Managing Director, Research
Douglas heads the Moody’s Analytics single obligor research group. This group produces credit risk metrics of small businesses, medium-sized enterprises, large corporations, financial institutions, and sovereigns worldwide. The group’s models are used by banks, asset managers, insurance companies, accounting firms, and corporations to measure name-specific credit risk for a wide variety of purposes. We measure credit risk using information drawn from financial statements, regulatory filings, security prices, derivative contracts, and behavioral and payment information. Previously, Doug was a principal at William M. Mercer, Inc. He has a PhD from Columbia University and a BA from Oberlin College, both in economics.
Combining Financial and Behavioral Information to Predict Defaults for Small and Medium-Sized Enterprises – A Dynamic Weighting Approach
One large challenge lenders currently face is how to combine different types of information into metrics that can support good business decisions. Currently, the banking industry uses two primary types of information — financial information and behavioral information — independently, to assess risk. Financial information includes Income Statement, Balance Sheet, Cash Flow, and Financial Ratios. Behavioral information includes spending and payment patterns, among others. Both types of information provide unique insights, but, to date, they have not been combined to generate one comprehensive risk metric for commercial use.
In this article, we discuss the issues associated with acquiring behavioral and financial data and transforming it into a business decision. We also present a unified modeling approach for combining the information into a credit risk assessment for both small firms and medium-sized enterprises.
In this article, we combine financial information with behavioral factors to more accurately assess credit risk for small firms and medium-sized enterprises.
There has been a significant increase in the demand for quantitative tools that assess the default risk of banks across different geographies. Pooling data from more than 90 countries, we see commonalities in linking default risk to a specific set of financial ratios. This finding suggests that a prescribed set of financial ratios, properly transformed, works well in estimating banks' default risk in a robust fashion. With this insight, we constructed the RiskCalc™ Banks v4.0 Model, intended for assessing the probability of default (PD) for banks across different geographies and regulatory environments. The model provides a unified framework to assess bank risk across different countries and regions, as well as different economic cycles. The one-year model is based upon a set of well-defined and ready-to-calculate financial ratios that effectively measure bank profitability, leverage, liquidity, growth, and asset quality. The five-year model combines these ratios with a measure derived from an economic capital framework based upon portfolio theory. Specifically, this measure captures the unexpected loss of a bank's loan portfolio relative to its loss-absorbing capital. Validation results show that the model delivers strong and robust power in rank ordering high risk banks from low risk banks, and that the results are robust across geographies and bank sizes.
This article discusses the role of third-party data and analytics in the stress testing process. Beyond the simple argument that more eyes are better, we outline why some stress testing activities should definitely be conducted by third parties.
EDF9 — the 9th generation of the Moody's Analytics Public Firm EDFTM (Expected Default Frequency) model — expands the frontiers of structural credit risk modeling. EDF metrics are forward-looking probabilities of default, available on a daily basis for 35,000-plus corporate and financial firms. The updated EDF9 model incorporates insights attained by evaluating the behavior of the prior version, EDF8, over the course of the recent financial and sovereign debt crises.
This semiannual report examines credit risk in the otherwise opaque U.S. private firm credit market. We report trends in 4 different areas of risk measurement.
In this paper, we detail a RiskCalc™ Stress Testing Model (ratio-based approach), based upon economic and accounting principles. Our simple, yet fundamental, model assumptions make the framework adaptable to many uses, including: loss forecasting, pro forma analysis, stress testing, as a challenger or benchmark model, and for customized scenario analysis.
To help our clients build benchmark commercial and industrial (C&I) loss models for the Federal Reserve's Comprehensive Capital Analysis and Review (“CCAR”)/DFAST exercises, we have developed an approach designed specifically to calculate provisions for losses of C&I portfolios. Our approach utilizes Moody's Analytics probability of default (PD), loss given default (LGD), and exposure at default (EAD) econometric models, which are intuitive, parsimonious, make economic sense, and have good statistical fit. We construct these models using our public EDF credit measures, RiskCalc™ private firm EDF credit measures, and Moody's Default & Recovery Database and Credit Research Database.
A major source of firm funding and liquidity, credit lines can pose significant credit risk to the underwriting banks. Using a unique dataset pooled from multiple U.S. financial institutions, we empirically study the credit line usage of middle market corporate borrowers. We examine to what extent borrowers draw down their credit lines and the characteristics of those firms with high usage. We study how line usage changes with banks' internal ratings, collateral, and commitment size and through various economic cycles. We find that defaulted borrowers draw down more of their lines than non-defaulted borrowers. They also increase their usage when approaching default. Risky borrowers tend to utilize a higher percentage of their credit lines as well.
