Journal article
Supervised learning models to predict firm performance with annual reports: An empirical study
Journal of the Association for Information Science and Technology, Vol.65(2), pp.400-413
02/2014
DOI: 10.1002/asi.22983
Abstract
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 change 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.
Details
- Title: Subtitle
- Supervised learning models to predict firm performance with annual reports: An empirical study
- Creators
- Xin Ying Qiu - Guangdong University of Foreign StudiesPadmini Srinivasan - The University of IowaYong Hu - Sun Yat‐sen University
- Resource Type
- Journal article
- Publication Details
- Journal of the Association for Information Science and Technology, Vol.65(2), pp.400-413
- DOI
- 10.1002/asi.22983
- ISSN
- 2330-1635
- eISSN
- 2330-1643
- Number of pages
- 14
- Language
- English
- Date published
- 02/2014
- Academic Unit
- Nursing; Computer Science; Business Analytics
- Record Identifier
- 9984003187402771
Metrics
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