Abstract
Comparability of financial statements has been a subject that is often referred to by academics and practitioners alike. In recent years, researchers have attempted to develop a quantifiable framework to study the benefits of comparability from the perspective of equity markets. Kim et al. (2013) approach this issue from the perspective of credit markets. This discussion of their paper has three objectives. First, it critiques their proxy for comparability and offers suggestions on how to validate their assumptions. Second, it recommends improvements to their research design, keeping in mind nuances of credit as an asset class. Finally, to help the authors with their future research, it offers proxies for comparability and information asymmetry that can be developed through some new datasets that have become available to researchers.
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Notes
One of the most widely used tools for assessing a model’s ability to correctly rank-order ex post default risk is the cumulative accuracy profile (CAP). Depicted graphically, a CAP curve shows the cumulative percent of observed defaults attributed to a ranking of observations by risk scores (or any other ranking system, such as bond ratings). It is essentially a plot of true positive rate (TPR) versus false positive rate (FPR) at any given threshold. Accuracy ratio (AR) is the area lying between a given CAP curve and a 45° line, divided by the total area lying above the 45° line. As such, an AR is a statistic that reflects both type I (false negative) and type II (false positive) error. An AR of 100 % would indicate that all defaulting issuers received the very lowest classification (assigning them the highest probability of default). See Crossen et al. (2011) for more details.
This is a variant of a likelihood test in which the probability of realizing a certain number of defaults is compared for two different default risk distributions coming from two different models.
For example, Moody’s has other offerings like CDS-implied or bond-spread-implied ratings or EDFs (equity-based default probabilities). These are described in Dwyer et al. (2010).
See Arora et al. (2005) for more details.
The Merton model assumes that equities are a call option on the underlying assets with the debt of the firm being the strike of the option. In this setup, the default risk can be stated as a function of three variables: the market valuation of assets, the volatility of assets, and the debt as a percentage of market value of assets. These three are combined to form a sufficient statistic called distance-to-default (DD), which is essentially the number of standard deviations a firm’s asset valuation is away from the point of default. DDs are then mapped to default probabilities based on either a theoretical or empirical mapping. Moody’s Analytics provides this output as a part of its proprietary model trademarked as EDF Model.
This study by Moody’s gets updated annually, but the numbers are fairly stable, since they are aggregated over multiple decades.
Credit loss rate is expressed as the product of default rate and loss given default. Credit spreads are closely approximated by credit loss rates.
See Arora et al. (2012a) for a study that uses a similar dataset to analyze a different research question.
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Arora, N. Discussion of “Financial statement comparability and credit risk”. Rev Account Stud 18, 824–832 (2013). https://doi.org/10.1007/s11142-013-9239-6
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DOI: https://doi.org/10.1007/s11142-013-9239-6