February 25, 2021

Review an Empirical Analysis of Altman’s Z-Score Model doi:10.1111/jifm.12053

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: [email protected]

Erkki K. Laitinen University of Vaasa, P.O.Box 700, FI-65101 Vaasa, Finland e-mail: [email protected]

Arto Suvas University of Vaasa, P.O.Box 700, FI-65101 Vaasa, Finland e-mail: [email protected]

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

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

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

research and in practice. We focus on accounting-based versions of the

Z-Score models, which even though they are occasionally outper-

formed by other models, do not rely on market data. Most firms oper-

ating in business are privately held; hence, only accounting data and

no market data (e.g., stock prices) are available. Private firms are usu-

ally financed by banks, which are obligated to assess their creditwor-

thiness and monitor their performance. In the case of internationally

active banks, from a regulatory perspective it is especially important to

use a single model for distress prediction, provisioning, and economic

capital calculation. According to current Basel regulatory require-

ments, banks need to validate their distress prediction models and doc-

ument their efficacy. Thus, it is important to analyze the performance

of accounting-based models in an international context.

In our study, we use a large international sample of firms to assess

the classification performance of the Z-Score model in bankruptcy pre-

diction. 4 We analyze the model’s performance for firms from 31 Euro-

pean and three non-European countries (China, Colombia and the

United States). These firms are mostly privately held, and a large num-

ber are from non-manufacturing industries. We use the version of the

model developed by Altman (1983) for private and public manufactur-

ing and non-manufacturing firms (the Z”-Score model). Such an exten-

sive international analysis of the Z-Score model’s performance has not

been presented to date. We regard our review and analysis as impor-

tant contributions to the economic literature.

The remainder of the paper is structured as follows. In the next sec-

tion, we summarize the original Z-Score model (Altman, 1968) and its

extension for private firms, that is, the Z’-Score and Z”-Score models

(Altman, 1983). In the third section, we present the results and conclu-

sions from the literature review on these models. The fourth section pre-

sents seven hypotheses on the performance of the Z”-Score model that

we will subject to empirical analysis. In the fifth section, we discuss the

empirical data and statistical methods, while the sixth section presents

empirical findings. Finally, the seventh section summarizes the study.

2. Classic Z-Score Models

2.1. Z-Score Model for Public Firms

Altman’s (1968) initial sample was composed of 66 corporations, with

33 firms in each of two groups. The bankrupt group (Group 1) con-

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

sisted of manufacturers that filed bankruptcy petitions under Chapter

X of the National Bankruptcy Act during the 1946–1965 period. The mean asset size of these firms was 6.4 million USD, ranging between

0.7 and 25.9 million USD. Altman recognized that this group was not

homogenous with respect to size and industry, although all firms were

relatively small and from manufacturing industries. He attempted to

carefully select non-bankrupt firms (Group 2). Group 2 consisted of a

paired sample of manufacturing firms chosen on a stratified random

basis. These firms were stratified by industry and size, with the asset

size range restricted to 1–25 million USD. Altman eliminated small firms (less than 1 million U.S.A. dollars in total assets) because of a

lack of data and very large firms because of the rarity of bankruptcies

among these firms in that period. He did not match the asset size of

the two groups exactly, and therefore, the firms in Group 2 were

slightly larger than those in Group 1. The data collected for the firms

in both groups were from the same years. For Group 1, the data were

derived from financial statements one reporting period prior to bank-

ruptcy.

Using financial statements, Altman compiled a list of 22 potentially

important financial ratios for evaluation. He classified these variables

into five standard ratio categories: liquidity, profitability, leverage, sol-

vency, and activity. These ratios were chosen based on their popularity

in the literature and their potential relevance to the study. The final

discriminant function estimated by Altman (1968) is as follows:

Z ¼ 0:012�X1 þ 0:014�X2 þ 0:033�X3 þ 0:006�X4 þ 0:999�X5 ð1Þ

where X1 = Working Capital/Total Assets; X2 = Retained Earnings/ Total Assets; X3 = Earnings before Interest and Taxes/Total Assets; X4 = Market Value of Equity/Book Value of Total Liabilities; X5 = Sales/Total Assets; Z = Overall Index.

2.2. Z’-Score and Z”-Score Models for Private Firms

The original Z-Score model was based on the market value of the firm

and was thus applicable only to publicly traded companies. Altman

(1983) emphasized that the Z-Score model is intended for publicly

traded firms and that ad hoc adjustments are not scientifically valid.

Altman (1983) advocated a complete re-estimation of the model, sub-

Financial Distress Prediction 135

stituting the book value of equity for the market value in X4. Using

the same data, Altman extracted the following revised Z’-Score model:

Z0 ¼ 0:717�X1 þ 0:847�X2 þ 3:107�X3 þ 0:420�X4 þ 0:998�X5 ð2Þ

where X4 = Book value of equity/Book value of total liabilities, with the other variables the same as those in the original (1968) Z-Score

model.

Due to the lack of a private firm database, Altman did not test the

Z’-Score model on a secondary sample. However, he analyzed the

accuracy of a four-variable Z”-Score model that excluded the Sales/

Total assets ratio, X5, from the revised model because of a potential

industry effect that is more likely to take place when this kind of

industry-sensitive variable (asset turnover) is included in the model.

Altman then estimated the following four-variable Z”-Score model

(Altman, 1983):

Z00 ¼ 3:25 þ 6:56�X1 þ 3:26�X2 þ 6:72�X3 þ 1:05�X4 ð3Þ

The EBIT/Total assets ratio, X3, contributed most to the discrimina-

tion power in this version of the model. The classification results for

the Z”-Score model were identical to the revised five-variable Z’-Score

model. In the current study, our empirical analysis focuses on the per-

formance of the Z”-Score model version in predicting bankruptcy,

where it has its widest scope, as it is intended for both privately held

and publicly listed firms and for both manufacturing and non-manu-

facturing firms.

