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

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journal contribution
posted on 18.12.2015, 14:48 by Peter Dawson, Paul Downward, Terence C. Mills
Sentiment 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 Statistics

Volume

41

Issue

6

Pages

1307 - 1314

Citation

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 & Francis

Version

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

2014

Notes

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/10.1080/02664763.2013.868417

ISSN

0266-4763

eISSN

1360-0532

Language

en

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