Company Data

StarMine Combined Credit Risk Model

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  1. Overview
  2. Features & benefits
  3. How it works
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An overview of StarMine Combined Credit Risk Model

The StarMine Combined Credit Risk Model (CCR) combines information from the StarMine Structural, SmartRatios, and Text Mining Credit Risk Models into one final estimate of corporate credit risk. By incorporating information from multiple independent data sources – from the equity market, from analyst estimates and financials, and from analysis of the language in important textual documents – and placing the most emphasis on the inputs that are most effective for a given company, StarMine CCR creates powerful default predictions and assessments of credit risk that are more accurate than using any one data source alone.

The CCR model uses a logistic regression framework. The weights partitioned to the Text Mining model, and the other two models are also conditioned on the volume of text on a given company, such that the weight on the Text Mining model increases with increasing text volume. This model calculates the probability that a company could default within 12 months.

Key Facts 

  • Geographical coverage
    Global
  • History
    From 1998
  • Data format
    CSV
    Delimited
    GZIP
    HTML
    JSON
    Python
    SQL
    Text
    User Interface
    XML
    Zip Archive
  • Delivery mechanism
    API
    Deployed/Onsite Servers
    Desktop
    Excel
    FTP
    SFTP
  • Data frequency
    Daily

Features & Benefits

What you get with StarMine Combined Credit Risk Model

  • CCR model intelligently handles missing data and make use of all the component inputs available, but requires only one to score a company.
  • Incorporates momentum in credit risk, which is more pronounced on the downside.
  • The CCR Model is StarMine’s best estimate of credit risk that incorporates information from the StarMine Structural, SmartRatios, and Text Mining Credit Risk Models into one final estimate of credit risk.
  • Weighting between Text Mining and other models is conditioned on volume of text – weight on Text Mining increases with volume of text.

RELEVANT USE CASES

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How it works

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