Opinion Mining for Relating Subjective Expressions and Annual Earnings in US Financial Statements
This addresses financial analysts and regulators by providing a method to detect linguistic patterns in corporate disclosures, though it is incremental in applying existing NLP techniques to financial data.
The paper tackled the problem of extracting opinionated statements from U.S. financial reports to analyze how managers use subjective language, finding that managers use more optimistic words to obscure negative performance and assertive statements for positive performance.
Financial statements contain quantitative information and manager's subjective evaluation of firm's financial status. Using information released in U.S. 10-K filings. Both qualitative and quantitative appraisals are crucial for quality financial decisions. To extract such opinioned statements from the reports, we built tagging models based on the conditional random field (CRF) techniques, considering a variety of combinations of linguistic factors including morphology, orthography, predicate-argument structure, syntax, and simple semantics. Our results show that the CRF models are reasonably effective to find opinion holders in experiments when we adopted the popular MPQA corpus for training and testing. The contribution of our paper is to identify opinion patterns in multiword expressions (MWEs) forms rather than in single word forms. We find that the managers of corporations attempt to use more optimistic words to obfuscate negative financial performance and to accentuate the positive financial performance. Our results also show that decreasing earnings were often accompanied by ambiguous and mild statements in the reporting year and that increasing earnings were stated in assertive and positive way.