3. Survey of Literature Related to the Altman Z-Score Model

We focus on papers published after 2000 in prominent international

journals and books. 5 Of the many articles and books identified, we

selected 31 articles in which the Z-Score was either used as a failure

prediction proxy or assessed mostly in terms of predictive ability. Of

the 31 studies, Altman’s Z-Score model was used in 16 cases as the

measure of distress or of financial strength. 6 In 13 studies, Altman’s

original model was modified and (or) verified, including re-estimation,

and in two cases, it was used solely for the robustness check. As Pin-

dado et al. (2008) noted, the Z-Score was also used for other purposes,

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

such as the evaluation of the costs and benefits of covenants in bonds,

the choice of debt type (bank versus non-bank, private or public), and

the relationship between investment and internal funds. We focused on

this part of the literature that verified and (or) modified Altman’s

original model. The broad use of the Z-Score model for measuring

financial distress and performing robustness checks indicates its accept-

ability as a reasonable, simple, and consistent measure of distressed

firms.

The most common modification to the Z-Score model was the use

of other estimation techniques or country-specific data. The use of

Altman’s ratios in combination with techniques other than MDA

improved its prediction capability. The application of new data on

both United States and non-US firms also improved model perfor-

mance. Kwak et al. (2005) used Multiple Criteria Linear Programming

(MCLP) to model 5 Altman and 9 Ohlson variables with data on

bankrupt US firms from 1992 to 1998 and nearly six times more

matched U.S.A. control firms. The MCLP approach performed better

than Altman’s original model and gave results similar to or better than

those of Ohlson’s original model. The original models were not recal-

culated, and the authors referred solely to their original prediction

rates.

Merkevicius et al. (2006), using data on United States and Lithua-

nian firms, developed a hybrid artificial discriminant model combining

MDA and an unsupervised learning artificial neural network. This

hybrid SOM-Altman model reached a high prediction rate of 92.35 per

cent. Xu and Zhang (2009) applied Altman’s Z-score, Ohlson’s O-

score, and Merton’s distance-to-default (D-score) models to Japanese

firms to check whether these models are useful for bankruptcy predic-

tion in Japan. They also “merged” these models into a new C-score

model. They then introduced variables unique to Japan to check

whether corporate structure variables have any impact on the probabil-

ity of bankruptcy; they called this the X-score model. These two mod-

els were useful for Japanese firms in predicting bankruptcy, but the

market-based model was the most successful. In summary, the C-score

and X-score (with country-specific variables) models improved bank-

ruptcy prediction.

Tinoco and Wilson (2013) used the original Z-Score as one of the

benchmarks to assess the performance of their model developed for

U.K. listed companies with combined accounting, market, and macroe-

conomic data. Altman’s Z-Score presented very good classification

Financial Distress Prediction 137

accuracy in the case of financially distressed firms (81 versus 87 per

cent for the new model); however, it was less correct for non-distressed

firm’s prediction. Another modification was introduced by Lyandres

and Zhdanov (2013), who posed the question of whether the inclusion

of variables related to investment opportunities improved the predic-

tive power of three models (Altman’s Z-score model and Zmijewski’s

and Shumway’s models). They used three proxies for investment

opportunities (market-to-book, value-to-book, and R&D-to-assets).

The measures of investment opportunities were linked to the likelihood

of default. The inclusion of either of these measures improved the out-

of-sample forecasting ability of all three.

The verification of Altman’s model concentrated on its efficacy or

on how it compares with other accounting-based, market-based, or

hazard models. However, although the original Z-Score model was not

solely based on accounting data because the market value of equity

was utilized, we classify it here as accounting-based. Grice and Ingram

(2001) used a novel dataset of US firms and posed three questions

about the efficacy of Altman’s model, concluding that the prediction

accuracy of Altman’s model had declined over time and that the coeffi-

cients of the model had significantly changed, which means that the

relation between the financial ratios and the signs of financial distress

had changed over time. The model was sensitive to industry classifica-

tion (more efficient for manufacturing firms than for non-manufactur-

ing firms) but was not sensitive to the type of financial distress. Similar

conclusions were drawn by Grice and Dugan (2003) regarding Ohl-

son’s (1980) and Zmijewski’s (1984) models.

Hillegeist et al. (2004) compared Altman’s Z-score and Ohlson’s O-

score (with original and updated coefficients) with a model based on

Black-Scholes-Merton (BSM) option pricing (a so-called BSM-Prob

model). Hillegeist et al. used relative information content tests to com-

pare the out-of-sample performance of these various models and deter-

mined that BSM-Prob outperformed the alternative accounting-based

models. The conclusions were robust to various modifications of

accounting-based models, such as updated coefficients, industry effects,

and the separation of variables. Chava and Jarrow (2004) employed an

extended bankruptcy database of U.S.A. listed firms to test the superi-

ority of Shumway’s model (2001) over Altman’s (1968) and Zmijew-

ski’s (1984) models. The authors re-estimated the models over the

1962–1990 period and forecasted bankruptcies over the 1991–1999 per- iod. In the case of Shumway’s model, 74.4 per cent (in the first decile)

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

of the bankruptcies were correctly identified; with Altman’s model,

63.2 per cent; and with Zmijewski’s model, 43.2 per cent. Shumway’s

market-based model also outperformed accounting-based models in

terms of the ROC curve (0.91).

Reisz and Perlich (2007) developed a model incorporating barrier

options for bankruptcy prediction and compared its discriminatory

power with other market-based models and Altman’s Z-Score and

Z”-Score. The dataset covered nearly 6000 industrial firms over the

1988–2002 period. The authors documented the superiority of Alt- man’s Z-Score and Z”-Score models for short-term (up to 1 year)

bankruptcy prediction. For medium- and long-term bankruptcy predic-

tion, their barrier option model outperformed the other models.

Pindado et al. (2008) developed an ex ante model for the estimation

of financial distress likelihood (FDL) using a panel data methodology

and presented a financially (not legally) based definition of distress.

Their sample covered 1,583 U.S.A. companies and 2,250 companies

from other G-7 countries for the 1990–2002 period. They used a re- estimated Z-Score as a benchmark. The FDL model outperformed the

Z-Score model in terms of stability and classification power for differ-

ent countries and periods. In the case of the re-estimated Z-Score

model, only profitability and retained earnings maintained their signifi-

cance for different years and countries.

