Does audit report information improve financial distress prediction over Altman's traditional Z‐Score model?

• Author: Nora Muñoz‐Izquierdo,David Pascual‐Ezama,Erkki K. Laitinen,María‐del‐Mar Camacho‐Miñano
• Published date: 01 February 2020
• DOI: http://doi.org/10.1111/jifm.12110
• Date: 01 February 2020

J Int Financ Manage Account. 2020;31:65–97.

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65

wileyonlinelibrary.com/journal/jifm

Received: 28 July 2017

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Revised: 24 September 2017

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Accepted: 27 February 2019

DOI: 10.1111/jifm.12110

ORIGINAL ARTICLE

Does audit report information improve financial

distress prediction over Altman's traditional Z‐Score

model?

NoraMuñoz‐Izquierdo1

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Erkki K.Laitinen2

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María‐del‐MarCamacho‐Miñano3

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DavidPascual‐Ezama3

© 2019 John Wiley & Sons Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

1Department of Finance and

Accounting,CUNEF (Colegio

Universitario de Estudios Financieros),

Madrid, Spain

2Department of Accounting and

Finance,University of Vaasa, Vaasa,

Finland

3Department of Financial Management

and Accounting,Faculty of Economics

and Business,Complutense University of

Madrid, Madrid, Spain

Correspondence

Nora Muñoz‐Izquierdo, Department of

Finance and Accounting, CUNEF (Colegio

Universitario de Estudios Financieros),

Leonardo Prieto Castro, 2, Ciudad

Universitaria, 28040, Madrid, Spain.

Email: nmunoz@cunef.edu

Funding information

CUNEF; Complutense University of

Madrid

Abstract

We analyze empirically the usefulness of combining ac-

counting and auditing data in order to predict corporate

financial distress. Concretely, we examine whether audit

report information incrementally predicts distress over a

traditional accounting model: the Altman's Z‐Score model.

Although the audit report seems to play a critical part in

financial distress prediction because auditors should warn

investors about any default risks, this is the first study

that uses audit report disclosures for predicting purposes.

From a dataset of 1,821 Spanish distressed private firms,

we analyze a sample of distressed and non‐distressed firms

and develop logit prediction models. Our results show that

while the only accounting model registers a classification

accuracy of 77%, combined models of accounting and au-

diting data exhibit considerably higher accuracy (about

87%). Specifically, our findings indicate that the number of

disclosures included in the audit report, as well as disclo-

sures related to a firm's going concern status, firms’ assets,

and firms’ recognition of revenues and expenses contribute

the most to the prediction. Our empirical evidence has im-

plications for financial distress practice. For managers, our

study highlights the importance of audit report disclosures

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MUÑOZ‐IZQUIERDO Etal.

1

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INTRODUCTION

The topic of financial distress has been widely studied in the literature due to its negative conse-

quences on both microeconomic and macroeconomic levels. Many stakeholders suffer from the effects

of a firm dealing with financial difficulties: from the shareholders of the business to its employees,

customers, suppliers, financial institutions, and society, in general. Although there have been numer-

ous studies on distress prediction in the past decades, an effort to improve the accuracy of prediction

models continues to be needed (Balcaen & Ooghe, 2006; Bauweraerts, 2016; Du Jardin, 2015).

The literature on the modeling of corporate financial distress starts with the pioneering works

of Beaver (1966) and Altman (1968), which are based on financial ratios. Since then, different ap-

proaches have been applied to improve prediction accuracy, such as the selection of other financial

ratios (see the reviews by Bellovary, Giacomino, & Akers, 2007; Tascón‐Fernández & Castaño‐

Gutiérrez, 2012); the application of more complex statistical and intelligent techniques1

like logistic

analysis, hazard models, or artificial intelligence (reviews by Balcaen & Ooghe, 2006; Kumar & Ravi,

2007); and the extension of traditional financial models with other variable sets like market variables

(Hernández‐Tinoco & Wilson, 2013; Hillegeist, Keating, Cram, & Lundstedt, 2004; Merton, 1974)

and non‐financial variables (Altman, Iwanicz‐Drozdowska, Laitinen, & Suvas, 2015; Altman, Sabato,

& Wilson, 2010; Back, 2005; Cheng, Yeh, & Chiu, 2007; Keasey & Watson, 1987; Laitinen, 1999;

Lussier, 1995).

Studies that highlight the benefits of incorporating non‐financial information in combination with

financial ratios usually supplement financial factors with variables such as firm age, type of busi-

ness and industry (Grunert, Norden, & Weber, 2005), legal form, payment behavior, management

structure (Laitinen, 1991), or group membership (Back, 2005). This trend of research also includes

auditing data as non‐financial factors. The most common examples are the type of auditor's opinion

(Altman etal., 2010, 2015; Flagg, Giroux, & Wiggins, 1991; Wilson, Wright, & Scholes, 2013), the

going concern opinion—generally issued when a firm's going concern status is in doubt—(Altman &

McGough, 1974; Altman etal., 2010), number of qualified audits (Keasey & Watson, 1987; Piñeiro‐

Sánchez, De Llano‐Monelos, & Rodríguez‐López, 2013), auditor switching (Altman etal., 2010;

Keasey & Watson, 1987), and auditor size and tenure (Piñeiro‐Sánchez etal., 2013). However, these

papers do not focus on the content of audit reports for anticipating financial distress (Muñoz‐Izquierdo,

Camacho‐Miñano, & Pascual‐Ezama, 2017), and a related study of Piñeiro‐Sánchez etal. (2013, pp.

