The Moody’s Analytics EDF™ (Expected Default Frequency) metric and Early Warning Toolkit highlighted the company’s rising default risk starting 18 months prior to default. This case study provides an example on how to use the firm’s EDF measure and the Early Warning Toolkit to assess risk.
Introduction
Conalvias Construcciones S.A.S., a civil construction company with operations in Peru, Panama, the United States, and Colombia, struggled to generate enough cash flow to meet its debt payments, after significantly increasing its debt. In September 2015, the company began a reorganization process. In June 2019, the company started the liquidation process.
Could the RiskCalc EDF Credit Measure and the Early Warning Toolkit Have Helped Flag Rising Default Risk and Avoid Credit Losses?
Moody’s Analytics RiskCalc™ suite is a collection of geographic- or industry-specific models designed for private firm default risk measurement. RiskCalc combines financial statement information and equity market information into a standalone, forward-looking credit risk metric — the RiskCalc EDF credit measure. Our Early Warning Toolkit provides a framework used for analyzing the EDF measure and provides additional insights for when a default event may be imminent. This case study utilizes financials sourced from Bureau van Dijk (BvD), which are run through the RiskCalc Emerging Markets 3.1 Model, along with the RiskCalc Early Warning Toolkit.
The toolkit examines default risk among five EDF-based metrics:
1. EDF Level
The most important of the five measures, EDF level is the starting point for credit assessment. To help guide decision-making, we produce trigger levels by region, above which firms show higher risk.
At the time of the company’s reorganization process announcement, Conalvias EDF measure was 4.98% (Figure 1), which mapped to an implied rating of B2 on Moody’s rating scale, a level historically consistent with speculative-grade firms at a relatively high risk of default (Figure 2). At the time of its liquidation announcement, Conalvias EDF measure was 17.67%, which mapped to a Caa/C implied rating on Moody’s rating scale. Moreover, as shown in Figure 1, Conalvias EDF measure trended above its trigger-level beginning in March 2015, six months before the reorganization process announcement.
2. EDF Change
Firms with an increasing EDF measure show a higher risk of default. Conalvias EDF measure showed an increasing trend beginning 2011. Its level increased from 0.52% in March 2011 to 2.6% in March 2014, before rising to 3.89% in March 2015. As of March 2019 its EDF level was 18.11%. This sustained increase provided another signal of elevated default risk.
3. Relative EDF Level and 4. Relative EDF Change
The relative EDF level is the percentile ranking of a firm’s EDF measure compared to its peers in the corresponding model development dataset. The higher the percentile, the higher the firm’s EDF level compared to its peers, and, therefore, the higher the company’s credit risk relative to its peers. Similarly, Relative EDF change can help identify firms whose relative financial strength is worsening significantly compared to peers.
In March 2011 Conalvias EDF measure, was among the safest names compared to the development dataset (10th Percentile). Since then, the firm’s EDF measure started to deteriorate, crossing the 50th percentile in March 2014, and rising above the 75th percentile in September 2015. At the time of the liquidation announcement, its EDF measure was above the 90th percentile.
5. EDF Term Structure
An inverted term structure (i.e. the five-year annualized EDF sits below the one-year EDF), indicates that the firm faces elevated risk during the near-term.
Figure 3 shows Conalvias one-year EDF measure and its five-year, annualized EDF measure. We observe that the EDF curve inverted in May 2014, with the gap between the one- and five-year EDF measures widening during 2015 and 2016.
Deeper Dive: Understanding the Drivers of Conalvias EDF Measure
The Relative Contribution graph (Figure 4) quantifies how much each ratio contributes to the one-year and five-year EDF values. For each ratio, if the evaluated company performs more poorly than the average firm from the development sample, i.e., the value of the ratio makes the firm riskier relative to an average firm, the relative contribution for that ratio will be positive. By the same token, a ratio will have a negative relative contribution if this ratio makes the firm safer relative to an average firm.
Most of Conalvias’ financial ratios as of 2018 had a positive relative contribution, indicating higher risk than its peers. The largest contributors were Liabilities to Assets and Return on Assets, indicating high leverage and low profitability.
In addition to relative contribution, the Percentile Graph (Figure 5) shows the company’s ratios relative to those of its peers within the model development database. The percentile shows the percentage of companies that have a ratio level less than or equal to the given company’s ratio level. Graphically, the green portion of the percentile spectrum has a decreasing effect on the EDF value. Similarly, percentiles falling in the red portion of the spectrum have an increasing effect on the EDF value.
In the case of Conalvias, percentile values for the financial ratios also indicate most of the factors did not compare well relative to other firms.
Conclusion
The Early Warning Toolkit highlighted elevated risk for Conalvias prior to default. Eighteen months prior to default, the firm had a significant increase on EDF, crossing the 50th percentile, with an EDF measure of 2.6%, which mapped to an implied rating of B1 on Moody’s rating scale. Sixteen months prior to default, the EDF curve inverted. Six months prior to default, the EDF measure reached 3.88%, B2 on Moody’s rating scale, and started trending above its trigger level.
A prudent early warning framework can provide advance warning of credit events and allow users to take appropriate action to mitigate losses.