Wu et al. (2010) evaluated the performance of five models (Altman,

1968; Ohlson, 1980 Zmijewski, 1984; Shumway, 2001; Hillegeist et al.,

2004) using an up-to-date dataset for U.S.A. listed firms. Based on

these models, the authors built their own integrated model, that is, a

multi-period logit model with an expanded set of variables. The inte-

grated model, which combined accounting and market data, as well as

firms’ characteristics, outperformed the other models. Altman’s Z-

score performed poorly compared with the other four models. Shum-

way’s model performed best, Hillegeist et al.’s model performed ade-

quately, and Ohlson’s and Zmijewski’s models performed adequately,

although their performance deteriorated over time.

Jackson and Wood (2013) tested 13 different models of bankruptcy

prediction and assessed their efficacy using the ROC curve. They

selected three single-variable models, three accounting-based models

(including Altman’s Z-Score) in two versions (with updated coefficients

and a neural network approach), and four contingent claims models,

and the latter group outperformed the other models. The four best

models were contingent claims models based on European call and

Financial Distress Prediction 139

barrier options. Although the predictive performance improved with

the application of the neural network to accounting-based models, it

was still lower than with the market-based models.

Acosta-Gonz�alez and Fern�andez-Rodr�ıguez (2014) used genetic algorithms with the Schwarz information criterion (GASIC) for vari-

able selection combined with the logit model for bankruptcy predic-

tion. Altman’s Z-Score model was used as one of two benchmarks

for the authors’ model evaluation. For one-step-ahead forecasting,

Altman’s model was better at predicting failed firms, but the type II

error was high. For two- and three-steps-ahead forecasts, the perfor-

mance of the models was similar for failed firms, but for non-failed

firms, and the prediction accuracy of Altman’s model was worse. For

four-steps-ahead forecasts, the GASIC model outperformed the other

models for failed firms, but it performed comparably for non-failed

firms.

In general, for the 31 articles we reviewed, Altman’s Z-Score model

underperformed compared with market-based models, but evidence

indicated that it performed well for short-term distress prediction. The

question of whether market-based models perform better than account-

ing-based models has been raised many times (e.g., discussion in Das

et al., 2009; Bauer and Agarwal, 2014). Our purpose is not to con-

tribute to this strand of research but rather to focus on the account-

ing-based approach. In this study, we primarily analyze privately held

firms; by definition, there are no market data for these firms. In this

case, an accounting-based approach is the only solution applicable by

banks as lenders, or by investors holding debt securities of firms not

listed on the stock exchange. Thus far, most studies have concentrated

on the U.S.A. market; only a few of them have used data from other

countries, such as Japan, the U.K., Lithuania, or the G-7 countries.

Our analysis is a significant extension of the previous research.

4. Methodology and Research Hypotheses

The literature survey shows that the Z-Score model (publicly traded

firms), the Z’-Score model (private manufacturing firms), and the Z”-

Score model (private and publicly traded manufacturing and non-man-

ufacturing firms) have been adapted for different purposes. In this

study, we are interested first in assessing the performance of the origi-

nal Z”-Score model in classifying bankrupt and non-bankrupt firms in

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

However, we also validate the results in a set of non-European coun-

tries to generalize the results for circumstances outside Europe. Sec-

ond, we re-estimate the model using extensive international data and

then use the re-estimated Z”-Score model as a benchmark for assessing

the effects of different factors on the model’s performance in terms of

classification accuracy. We assess the effects of the following five fac-

tors on this performance: year of bankruptcy, size of firms, age of

firms, industry, and country of origin. We test a set of hypotheses

based on the effects of the model on performance on two different

levels. First, we test a set of hypotheses on a pooled set of all firms

and, second, on data from each country individually. Compared with

previous research, this study’s contribution is its focus on an inter-

national context, not simply model application or re-estimation of a

given country’s data. Because we focus on the performance of the

Z”-Score model when using a large body of international data, the

research hypotheses are of a technical nature and are given as

follows.

4.1. H1: Obsolescence of the Coefficients

The Z”-Score model was originally estimated using the same sample of

firms used to develop the Z-Score model. The bankruptcies in the esti-

mation data occurred during the 1946–1965 period. Thus, the oldest observations are from nearly seventy years ago, during the post-war

period. Altman (1983) recommended utilizing data as near to the pre-

sent as possible when developing a bankruptcy prediction model. It is

obvious that firms’ financial behavior and their business environment

have significantly changed since then. The importance of the financial

ratios, as reflected by the coefficients of the model, may differ from

their original importance. Therefore, we suggest, as the first hypothesis

(H1), that the re-estimation of the coefficients of the four original vari-

ables of the Z”-Score model will improve the classification perfor-

mance of the model in an international context. This hypothesis is

supported by the previous research (e.g., Grice and Ingram, 2001) and

by practice. H1 aims to support this evidence on the international level

and is expressed as follows:

H1: Re-estimating the coefficients of the Z”-Score model improves its clas-

sification accuracy.

Financial Distress Prediction 141

4.2. H2: Method of Estimation

The original Z”-Score model has been estimated using MDA. How-

ever, MDA is based on the ordinary least squares (OLS) method and

thus requires assumptions of multinormality, homoscedasticity, and

linearity, which are not often met in empirical financial ratio analysis.

We re-estimate the Z”-Score model using logistic regression analysis

(LRA) to assess the effect of the estimation method. LRA does not

require most of the restricting assumptions of MDA. In LRA, the

multivariate normality of the independent variables is not required,

nor are homoscedasticity and linearity. For the sake of OLS, MDA

can be more useful than LRA for small samples, such as the original

sample of 66 firms used in the estimation of the Z”-Score model. How-

ever, in a large sample, LRA may potentially perform better. In this

study, we use large samples, which is advantageous for LRA. Our sec-

ond hypothesis (H2) is that the classification performance of the re-

estimated Z”-Score model will improve when it is estimated using

LRA instead of MDA. The model re-estimated for the original vari-

ables using LRA and all pooled data arecalled the Z”-Score LR

model. The performance of this re-estimated model is used as the

benchmark for further analyses. Thus, the second hypothesis is as fol-

lows:

H2: The prediction accuracy of the logistic regression version of the

Z”-Score model is higher than that of the multiple discriminant analysis

version.