168) literally suggests “improving the codification of the qualifications to enhance the accuracy of

the model.”

Thus, our investigation helps to fill this gap by empirically assessing the extent to which the combi-

nation of accounting and audit data included in the audit report predicts financial distress. The aim of

for anticipating a financial distress situation. For regulators

and auditors, our study underscores the importance of recent

changes in regulation worldwide intended to increase audi-

tor's transparency through a more informative audit report.

KEYWORDS

Altman's Z‐Score, audit report, emphasis of matter sections, financial

distress prediction, private companies, qualifications

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MUÑOZ‐IZQUIERDO Etal.

this paper focuses on whether the classification accuracy of the Altman's Z’’‐Score model is improved

by qualitative variables that represent the content of audit report disclosures.

Starting from a dataset of 1,821 financially distressed firms, a matched sample of 808 private

manufacturing and non‐manufacturing Spanish firms—404 distressed and 404 non‐distressed com-

panies—is manually created compiling financial, audit, and legal information from two data sources:

Bureau Van Dijk database (hereafter BVD)2

and “Registro Público Concursal” (hereafter RPC).3

We

adopt the occurrence of the firm's insolvency filing as our definition of a distressed firm (Lizarraga‐

Dallo, 1998; Piñeiro‐Sánchez etal., 2013). This legal definition can be applied as the current Spanish

law is based on a single court proceeding. This means that the legal procedure begins with the insol-

vency filing when a company is under financial distress, and the process finishes with either the reor-

ganization or the liquidation of the firm.4

The 404 distressed firms in our sample filed for insolvency

proceedings between 2004 and 2014. For the non‐distressed firms’ selection, the matching procedure

is done by hand, based on year, size, and industry, as in prior literature (Blay, Geiger, & North, 2011;

Charitou, Lambertides, & Trigeorgis, 2007; Knechel & Vanstraelen, 2007; Schwartz & Menon, 1985).

In this study, the Altman's Z’’‐Score is used as the benchmark model. First, this model is chosen

because the sample consists of private companies from different industries, and this is the version

developed by Altman for private and public manufacturing and non‐manufacturing firms (Altman,

1983). Second, and most importantly, the Z’’‐Score is selected due to its relevance, high recurrence,

and popularity in prior research. A recent study by Altman, Iwanicz‐Drozdowska, Laitinen, and Suvas

(2017, pp. 133–134) argues that “even though the Z‐Score model was developed more than 45years

ago and many alternative failure prediction models exist, the Z‐Score model continues to be used

worldwide as a main or supporting tool for bankruptcy or financial distress prediction and analysis

both in research and in practice.”

This paper follows with the benchmark model supplemented by audit report information variables,

examining their effect on the performance in terms of classification accuracy. Using the audit report

codification of 20 dummy variables developed in Muñoz‐Izquierdo etal. (2017), the content of the

audit reports of the whole sample is extracted and manually labeled. In this codification, three of the

variables represent the type of paragraph in which the disclosure is included (emphasis of matter sec-

tion, scope limitation, or GAAP5

violation), and the seventeen remaining variables typify the content

of each disclosure. These variables include accounting issues as well as more general comments made

by the auditors. The complete classification will be explained in the next sections. It is important to

note that such a broad investigation of audit data and, more specifically, an analysis of the audit report

information has not been presented so far in corporate distress prediction studies (Altman etal., 2010;

Laitinen & Laitinen, 2009a).

For all estimation models, logistic regression analysis is used following prior research (Balcaen &

Ooghe, 2006), and predictions are provided for a horizon of one year. Thus, the ability of information

in the period prior to filing is assessed to predict financial distress in the following year. Due to the

manual process of analyzing every audit report in detail, the horizon is not expanded to more years.

Also, prior studies demonstrate that typical accounting‐based models are useful for prediction for 1 or

2years prior to bankruptcy (Altman etal., 2015).

Our results show that the combined use of financial and non‐financial factors leads to a more

accurate prediction of distress events than the single use of each of these factors. While the evidence

from our sample of Spanish firms indicates that the predictive power of the Z”‐Score model is 77%,

the classification accuracy improves 10% units (up to 87%) when audit report information is consid-

ered. Interestingly, consistent with prior literature that applies samples from different periods and

countries—for example, Altman etal. (2010) using a sample of 5.8 million small‐ and medium‐sized

enterprises from the United Kingdom in the period 2000–2007—our results highlight the benefits