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    Dr. Janet Yinqing Zhao

    Janet joined the research team of Moody's Analytics in 2008. She leads RiskCalc model development and small business modeling efforts. Janet works closely with clients to facilitate better understanding and applications of RiskCalc models. She also pushes forward on research initiatives such as exposure-at-default modeling, accounting quality measurement, and machine learning in credit risk modeling. She has published in academic and professional journals. Janet has a PhD in finance from City University of Hong Kong and a PhD in accounting from Carnegie Mellon University.
    Published Work

    Assessing Financial Statement Quality

    Is a financial statement decision useful? Is it informative enough to make a loan, acquire a company, increase a limit or move a borrower to work out? The quality of financial statements is a concern for all firms, especially as the demand for faster and more accurate due diligence grows.

    November 2019

    Usage and Exposures at Default of Corporate Credit Lines — An Empirical Study

    Using a unique data set pooled from multiple U.S. financial institutions, we empirically study the credit line usage of middle-market corporate borrowers.

    September 2019

    Sovereign & Size-Adjusted EDF-Implied Rating Template (for Private Firms)

    RiskCalc™ EDF™ (Expected Default Frequency) values and agency ratings are widely used credit risk measures. RiskCalc EDF values typically measure default risk for private companies, while agency ratings are only available for rated companies. A RiskCalc EDF value measures a company's standalone credit risk based on financial statement information, while an agency rating considers qualitative factors such as Business Profile, Financial Policy, external support, and country-related risks. Moody's Analytics new Sovereign & Size-Adjusted EDF-Implied Rating Template combines RiskCalc EDF values with additional factors to provide a rating comparable to agency ratings for private companies. The new template applies to RiskCalc EDF values across numerous geographies and regulatory environments. With the new template, users can generate a rating more comparable to an agency rating than RiskCalc EDF values or EDF-implied ratings. Analyzing data from 3,900+ companies in 60+ countries, we find that sovereign rating and total asset size, in addition to EDF value, have a statistically significant impact on an agency rating — our quantitative template incorporating these three variables reliably estimates agency ratings in a robust fashion.

    December 2018

    Identifying At-Risk Names in Your Private Firm Portfolio — RiskCalc Early Warning Toolkit

    This report outlines a practical approach for using RiskCalc EDF credit measures to effectively monitor large portfolios of private firms and to proactively identify at-risk names. The RiskCalc Early Warning Toolkit Excel add-in is an easy to use, yet comprehensive tool that allows users to focus costly and scarce resources on a highly targeted selection of the most at-risk names in their portfolios. This research for private firms compliments previous research on Early Warning Toolkit for public firms. The Early Warning Toolkit identifies at-risk names within a private firm portfolio well before default, using a number of different EDF-related risk metrics.

    November 2018

    Features of a Lifetime PD Model: Evidence from Public, Private, and Rated Firms

    With the new CECL and IFRS 9 requirements, this document formally investigates and summarizes the term structure properties consistently seen across public, private, and rated firms.

    May 2018

    Applications of Alternative Data in Credit Decisioning

    With an immense amount of available data generated worldwide within the last two years, the next evolution of banking analytics will include information from a variety of open and closed sources.

    April 2018