4.3. H3: Bankruptcy Year

The model based on the relationship between bankruptcy and financial

ratios is likely to be affected by the macroeconomic environment.

These effects may significantly decrease the classification accuracy of

the model. If the model is estimated using data from 1 year and will

be applied to data from another year, the validity of the model can be

questioned. In terms of economic growth, credit policy, and interest

rates, business cycles can affect the boundary between bankrupt and

non-bankrupt firms. The original Z”-Score model is estimated using

data from the 1946–1965 period, which includes several business cycles. Therefore, the model is not focused on any specific stage of a cycle

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

and does not explicitly take into account the bankruptcy year. Altman

(1983) suggested gathering data from firms for the most recent few

years when developing a prediction model. In this study, the bench-

mark Z”-Score LR model is estimated for a shorter period than in the

original estimation, although it covers several recent years for different

stages of the business cycle in different countries. The third hypothesis

(H3) is that the classification accuracy of the benchmark model can be

increased by explicitly taking into account the year of bankruptcy in

the model estimation. The third hypothesis is as follows:

H3: The model’s prediction accuracy is higher when the effect of the year

of bankruptcy is included.

4.4. H4: Size of the Firm

The boundary between bankrupt and non-bankrupt firms is different for

small and large firms, which decreases the performance of the model

estimation when data from one size category are applied to another size

category. For the bankrupt and non-bankrupt firms in the original data

for Z”-Score model estimation, asset sizes ranged between approxi-

mately 1 and 25 million U.S.A. dollars. The data did not include very

small or very large firms. Altman (1983) regarded the suitability of the

original Z-Score model (and, likewise, the Z”-Score model) for all firms

as debatable because of this omission. In the current study, the bench-

mark Z”-Score LR model is estimated for data from many size cate-

gories, from very small to very large firms. The fourth hypothesis (H4)

assumes that the classification performance of the uniform benchmark

LR model based on the original four financial variables of the Z”-Score

model is improved when the size category of the firm is explicitly taken

into account. Thus, we present the fourth hypothesis:

H4: The model’s prediction accuracy is higher when the effect of size is

included.

4.5. H5: Age of the Firm

International insolvency statistics generally show that bankruptcy risk

is a function of the age of the firm. Very young firms typically show

Financial Distress Prediction 143

very high risk. The original Z”-Score model does not explicitly take

age into account. However, Altman (1983) noted that the age of a firm

is implicitly considered in the Retained Earnings/Total Assets ratio

(X2), which was regarded as a new ratio in the bankruptcy prediction

context. A relatively young firm will probably show a low ratio

because it has not had time to build up cumulative profits. Thus, a

young firm is, to some degree, discriminated against in the model, and

its likelihood of being classified as bankrupt is relatively higher than

that of an older firm. The incidence of failure is much higher in the

early years of a firm. Although the age of the firm is implicitly taken

into account in X2, we expect that an explicit consideration of age will

improve the classification accuracy by controlling for the age factor.

The fifth hypothesis (H5) proposes that the performance of the uni-

form benchmark model based on the original four financial variables

of the Z”-Score model increases when the age of the firm is explicitly

taken into account. The fifth hypothesis is as follows:

H5: The model’s prediction accuracy is higher when the effect of firm age

is included.

4.6. H6: Industry of the Firm

The original Z’-Score model was estimated only for manufacturing

firms. Altman (1983) stated that it would be ideal to develop a bank-

ruptcy prediction model utilizing a homogenous group of bankrupt

firms. If we are interested in a particular industry grouping, we should

gather data from bankrupt and non-bankrupt firms in that grouping.

Previous studies show that financial distress analysis is influenced by

the industry effect (Smith and Liou, 2007). Firms in different industries

tend to report different levels of the same financial ratios, which may

have an effect on the boundary between bankrupt and non-bankrupt

firms. This industry effect may be present in the Z’-Score model, espe-

cially due to the Sales/Total Assets ratio (X5), which showed the least

significance on a univariate basis while making a very significant con-

tribution to the discriminant power of the multivariate model. Altman

(1983) recognized the potential industry effect due to the wide varia-

tion among industries in asset turnover and specified the Z”-Score

model without X5. However, the Z”-Score model was also estimated

using the original sample of manufacturing firms. In our analysis, the

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

uniform benchmark model based on the original four financial vari-

ables of the Z”-Score model is estimated for a statistical sample repre-

senting different industries. The sixth hypothesis (H6) assumes that an

explicit consideration of industry will improve the classification accu-

racy of this benchmark model. H6 can be expressed in the following

form:

H6: The model’s prediction accuracy is higher when the effect of industry

is included.

4.7. H7: Country of Origin

The original Z”-Score model has been estimated only for U.S.A. firms.

It can be expected that the international applicability of the model to

other countries is affected by country-specific differences. The eco-

nomic environment, legislation, culture, financial markets, and

accounting practices in a country may affect the financial behavior of

firms and the boundary between bankrupt and non-bankrupt firms.

These factors may weaken the classification performance of the model

for countries other than that for which the model was originally esti-

mated (Ooghe and Balcaen, 2007). The seventh hypothesis (H7)

assumes that explicitly taking the country of origin of a firm into

account will improve the classification accuracy of the benchmark

model. In our empirical study, the country effect is assessed by includ-

ing a variable for country risk. The seventh hypothesis is as follows:

H7: The model’s prediction accuracy is higher when the effect of country

risk is included.

5. Empirical Data and Statistical Methods

5.1. Sample of Firms

The principal data for this study were extracted from the ORBIS data-

bases of Bureau Van Dijk (BvD). ORBIS Europe is a commercial

database that, at the moment of sampling, contained administrative

information on more than 50 million European firms. However,

income statement and balance sheet information was available for

Financial Distress Prediction 145

approximately 8 million companies. More than 99 per cent of the com-

panies covered in this database are private companies from various

industries, justifying the use of the Z”-Score model instead of the orig-

inal Z-Score model. The Z”-Score model was originally made robust

across all industrial groupings and for both private and public entities

(Altman, 1983, 2014; Altman and Hotchkiss, 2006). Because we do not

want to limit the scope of the Z”-Score model in this study, we retain

both private and public firms from all industrial groupings.

