El-Haj, Mahmoud and Rayson, Paul Edward and Young, Steven Eric and Walker, Martin and Moore, Andrew and Athanasakou, Vasiliki and Schleicher, Thomas (2016) Learning tone and attribution for financial text mining. In: Proceedings of LREC 2016, Tenth International Conference on Language Resources and Evaluation :. European Language Resources Association (ELRA), pp. 1820-1825. ISBN 9782951740891
elhajlrec2016pea.pdf - Accepted Version
Available under License Creative Commons Attribution-ShareAlike.
Download (242kB)
Abstract
Attribution bias refers to the tendency of people to attribute successes to their own abilities but failures to external factors. In a business context an internal factor might be the restructuring of the firm and an external factor might be an unfavourable change in exchange or interest rates. In accounting research, the presence of an attribution bias has been demonstrated for the narrative sections of the annual financial reports. Previous studies have applied manual content analysis to this problem but in this paper we present novel work to automate the analysis of attribution bias through using machine learning algorithms. Previous studies have only applied manual content analysis on a small scale to reveal such a bias in the narrative section of annual financial reports. In our work a group of experts in accounting and finance labelled and annotated a list of 32,449 sentences from a random sample of UK Preliminary Earning Announcements (PEAs) to allow us to examine whether sentences in PEAs contain internal or external attribution and which kinds of attributions are linked to positive or negative performance. We wished to examine whether human annotators could agree on coding this difficult task and whether Machine Learning (ML) could be applied reliably to replicate the coding process on a much larger scale. Our best machine learning algorithm correctly classified performance sentences with 70% accuracy and detected tone and attribution in financial PEAs with accuracy of 79%.