A review of artificial intelligence and biologically inspired computational approaches to solving issues in narrative financial disclosure

Minhas, Saliha; Poria, Soujanya; Hussain, Amir; Hussainey, Khalid

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.

Citation

Minhas, S., Poria, S., Hussain, A., & Hussainey, K. (2013, June). A review of artificial intelligence and biologically inspired computational approaches to solving issues in narrative financial disclosure. Presented at BICS 2013: 6th International Conference on Brain Inspired Cognitive Systems, Beijing, China

• Presentation Conference Type: Conference Paper (published)
• Conference Name: BICS 2013: 6th International Conference on Brain Inspired Cognitive Systems
• Start Date: Jun 9, 2013
• End Date: Jun 11, 2013
• Publication Date: 2013
• Deposit Date: Oct 11, 2019
• Publisher: Springer
• Pages: 317-327
• Series Title: Lecture Notes in Computer Science
• Series Number: 7888
• Series ISSN: 0302-9743
• Book Title: Advances in Brain Inspired Cognitive Systems: 6th International Conference, BICS 2013, Beijing, China, June 9-11, 2013. Proceedings
• ISBN: 978-3-642-38785-2
• DOI: https://doi.org/10.1007/978-3-642-38786-9_36
• Keywords: Narrative Financial Disclosure; computational approaches; SenticNet
• Public URL: http://researchrepository.napier.ac.uk/Output/1793098