The ORBIS formats have been derived from the world’s most com-

monly used formats for the presentation of business accounts (Ribeiro

et al., 2010). International comparability may be a problem when

administrative firm-level data are pooled across countries. Although

the definition of variables is usually less harmonized for administrative

data, this is less of a problem in the ORBIS database because of the

common international format of balance sheets. For example, although

some discrepancies in profit/loss statements may arise because of dif-

ferences in fiscal systems across countries, balance sheet variables lar-

gely adhere to international standards.

A number of factors influence the international applicability of

bankruptcy prediction models: accounting legislation and practice,

creditor rights and investor protection, judicial efficiency, corporate

governance, bankruptcy protection and insolvency management, and

firm risk-taking. These factors strongly differ between European and

non-European countries. Therefore, we aim to test the performance of

the Z”-Score model outside Europe. First, it is particularly important

to include the US because it is the country of origin for the Z”-Score

model and because it has the largest market capitalization in the

world. Second, a central motivation in developing the modified Z”-

Score model was to make it applicable to emerging market companies.

We include firms from China and Colombia, which represent two very

culturally and institutionally dissimilar emerging market countries. For

other non-European countries, sufficient bankruptcy data (more than

60 bankrupt firms, i.e., the limit we set for European countries) from

ORBIS World were not available. Thus, the results are also estimated

and tested for three non-European countries (the United States, China,

and Colombia) to gain a more global view of the Z”-Score model’s

performance. The samples of firms from these countries were extracted

from ORBIS World, which contains middle-sized (total assets over 1.5

million EUR) and larger firms from around the world.

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

Several requirements are set for the statistical sampling of the

empirical data. First, we require that the firm to be selected be an

industrial (non-financial) company. Second, its owners must have lim-

ited liability (so partnerships and sole proprietors are left out of the

study). Third, we set a minimum requirement for the size of the firm.

Because the financial ratios of very small firms are generally too unsta-

ble for a failure prediction model, these firms are excluded (see Balcaen

and Ooghe, 2006). We require that a firm’s total assets must have

exceeded 100 thousand EUR at least once in the available time series.

Fourth, we include in our estimation sample firms from all European

countries and three pre-selected non-European countries where the

number of failed firms is greater than 60. If the number of failed firms

for any European country is less than 60, the firms from this country

are included only in the test sample. For qualifying European coun-

tries, firms are randomly classified in the estimation and test samples

so that the number of firms is approximately equal in both samples.

Thus, our estimation data include firms from 28 European and three

non-European countries. Fifth, all failed firms that fulfill the above

requirements are included in our samples. However, if the number of

non-failed firms in a country is very high, a sample is randomly

selected from that country. Finally, the time span of fiscal years poten-

tially available for this study ranges from 2002 to 2010. Because the

most recent financial statements for failed firms in the database are

from a financial period within 2007 and 2010, earlier years are also

excluded for non-failed firms for comparability. All qualifying observa-

tions of non-failed firms from 2007 to 2010 are included in the data-

sets. We restrict the analyses of failed firms to the most recent

financial statements available before failure. The four independent

variables of the Z”-Score model were winsorized at 1 and 99 per cent

to minimize outliers.

Table 1 shows the resulting number of non-failed and failed firms in

the estimation data and test data by country. The estimation sample

includes data from 2,602,563 non-failed and 38,215 failed firms from

28 European and three non-European countries. The test sample is

slightly larger because it includes data from 31 European and three

non-European countries. For the country of origin of the Z”-Score

model, the United States, the estimation sample includes only 56 bank-

rupt firms. The available U.S.A. data consist only of listed (and

delisted) firms. From China, there are three sub-samples. Only 32 pub-

lic firms with special treatment (ST) 7 status are included in the estima-

Financial Distress Prediction 147

tion sample. 8 The Chinese datasets of predominantly private firms

(CN) and of public firms with delisted (DL) failure status are analyzed

separately only for the test data. 9 ST firms are listed firms suffering

from serious financial difficulties. Excluding the special U.S.A. data

and the two non-private Chinese datasets, 99.4 per cent of the observa-

tions in the data are private firms.

Table 1. Number of Observations by Country

Country

Estimation data Test data

Non-failed Failed Non-failed Failed

Austria (AT) 7,430 55 7,526 44 Belgium (BE) 179,979 2,994 179,818 2,944 Bosnia (BA) 29,391 35 29,139 32 Bulgaria (BG) 50,041 48 42,351 44 China (CN) 39,315 198 China, delisted dataset, DL 16,291 29 China, ST data 846 16 1,020 16 Colombia (CO) 8,366 139 6,982 125 Greece (GR) 51,763 28 Croatia (HR) 59,541 249 58,478 275 Czech Republic (CZ) 92,835 556 92,562 564 Denmark (DK) 167,934 1,334 168,538 1,398 Estonia (EE) 34,313 234 34,196 242 Finland (FI) 90,878 481 91,227 459 France (FR) 160,749 6,124 161,653 6,318 Germany (DE) 98,814 910 99,496 921 Hungary (HU) 19,421 303 20,155 313 Iceland (IS) 17,399 248 17,624 243 Ireland (IE) 6,665 121 6,406 139 Italy (IT) 167,113 8,101 166,258 8,124 Latvia (LV) 8,064 433 8,241 477 Lithuania (LT) 10,000 56 Netherlands (NL) 20,885 154 15,854 147 Norway (NO) 172,467 1,294 170,985 1,206 Poland (PL) 87,200 291 86,233 264 Portugal (PT) 180,114 3,390 178,646 3,422 Romania (RO) 161,992 97 164,259 93 Russian Federation (RU) 116,903 2,534 115,711 2,481 Serbia (RS) 100,100 68 Slovakia (SK) 7,856 120 7,788 124 Slovenia (SI) 14,419 59 14,081 41 Spain (ES) 156,746 3,036 158,122 2,991 Sweden (SE) 169,810 2,256 169,999 2,314 Ukraine (UA) 133,342 1,787 133,980 1,765 United Kingdom (GB) 171,493 760 170,930 716 U.K. (GB), liquidation dataset 342,423 4,990 United States (US) 9,557 56 9,929 53 Total 2,602,563 38,215 3,148,079 43,664

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

5.2. Status of Failed Firms

ORBIS has five classes for potentially active firms (active, default of

payment, receivership, dormant, and branch) and seven classes for

inactive firms that no longer carry out business activities (bankruptcy,

dissolved, dissolved-merger, dissolved-demerger, in liquidation, branch,

and no precision). Among these classes, only active is selected to repre-

sent non-distressed firms. In selecting the failed firms, we try to avoid

ambiguity by considering (with exceptions described below) a firm

failed if its status in ORBIS is stated as bankruptcy. However, because

of the small number of bankrupt firms in some countries, we also con-

sider receivership (active) firms as failed even if they are active. These

firms generally suffer from serious financial distress. However, firms in

liquidation are generally not included in the sample of failed firms.

