Opinion Mining for Relating Subjective Expressions and Annual Earnings in US Financial Statements

TL;DR

This paper develops CRF-based models to extract subjective opinion statements from US financial reports, revealing patterns of optimistic language use and their relation to financial performance.

Contribution

It introduces a novel approach focusing on multiword expression opinion patterns in financial texts, enhancing opinion detection accuracy.

Findings

Managers tend to use optimistic language to mask negative results.

Decreasing earnings are often described with mild or ambiguous statements.

Increasing earnings are associated with assertive and positive expressions.

Abstract

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.