Predicting Default More Accurately: To Proxy or Not to Proxy for Default?

• Published date: 01 December 2019
• Author: Koresh Galil,Neta Gilat
• Date: 01 December 2019
• DOI: http://doi.org/10.1111/irfi.12197

Predicting Default More

Accurately: To Proxy or Not

to Proxy for Default?*

KORESH GALIL AND NETA GILAT

Department of Economics, Ben-Gurion University, Beersheba, Israel

ABSTRACT

Previous studies targeting accuracy improvement of default models mainly

focused on the choice of the explanatory variables and the statistical

approach. We alter the focus to the choice of the dependent variable. We

particularly explore whether the common practice (in the literature) of using

proxies for default events (bankruptcy or delisting) to increase sample size

indeed improves accuracy. We examine four definitions of financial distress

and show that each definition carries considerably different characteristics.

We discover that rating agencies effort to measure correctly the timing of

default is valuable. Our main conclusion is that one cannot improve default

prediction by making use of other distress events.

JEL Codes: G17; G33

Accepted: 17 April 2018

I. INTRODUCTION

Previous studies targeting accuracy improvements of default models have

focused on finding the optimal set of explanatory variables or specifying the

optimal methodology used to estimate the likelihood of failure. A third aspect

concerning the prediction of financial distress, which has not received much

attention, is the definition of financial distress, which seems to vary signifi-

cantly among different studies. Altman (1968) and Ohlson (1980) attempted to

forecast bankruptcy, which they identified with a firm’sfiling of a bankruptcy

petition. Dichev (1998) outlined a broader definition of distress, by addressing

firms that were delisted because of poor performance as his sample for failed

firms. Shumway (2001) used bankruptcy and delisting events as an indicator of

financial distress. Campbell et al. (2008) used a failure indicator that included

bankruptcy filings, delisting for financial reasons, or receiving a D rating.

* The authors would like to thank Zvika Afik, Banita Bissoondoyal-Bheenick, Doron Kliger, Jan

Pieter Krahnen and Gal Zahavi, and seminar participants in Operational Research International Con-

ference (ORIC) 2012 meeting in Jerusalem, International Finance and Banking Society (IFABS) 2012

meeting in Valencia, and Multinational Financial Society (MFS) meeting in Prague for fruitful

comments.

© 2018 International Review of Finance Ltd. 2018

International Review of Finance, 19:4, 2019: pp. 731–758

DOI: 10.1111/irfi.12197

Bharath and Shumway (2008) defined distress as default; they obtained their

default data from the database of firm default maintained by Edward Altman

and by using the list of defaults published by Moody’s. In this paper, we alter

the focus to the choice of the dependent variable and explore whether the com-

mon practice of using proxies for default events to increase sample size indeed

improves accuracy.

There are several possible reasons for researchers’practice of using proxies

for default events. First, a standard dataset of default events among US public

companies is nonexistent, and therefore, researchers rely on diverse sources for

the construction of their events lists. Second, the number of default events is

relatively small. Moreover, once such a list is intersected with other data

(e.g. accounting or market data), the final set becomes even smaller. Under

these terms, it is tempting to use alternative distress definitions (proxies for

defaults) in order to expand the set of failure events.

The proximity of the default events to other types of negative events assists

in identifying such proxies. A financial default is a state in which a debtor is

unable or unwilling to fulfill the terms of a debt contract or a debt instrument.

Such an event may come after the occurrence of other negative events, such as

a rating downgrade and a major drop in the value of the equity. A default may

also precede other types of financial distress events, such as bankruptcy filing

and delisting. Rating agencies exert effort in identifying default events and their

exact timing. Moody’sdefinition of default includes three types of credit

events: (i) a missed or delayed disbursement of interest and/or principal;

(ii) bankruptcy filing or legal receivership; and (iii) a distressed exchange.

1,2

The

time of default is set (by the rating agency) to be the earliest of the above events

because it is then that the major loss is recognized. Missed or delayed payments

and distressed exchanges normally precede bankruptcy filings, and therefore,

default events (as defined by rating agencies) normally precede bankruptcy

events.

3

Moody’s (2000) emphasizes that the alternative definitions of default

are not intended to broaden the central idea of nonpayment or bankruptcy, but

simply to get the timing right. Yet, Moody’sdefinition of default is also slightly

broader than the definition of bankruptcy because it also includes delayed pay-

ments. Such events do not necessarily lead to bankruptcies as debtors could be

repaid later on; however, rating agencies still consider them as default events

because of the meaningful opportunity costs they load on investors.

1 This definition appears in various default studies by Moody’s. See, for example, Moody’s

(2011, p. 61). S&P definition of default is similar. See, for example, Standard and Poor’s

(2011, p. 65).

2 A distressed exchange is an event in which the issuer offers bondholders a new security or

package of securities that amount to a diminished financial obligation (such as preferred or

common stock, or debt with a lower coupon or par-amount) helping the borrower to avoid

the other types of default.

3 Brunner and Krahnen (2008) showed in the context of bank debt that private workout activi-

ties usually commence well before formal bankruptcy proceedings are initiated, and therefore,

a bankruptcy filing may be a late indicator of financial distress.

© 2018 International Review of Finance Ltd. 2018732

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