Firms in liquidation may, depending on the country, contain firms that

have ceased activities due to reasons other than failure (mergers, dis-

continuing the operations of a daughter company or of a foreign

branch, etc.). Therefore, for most countries, we select only firms that

are coded as being bankrupt or under receivership. However, there are

a number of special cases where failed firms are coded under a differ-

ent status heading. These countries or samples are the following:

Country Status categories

Bulgaria In liquidation, Bankruptcy Denmark Inactive (no precision) Greece Active (receivership), In liquidation, Bankruptcy Ireland In liquidation, Active (receivership) Norway In liquidation Slovenia In liquidation Spain Active (receivership), In liquidation, Bankruptcy Ukraine In liquidation, Bankruptcy U.K., liquidation set In liquidation China, ST Active (special treatment) China, delisted, DL Active (delisted)

If no such category of failed firms could be identified, that country

was excluded from the study (for example, Switzerland). If a country

had only a very small number of failed firms, it was dropped from the

study (typically small countries, including Luxembourg, Liechtenstein,

and Montenegro). It should also be noted that the status classes (in-

cluding the bankruptcy category) are not completely homogenous

within European countries due to different legislations, although there

Financial Distress Prediction 149

are obvious similarities in insolvency acts (Philippe et al., 2002). China

is a special case because it includes samples with three different criteria

of failure (bankruptcy, special treatment, and delisted). Additionally,

for the United Kingdom, there are two different samples (liquidation

and receivership).

5.3. Statistical Methods

In this study, seven research hypotheses are drawn for statistical

testing. The statistical analysis begins with calculating the original

Z”-Score for the firms in the data, as in equation (3). The classification

performance of the original model is assessed by the AUC (Area Under

Curve) measure extracted from the ROC curve. AUC has a close con-

nection with the Accuracy Ratio (AR) because AR = 2 � AUC – 1. AR equals 0 for a random model, 1 for a perfect model, and 0.5 for a

model with an average classification performance. SAS software (SAS

Institute Inc., Cary, NC, USA) is used for all statistical analyses.

The first hypothesis (H1) assumes that the coefficients of the original

model are obsolete. H1 is tested by re-estimating the coefficients of the

Z”-Score model using the original statistical method (multiple discrimi-

nant analysis, or MDA). The problem is that the estimation sample

includes different numbers of failed and non-failed firms from 31 coun-

tries. In the original Z-Score” sample (1983), equal numbers of bank-

rupt and non-bankrupt firms were selected from the US Following the

characteristics of these data. Therefore, we weight the failed and non-

failed firms equally. In so doing, the non-proportional sampling from

different countries will not affect the re-estimated model. The number

of firms from different countries, however, varies significantly, leading

to greater weights for larger countries. To avoid this problem, the

observations are also weighted so that each country has an equal

weight in the analysis. Then, the coefficients of the Z”-Score model are

re-estimated using these weighted data, and the resulting AUC is com-

pared with that based on the original model.

The second hypothesis (H2) tests whether the classification perfor-

mance of the re-estimated Z”-Score model improves when it is re-esti-

mated using logistic regression analysis (LRA), which is based on less-

restrictive statistical assumptions than MDA. In this estimation, the

dependent variable Y = 0 for non-failed firms and Y = 1 for failed firms. LRA does not require that independent variables be multivariate

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

assumptions in MDA (Hosmer and Lemeshow, 1989). LRA creates a

(logit) score L for every firm. It is assumed that the independent vari-

ables are linearly related to L. This score or logit is used to determine

the conditional probability of failure as follows:

pðY ¼ 1jXÞ ¼ 1 1 þ e�L ¼

1 þ e�ðb0þb1X1þ…þb4X4Þ ð4Þ

where bi (i = 0, . . . 4) are the coefficients and Xi (i = 1,. . ., 4) are the four independent variables of the original Z”-Model.

10 The effect of

this method on classification performance is assessed by testing the sta-

tistical significance of the difference between AUCs for this LR model

and for the re-estimated MDA model. The resulting model is called

the Z”-Score LR model, and it is used as a benchmark for further sta-

tistical AUC comparisons because LR is applied as the principal

method in testing the remaining research hypotheses.

The third hypothesis (H3) is associated with the performance effect

of taking account of the bankruptcy year in the estimation. This

hypothesis is tested by estimating an LR model based on the following

logit:

L ¼ b0 þ X4

i¼1 biXi þ

j¼1 cjDj ð5Þ

where b0 is a constant, Xi (i = 1,. . ., 4) are the four independent vari- ables of the original Z”-Model, bi (i = 1,. . ., 4) are their coefficients, cj (j = 1,. . ., 3) are coefficients of the dummy variables, and D1 = 1 when year = 2008, 0 otherwise; D2 = 1 when year = 2009, 0 otherwise; D3 = 1 when year = 2010, 0 otherwise.

The dummy variables do not directly refer to the bankruptcy year,

which is not given in the database, but rather to the last available year

before bankruptcy. For failed firms, there is an approximately 1–2-year lead time to failure from this year. In this model, the year 2007 is the

base category. If the AUC of this extended LR model statistically sig-

nificantly exceeds the AUC of the Z”-Score LR model (benchmark),

the evidence supports hypothesis H3.

