Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman's Z‐Score Model

• DOI: http://doi.org/10.1111/jifm.12053
• Author: Edward I. Altman,Erkki K. Laitinen,Małgorzata Iwanicz‐Drozdowska,Arto Suvas
• Published date: 01 June 2017
• Date: 01 June 2017

Financial Distress Prediction in an Interna-

tional Context: A Review and Empirical

Analysis of Altman’s Z-Score Model

doi:10.1111/jifm.12053

Edward I. Altman

NYU Salomon Center, Henry Kaufman Management Center, New York University, Stern

School of Business, 44 West Fourth Street, New York, NY 10012, USA

Małgorzata Iwanicz-Drozdowska

Institute of Finance, Warsaw School of Economics, al. Niepodleglosci 162, 02-513 Warsaw,

Poland

e-mail: miwani@sgh.waw.pl

Erkki K. Laitinen

University of Vaasa, P.O.Box 700, FI-65101 Vaasa, Finland

e-mail: erkki.k.laitinen@uwasa.fi

Arto Suvas

University of Vaasa, P.O.Box 700, FI-65101 Vaasa, Finland

e-mail: asuvas@welho.com

Abstract

This paper assesses the classification performance of the Z-Score model in predicting

bankruptcy and other types of firm distress, with the goal of examining the model’s

usefulness for all parties, especially banks that operate internationally and need to

assess the failure risk of firms. We analyze the performance of the Z-Score model for

firms from 31 European and three non-European countries using different modifications

of the original model. This study is the first to offer such a comprehensive international

analysis. Except for the United States and China, the firms in the sample are primarily

private, and include non-financial companies across all industrial sectors. We use the

original Z00-Score model developed by Altman, Corporate Financial Distress: A Com-

plete Guide to Predicting, Avoiding, and Dealing with Bankruptcy (1983) for private and

public manufacturing and non-manufacturing firms. While there is some evidence that

Z-Score models of bankruptcy prediction have been outperformed by competing mar-

ket-based or hazard models, in other studies, Z-Score models perform very well. With-

The authors are grateful to the Editor, Richard Levich, and to the anonymous referees for

many helpful comments and suggestions. We also wish to thank participants at the 2014 Inter-

national Risk Management Conference (IRMC) in Warsaw for useful discussions. Laitinen and

Suvas thank the Foundation for Economic Education, and Jenny and Antti Wihuri Foundation

for financial support. Laitinen also thanks OP-Pohjola Group Research Foundation for

support.

Journal of International Financial Management & Accounting 28:2 2017

©2016 John Wiley & Sons Ltd.

out a comprehensive international comparison, however, the results of competing mod-

els are difficult to generalize. This study offers evidence that the general Z-Score model

works reasonably well for most countries (the prediction accuracy is approximately

0.75) and classification accuracy can be improved further (above 0.90) by using coun-

try-specific estimation that incorporates additional variables.

1. Introduction

The first multivariate bankruptcy prediction model was developed by

Altman (1968) in the late 1960s. After this pioneering work, the

multivariate approach to failure prediction spread worldwide among

researchers in finance, banking, and credit risk. Failure prediction mod-

els are important tools for bankers, investors, asset managers, rating

agencies, and even distressed firms themselves. The banking industry, as

the main provider of financing in the economy, is especially interested in

minimizing the level of non-performing loans in order to maximize profit

on credit activity, and banks seek to reduce their own risk of default.

Another issue of interest for bankers is capital adequacy and the internal

ratings-based approach encouraged by the Basel Accords. The Z-Score

model has become a prototype for many of these models. Asset man-

agers and investors need reliable tools that can help them select appro-

priate companies for their portfolios. Financial distress is detrimental to

investor returns, but risk may provide opportunities for high returns on

short-sale strategies. Rating agencies assess the risk of the entities and of

securities issues, and thus, they need a tool to predict default. Altman

(1983) suggested that the management of distressed firms can utilize the

Z-Score model as a guide to financial turnaround.

The approach used for bankruptcy prediction has evolved over time.

Beaver (1966, 1968) used univariate analysis for selected ratios and

found that some had very good predictive power. Altman (1968) made

strides by developing a multiple discriminant analysis model (MDA)

called the Z-Score model. The next two decades saw additional contri-

butions to financial distress research.

1

For example, Ohlson (1980) pro-

posed a logit model,

2

Taffler (1984) offered a Z-Score model for the

United Kingdom, and Zmijewski (1984)

3

used a probit approach. Dim-

itras et al. (1996) reviewed 47 studies on business prediction models,

summarizing the methods employed and the variety of ratios used.

Discriminant analysis was the prevailing method, and the most impor-

tant financial ratios came from the solvency category, with profitability

ratios also being important.

132 Edward I. Altman, Małgorzata Iwanicz-Drozdowska, Erkki K. Laitinen and Arto Suvas

©2016 John Wiley & Sons Ltd

Balcaen and Ooghe (2006) reviewed 43 models of business failure pre-

diction which they classified into four categories: univariate models (1);

risk index models (2); MDA models (21); and conditional probability

models (19). However, their review omitted the rapidly growing type of

models based on option pricing theory and contingent claims (e.g., Vas-

salou and Xing, 2004; commercialized into Kealhofer, McQuown and

Vasicek’s model, known as the KMV model), as well as hazard models

(e.g., Shumway, 2001). Kumar and Ravi (2007) reviewed 128 statistical

and artificial intelligence models for bank and firm bankruptcy predic-

tions, paying special attention to the techniques used in the different

models. These authors noted that neural networks were the most popu-

lar intelligence technique. In their review, Jackson and Wood (2013) pre-

sented the frequency of occurrence of specific forecasting techniques in

the prior literature. The five most popular techniques were as follows: (1)

multiple discriminant analysis, (2) logit models, (3) neural networks, (4)

contingent claims, and (5) univariate analysis.

Recent reviews of the efficacy of these models have been offered by

Agarwal and Taffler (2008), Das et al. (2009), and Bauer and Agarwal

(2014). These reviews take into account the performance of account-

ing-based, market-based, and hazard models. These three model types

prevail in the literature. According to Agarwal and Taffler (2008),

there is little difference in the predictive accuracy of accounting-based

and market-based models; however, the use of accounting-based mod-

els allows for a higher level of risk-adjusted return on credit activity.

Das et al. (2009) showed that accounting-based models perform com-

parably to the Merton structural, market-based approach for credit

default spread (CDS) estimation. However, a comprehensive model,

which used both sources of variables, outperformed the other models.

In Bauer and Agarwal (2014), hazard models using accounting and

market information (Shumway, 2001; Campbell et al., 2008) were com-

pared with two other approaches: the original Taffler (1984) Z-score

model, which was tested by Agarwal and Taffler (2008), and a contin-

gent claims model using Bharath and Shumway’s (2008) approach.

Using U.K. data, the hazard models were superior in bankruptcy pre-

diction accuracy, ROC (Receiver Operating Characteristic) analysis,

and information content.

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

Financial Distress Prediction 133

©2016 John Wiley & Sons Ltd