Methods for measurement of delays

 

The conventional method for assessing delays is to run a survey of insolvency practitioners (judges, lawyers, accountants). In this article, we offer a new idea for measuring one part of the delay. There are many parts of the bankruptcy process. Many societies try to avoid the problem of failed firms by putting things off. This leads to a delay between the actual firm failure and the commencement of the formal bankruptcy process. Could this be measured directly? We could start from the date of the end (the date of the official legal action about firm closure), and look back to the date on which extreme credit distress is identified. The gap between the two events will serve as a proxy for delays of the bankruptcy process. Let's play this idea out with the failure of Kingfisher Airlines. The decisive end date is 18th November, 2016, when the Karnataka High Court ordered the winding up of Kingfisher Airlines (See here). Conventional notion of delay: The case was filed by an unsecured (foreign) creditor on 18th September, 2012. It took the legal system four years to come to the conclusion that Kingfisher Airlines is insolvent. Looking back using interest coverage ratio (ICR) as a measure of credit stress. This is defined as the ration of the earnings of a company (EBITDA) and the total interest payments due. The logic being that if a company's income is not enough to even pay the interest on its borrowing, there is acute distress. The literature in finance uses ICR in a couple of different ways when assessing firm distress. Here are three examples:

 

ICR below 1

 

Distress is where the ICR falls below 1 in any year. Example: Claessens, Djankov, and Lang 1998.

 

ICR below 0.75

 

Distress is where the ICR for any financial year falls below 0.75. Example: Love, 2010.

 

P-ICR

 

Distress is Persistent-ICR, which is defined as an ICR of less than 1 for three consecutive financial years. Example: Chung and Ratnovski, 2016.

 

We extract the annual financial data for Kingfisher Airlines from CMIE Prowess database. The data is available from March 2001 till March 2013. Figure 1 below shows when Kingfisher Airlines met these criteria. It broke into ICR-below-1 and ICR-below-0.75 in March 2005 (red line) and it achieved P-ICR in March 2007 (orange line). The court finally ordered winding up 3520 days after Kingfisher meeting all the three criteria (gray line). The black line (October 20, 2012) is when DGCA suspended Kingfisher's license.

 

Figure 1: Interest cover ratio for Kingfisher Airlines

 

 

Looking back using Distance to Default as a measure of credit stress. Distance to Default (DtD) is measured as the difference between the asset value of the firm and the face value of its debt, scaled by the standard deviation of firm's asset value. It measures the distance (in standard deviations) between the expected value of the firm and the "default point" (face value of the debt). Thus, lower values of DtD imply that the firm is more likely to default on its financial obligations. DtD is implemented in R using the ifrogs package developed by IGIDR Finance Research Group.

 

Figure 2: Distance to default for Kingfisher Airlines

 

 

Figure 2 shows the DtD for Kingfisher Airlines from June 2007 onwards. As the listing took place on 12 June 2006, the DtD calculation only commences on 12 June 2007. The red line shows the date when DtD was below 1 for the first time (August 1, 2008). DtD reached its lowest value of 0.30 on July 6, 2009 (orange line). Thus, at that point the likelihood of default was the highest (19 percent). 

 

Conclusions

 

The approach shown above with Kingfisher is potentially scalable into large datasets and all countries. This could yield large-scale measurement, at the level of individual bankruptcy transactions, about delays. This can yield useful summary statistics. The new insolvency law should reduce the time between financial insolvency and the legal recognition of the same. This should yield measurable gains when these techniques are applied to the data in the future. Our approach yields micro data about delays which can be analysed in order to explore cross-sectional and time-series variation. Such analysis is not feasible with the conventional measurement of delays that is based on surveying practitioners. The authors would like to thank Radhika Pandey, Rajat Kochhar and Mohit Desai for their inputs.

 

The authors are Dhananjay Ghei and Shubho Roy, Consultants at NIPFP, New Delhi.

 

The views expressed in the post are those of the authors only. No responsibility for them should be attributed to NIPFP.