Research hypotheses H4–H7 are tested using the same approach as the third hypothesis above. However, for each hypothesis, appropriate

variables are used instead of the year dummies. Hypothesis H4

Financial Distress Prediction 151

addresses the performance effect of taking size into account, and it is

tested by adding two additional variables measuring firm size to the

LR model. In this LR model, size is measured by the natural loga-

rithm of total assets and its squared form. In this way, the effect of

logarithmic size can be reflected in a function following the second-

order parabola. Hypothesis H5 tests whether the classification perfor-

mance improves when the age of the firm is explicitly taken into

account. When testing this hypothesis, the 6–14 years category is used as the base category, and two dummy variables are incorporated in the

LR model (D1: less than 6 years, D2: 15 years or more). Hypothesis

H6 looks at whether classification performance is affected by the expli-

cit consideration of industry effects. The hypothesis is tested here using

dummy variables for seven industries (D1: restaurants and hotels; D2:

construction; D3: wholesale and retailing; D4: agriculture; D5: manufac-

turing; D6: energy and water production; D7: information technology),

with all other industries acting as the base category.

Hypothesis H7 tests whether the explicit consideration of the coun-

try of origin will improve classification performance. This hypothesis is

tested by using country risk measures instead of dummy variables for

countries. The country risk of each country is measured by Standard

& Poor’s Country Risk Rating per 6 months after the annual closing

of accounts. The rating is numerically recoded such that the best rat-

ing, AAA, equals 1, the second-best rating, AA+, equals 2, and so on. Finally, the lowest rating, D, equals 22. Thus, H7 is tested by estimat-

ing an LR model based on the four financial ratios of the original Z”-

Score model and a 22-step variable referring to country risk. The five

LR models with the original four financial ratios and the additional

variables specified in the hypotheses are estimated for all data. In addi-

tion, an LR model including all additional variables is estimated for

all data to assess the simultaneous effect of all variables. Finally, six of

the seven hypotheses are tested for the data of each country separately.

Hypothesis H7 is not included in this country-level testing because the

additional variable (country risk) is constant within the country.

6. Empirical Results

6.1. All Data: Coefficients of the Z”-Score Models

Table 2 presents descriptive statistics of the four independent variables

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

is significant, as shown by the standard deviation and the quartiles.

For X1 (WCTA), X2 (RETA), and X3 (EBITTA), the median and the

mean for non-failed firms are close to each other, indicating a symme-

try of distributions. However, this is not the case for failed firms. For

failed/distressed firms, the median exceeds the mean for these three

ratios, indicating negatively skewed distributions. For X4 (BVETD),

the means significantly exceed the median for both failed and non-

failed firms, indicating a positively skewed distribution. For each of

the four variables, both the mean and the median are higher for non-

failed firms, which is consistent with our expectations. The difference

between the means of non-failed and failed firms is larger in the origi-

nal U.S.A. data than in our all data for Retained Earnings/Total

Assets (RETA) and EBIT/Total Assets (EBITTA) but is approxi-

mately the same size for Working Capital/Total Assets (WCTA) and

Book Value of Equity/Total Liabilities (BVETD) (Altman, 1983).

These characteristics of the data may indicate lower classification accu-

racy than in the original sample.

Table 3 presents the coefficients of the different models estimated

for all data. All LRA estimates (Model 2 to Model 9) are statistically

significant at 0.0001 due to their contributions and to the large sample

size. The first column presents the coefficients of the original Z”-Score

model. The “Model 1” column shows the coefficients when they are

re-estimated by the same statistical method, specifically MDA. The

coefficients here are negative because the models are estimated using

Table 2. Descriptive Statistics (All Data)

WCTA RETA EBITTA BVETD

Statistic Non- failed Failed

Non- failed Failed

Median 0.152 �0.059 0.189 �0.024 0.041 �0.020 0.451 0.025 Mean 0.147 �0.213 0.188 �0.317 0.055 �0.108 3.594 0.703 Standard deviation

0.442 0.604 0.509 0.767 0.227 0.296 11.499 5.712

Upper quartile

0.420 0.142 0.476 0.087 0.131 0.042 1.548 0.215

Lower quartile

�0.040 �0.440 0.011 �0.450 �0.008 �0.208 0.100 �0.240

Maximum 0.956 0.956 0.958 0.958 0.785 0.785 68.606 68.606 Minimum �1.637 �1.637 �2.453 �2.453 �0.828 �0.828 �0.649 �0.649

WCTA, Working Capital/Total Assets; RETA, Retained Earnings/Total Assets; EBITTA, EBIT/Total Assets; BVETD, Book Value of Equity/Total Liabilities.

Financial Distress Prediction 153

T a b le

3 . T h e C o effi

ci en ts

o f th e D iff er en t M o d el s E st im

a te d fo r a ll D a ta

C o effi

ci en ts

fo r d iff er en t st a ti st ic a l m o d el s

V a ri a b le

Z ” -S co re

M o d el

1 M o d el

2 M o d el

3 M o d el

4 M o d el

5 M o d el

6 M o d el

7 M o d el

C o n st a n t

3 .2 5

�0 .0 4 2

0 .0 3 5

0 .2 0 7

�1 3 .4 6 6

0 .0 0 7

0 .0 4 8

0 .0 4 9

�1 3 .3 0 2

W C T A

6 .5 6

�0 .5 6 1

�0 .4 9 5

�0 .4 8 3

�0 .4 4 1

�0 .4 8 7

�0 .5 4 0

�0 .4 9 6

�0 .4 5 9

R E T A

3 .2 6

�0 .7 2 4

�0 .8 6 2

�0 .8 9 1

�1 .1 4 6

�0 .8 4 6

�0 .8 5 9

�0 .8 6 3

�1 .1 6 0

E B IT

T A

6 .7 2

�1 .7 9 1

�1 .7 2 1

�1 .7 9 0

�1 .6 1 9

�1 .7 5 7

�1 .6 9 5

�1 .7 1 7

�1 .6 8 2

B V E T D

1 .0 5

�0 .0 2 1

�0 .0 1 7

�0 .0 1 6

�0 .0 1 2

�0 .0 1 7

�0 .0 1 6

�0 .0 1 7

�0 .0 1 3

Y ea r d u m m ie s

Y ea r 2 0 0 8

�0 .0 5 5

�0 .0 3 4

Y ea r 2 0 0 9

�0 .1 7 9

�0 .1 5 0

Y ea r 2 0 1 0

�0 .6 6 6

�0 .6 3 1

S iz e v a ri a b le s

T o ta l a ss et s (l o g )

