Supervised learning models to predict firm performance with annual reports: An empirical study. (20th November 2013)

• Record Type: Journal Article
• Authors: Qiu, Xin Ying; Srinivasan, Padmini; Hu, Yong
• Is Part Of: Journal of the Association for Information Science and Technology. Volume 65:Number 2(2014:Feb.)
• Page Start: 400
• Page End: 413
• Publication Date: 2013-11-20
• Subjects: Information science -- Periodicals; Information technology -- Periodicals; 020.5
• DOI: 10.1002/asi.22983
• Languages: English
• ISSNs: 2330-1635

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Text mining and machine learning methodologies have been applied toward knowledge discovery in several domains, such as biomedicine and business. Interestingly, in the business domain, the text mining and machine learning community has minimally explored company annual reports with their mandatory disclosures. In this study, we explore the question "How can annual reports be used to predict <italic>change</italic> in company performance from one year to the next?" from a text mining perspective. Our article contributes a systematic study of the potential of company mandatory disclosures using a computational viewpoint in the following aspects: (a) We characterize our research problem along distinct dimensions to gain a reasonably comprehensive understanding of the capacity of supervised learning methods in predicting change in company performance using annual reports, and (b) our findings from unbiased systematic experiments provide further evidence about the economic incentives faced by analysts in their stock recommendations and speculations on analysts having access to more information in producing earnings forecast.</p> </abstract>