This semiannual report examines credit risk in the otherwise opaque U.S. private firm credit market. We report trends in four different areas of risk measurement: realized defaults, internal bank ratings, financial statement-based information, and model-based risk estimates. We derive the statistics in this report from Moody's Analytics CRD™ (Credit Research Database).
Learn about stress testing best practices and our RiskCalc™ Plus United States Stress Testing Models. This webinar focuses on stress testing best practices for the private company C&I asset class.
This semiannual report examines credit risk in the otherwise opaque US private firm credit market. We report trends in four different areas of risk measurement: realized defaults, internal bank ratings, financial statement-based information, and model-based risk estimates. We derive the statistics in this report from Moody's Analytics Credit Research Database® (CRD).
This semiannual report examines credit risk in the otherwise opaque US private firm credit market. We report trends in four different types of risk measures: actual defaults, internal bank ratings, financial statement-based information, and model-based risk estimates. The statistics in this report are derived from Moody's Analytics Credit Research Database® (CRD).
Analysts often find themselves working with less than perfect development and/or validation samples and data issues typically impact the interpretation of default prediction validation tests. Discriminatory power and calibration of default probabilities are two key aspects of validating default probability models. Both are susceptible to data issues. In this paper, we look at how data issues affect three important power tests: the Accuracy Ratio, the Kolmogorov-Smirnov test, and the Conditional Information Entropy Ratio, as well as how data issues affect the Hosmer-Lemeshow test, a default probability calibration test. We employ a simulation approach that allows us to assess the impact of data issues on model performance when the exact nature of the data issue is known.
In this presentation we examine the strengths of a risk calculation model that assesses localized accounting practices of individual countries within the wider context of the credit cycle. The model takes account of liquidity, profitability, activity, leverage, growth variables and other integrated factors to deliver objective results. Here we put the spotlight on exactly what this model can do and how it works.
This methodology proposes a combined approach to credit valuation and provides the framework for it.
In this paper, we present a framework that links two commonly used risk metrics: default probabilities and credit spreads. This framework provides credit default swap-implied (CDS-implied) EDF™ (Expected Default Frequency) credit measures that can be compared directly with equity-based EDF credit measures.
This document outlines the validation results for the RiskCalc v3.1 U.S. Banks model, and highlights the deteriorating financial ratios present in the banking sector. We contrast trends of key risk measures to those of the savings and loan crisis of the late 1980s and early 1990s. We also explore the speed and nature of recent bank failures and demonstrate the model?s strong performance in light of this rapidly changing environment.
In this paper, we validate the Moody's KMV RiskCalc v3.1 United States private firm default model. We show that the EDF™ (Expected Default Frequency) produced by the model continues to rank order risk effectively by providing substantial discriminatory power across multiple cuts of the data.
Quantitative rating systems are increasingly being used for the purposes of capital allocation and pricing credits. For these purposes, it is important to validate the accuracy of the probability of default (PD) estimates generated by the rating system and not merely focus on evaluating the discriminatory power of the system. The validation of the accuracy of the PD quantification has been a challenge, fraught with theoretical difficulties (mainly, the impact of correlation) and data issues (eg, the infrequency of default events).
The Moody's KMV Expected Default Frequency model of public firms is the pioneering implementation of a structural model that gives investors the ability to monitor credit risk across a broad range of firms. The release of the EDF™ 8.0 model represents a major recalibration of the model, which incorporates both a larger default dataset and improved estimation techniques that derive the EDF term structure from credit migration.
Default databases play a key role in the development, validation and application of credit models. Nevertheless, it has often been difficult to ascertain the extent to which these databases accurately capture all of the default events that have occurred over a particular time period or market segment. While it is generally understood that not all default events are captured in any one dataset, estimates of the magnitude of missed defaults are previously non-existent (to our knowledge) even though such information is extremely valuable for credit risk management.
The key to building default prediction models, if they are to be incorporated into credit risk management systems, is to build the most powerful model possible subject to the constraints that it is transparent, usable, and intuitive. In this process, we must constantly be on guard for whether or not we have overfit the data.
Moody's KMV RiskCalc® V3.1 Model: Next-Generation Technology for Predicting Private Firm Credit Risk
This white paper outlines the methodology, performance, and key economic benefits of the Moody's KMV EDF™ (Expected Default Frequency™) RiskCalc model, which powers the next-generation of default prediction technology for middle market, private firms.