter Dawson, Paul DownwardPaul Downward, Terence C. MillsSentiment affects the evolving economic valuation of companies through the stock market. It is unclear 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 Great Britain'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. © 2013 Taylor & Francis.
History
School
- Sport, Exercise and Health Sciences
Published in
Journal of Applied StatisticsVolume
41Issue
6Pages
1307 - 1314Citation
DAWSON, P., DOWNWARD, P. and MILLS, T.C., 2014. Olympic news and attitudes towards the Olympics: a compositional time-series analysis of how sentiment is affected by events. Journal of Applied Statistics, 41 (6), pp. 1307 - 1314.Publisher
© Taylor & FrancisVersion
- AM (Accepted Manuscript)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Publication date
2014Notes
This is an Accepted Manuscript of an article published in the Journal of Applied Statistics on 19 Dec 2013, available online: http://www.tandfonline.com/10.1080/02664763.2013.868417ISSN
0266-4763eISSN
1360-0532Publisher version
Language
- en