Mining Opinion Holders and Opinion Patterns in US Financial Statements

Subjective statements provide qualitative evaluation of the financial status of the reporting corporations, in addition to the quantitative information released in US 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. The CRF models showed reasonable effectiveness to find opinion holders in experiments when we adopted the popular MPQA corpus for training and testing. We also identified opinion patterns in the form of multi-word expressions (MWEs), which is a major contribution of our work. In a recent article published in a prestigious journal in Finance, single words, rather than MWEs, were reported to indicate positive and negative judgments in financial statements.