Credit rating change modeling using news and financial ratios

Hsin Min Lu, Feng Tse Tsai, Hsinchun Chen, Mao Wei Hung, Shu Hsing Li

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Credit ratings convey credit risk information to participants in financial markets, including investors, issuers, intermediaries, and regulators. Accurate credit rating information plays a crucial role in supporting sound financial decision-making processes. Most previous studies on credit rating modeling are based on accounting and market information. Text data are largely ignored despite the potential benefit of conveying timely information regarding a firm's outlook. To leverage the additional information in news full-text for credit rating prediction, we designed and implemented a news full-text analysis system that provides firm-level coverage, topic, and sentiment variables. The novel topic-specific sentiment variables contain a large fraction of missing values because of uneven news coverage. The missing value problem creates a new challenge for credit rating prediction approaches. We address this issue by developing a missingtolerant multinomial probit (MT-MNP) model, which imputes missing values based on the Bayesian theoretical framework. Our experiments using seven and a half years of real-world credit ratings and news full-text data show that (1) the overall news coverage can explain future credit rating changes while the aggregated news sentiment cannot; (2) topic-specific news coverage and sentiment have statistically significant impact on future credit rating changes; (3) topic-specific negative sentiment has a more salient impact on future credit rating changes compared to topic-specific positive sentiment; (4) MT-MNP performs better in predicting future credit rating changes compared to support vector machines (SVM). The performance gap as measured by macroaveraging F-measure is small but consistent.

Original languageEnglish (US)
Article number14
JournalACM Transactions on Management Information Systems
Volume3
Issue number3
DOIs
StatePublished - Oct 1 2012

Keywords

  • Credit rating changes
  • Latent dirichlet allocation
  • Missing-tolerant multinomial probit
  • News coverage
  • News sentiment
  • SVM
  • Topic-specific news coverage
  • Topic-specific news sentiment

ASJC Scopus subject areas

  • Management Information Systems
  • Computer Science(all)

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