Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/16518
Appears in Collections:Computing Science and Mathematics Book Chapters and Sections
Peer Review Status: Refereed
Title: A review of artificial intelligence and biologically inspired computational approaches to solving issues in narrative financial disclosure
Authors: Minhas, Saliha
Poria, Soujanya
Hussain, Amir
Hussainey, Khaled
Contact Email: amir.hussain@stir.ac.uk
Editors: Liu, D
Alippi, C
Zhao, D
Hussain, A
Citation: Minhas SZ, Poria S, Hussain A & Hussainey K (2013) A review of artificial intelligence and biologically inspired computational approaches to solving issues in narrative financial disclosure In: Liu D, Alippi C, Zhao D, Hussain A (ed.) Advances in Brain Inspired Cognitive Systems: 6th International Conference, BICS 2013, Beijing, China, June 9-11, 2013. Proceedings, Berlin Heidelberg: Springer. 6th International Conference on Brain Inspired Cognitive Systems, BICS 2013, 9.6.2013 - 11.6.2013, Beijing, China, pp. 317-327.
Keywords: Narrative Financial Disclosure
Biologically Inspired
SenticNet
Issue Date: 2013
Publisher: Springer
Series/Report no.: Lecture Notes in Computer Science, 7888
Abstract: Indisputably, financial reporting has a key role to play in the efficient workings of capitalist economies. Problems related to agency and asymmetric information (Jensen and Meckling, 1976) would abound and cripple financial markets, as it has done when left unchecked (Enron, WorldCom and Tyco). However for too long, quantitative data has monopolised the assessment and prediction role within this arena and this has contributed to the failures, borne out by research (Kumar & Ravi, 2007). As qualitative data proliferates, containing value relevant information it needs to be factored into the analysis. This paper reviews work on financial narrative disclosures and looks at conventional artificial intelligence and more recent biologically inspired computational approaches to catapult the domain to more progressive methods of using linguistic data in evaluations.
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
Type: Conference Paper
URI: http://hdl.handle.net/1893/16518
URL: http://link.springer.com/chapter/10.1007/978-3-642-38786-9_36#
Affiliation: Computing Science and Mathematics
Jadavpur University
Computing Science - CSM Dept
Accounting and Finance

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