1 .8 3 0

1 .8 3 7

T o ta l a ss et s sq u a re d (l o g )

�0 .0 6 1

A g e d u m m ie s

L es s th a n 6 y ea rs

0 .1 3 5

0 .1 8 6

1 5 y ea rs

o r m o re

�0 .0 5 8

�0 .0 9 9

C o u n tr y ri sk

S P co u n tr y ra ti n g ra n k

�0 .0 0 3

�0 .0 1 4

In d u st ry

d u m m ie s

R es ta u ra n ts

a n d h o te ls

�0 .6 5 3

�0 .6 2 8

C o n st ru ct io n

0 .4 4 5

0 .3 6 5

W h o le sa le

a n d re ta il in g

�0 .1 1 2

�0 .1 5 7

A g ri cu lt u re

�0 .1 8 0

�0 .1 7 6

M a n u fa ct u ri n g

0 .1 3 9

0 .0 9 5

E n er g y a n d w a te r p ro d u ct io n

�0 .4 5 4

�0 .4 7 2

In fo rm

a ti o n te ch n o lo g y

�0 .9 1 3

�0 .9 1 5

S ig n ifi ca n ce : C o effi

ci en ts

a re

a ll st a ti st ic a ll y si g n ifi ca n t a t 0 .0 0 0 1 .

M o d el s:

Z ” -S co re

= O ri g in a l A lt m a n (1 9 8 3 ) Z ” -S co re

M o d el

co effi

ci en ts ; M o d el

1 =

T h e M D A

m o d el ; M o d el

2 =

T h e L R

m o d el ; M o d el

3 =

T h e L R

m o d el

es ti m a te d fo r a ll d a ta

w it h y ea r d u m m ie s;

M o d el

4 = T h e L R

m o d el

es ti m a te d fo r a ll d a ta

w it h si ze

v a ri a b le s;

M o d el

5 =

T h e L R

m o d el

es ti m a te d fo r a ll d a ta

w it h a g e ca te g o ry

d u m m ie s;

M o d el

6 =

T h e L R

m o d el

es ti m a te d fo r a ll d a ta

w it h in d u st ry

d u m m ie s;

M o d el

7 =

T h e

L R

m o d el

es ti m a te d fo r a ll d a ta

w it h co u n tr y ri sk

ra n k in g s; M o d el

8 =

T h e L R

m o d el

es ti m a te d fo r a ll d a ta

w it h a ll v a ri a b le s.

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

Y = 1 for the failed firms. In our all data, EBITTA has a significantly higher relative weight than in the original U.S.A. data, while the

weights of WCTA and BVETD have proportionally decreased. The re-

estimated coefficient of BVETD is very small, indicating a minor effect

on the logit. The “Model 2” column presents the coefficients for the

Z”-Score LR model. These coefficients are directly comparable with

those of the MDA model, as expected for this exceptionally large sam-

ple. For each model, the coefficient of BVETD is very close to zero.

The differences in the coefficients of the original four variables among

the eight LR models (Models 1–8) are small, indicating that the origi- nal four variables and the additional variables are quite independent

of each other.

Table 3 also shows the coefficients of the additional variables in the

LR models. The negative coefficients of the dummy (year) variables of

Model 3 indicate that after 2007 (the base category), this risk of failure

significantly decreased year by year. The base year of 2007 indicates

that a failure emerged during 2008–2009 as a result of the global finan- cial crisis. The crisis played a significant role in the failure of key busi-

nesses and caused a downturn in economic activity, leading to the

2008–2012 recession. The effects were especially felt in Europe. The coefficients of Model 4 for the size variables show that the contribu-

tion of size to the logit (risk measure) reaches its maximum value when

logarithmic total assets are 15 or when total assets are approximately

3.3 million EUR. Model 5 confirms the riskiness of young firms

because the risk of failure is very high for newly founded firms (less

than 6 years old), as shown by the coefficient of the first dummy vari-

able. The coefficients of the industry dummies in Model 6 show that

construction is an exceptionally risky industry, followed by manufac-

turing. For Model 7, the coefficient of the country risk dummy is sta-

tistically significant (because of the large sample) but negative and very

close to zero. Finally, the coefficients of all variables in Model 8 are

directly comparable to those in Models 3–7.

6.2. All Data: Performance of the Z”-Score Models

Table 4 shows the AUCs in the test data for the different “all data”

models by country. Model 1 refers to the original Z”-Score model.

The classification performance of the score at the level of all countries

is fair because AUC = 0.743 refers to AR = 0.486, which is approxi- mately average accuracy (0.5). However, the score gives relatively good

Financial Distress Prediction 155

T a b le

4 . T es t D a ta

A U C s fo r D iff er en t C o u n tr ie s,

B a se d o n A ll D a ta

M o d el

V er si o n s. C o m p a ri so n s a re

w it h th e A U C

o f th e L R

M o d el

E st im

a te d fo r A ll D a ta

( B en ch m a rk ) . T es t d a ta

A U C

fo r d iff er en t m o d el s

C o u n tr y

B en ch m a rk

M o d el

1 M o d el

2 M o d el

3 M o d el

4 M o d el

5 M o d el

6 M o d el

7 M o d el

A ll d a ta

0 .7 4 8

0 .7 4 3 —

0 .7 4 5 —

0 .7 5 2 + + + +

0 .7 6 0 + + + +

0 .7 4 8 + + +

0 .7 5 1 + + + +

0 .7 4 9 + + + +

0 .7 7 1 + + + +

C o u n tr ie s w it h es ti m a ti o n d a ta

A u st ri a (A

T )

0 .8 0 0

0 .7 8 8

0 .7 9 7

0 .8 0 5

0 .8 1 8 +

0 .8 1 4 +

0 .7 8 2

0 .8 0 0

0 .8 1 9

B el g iu m

(B E )

0 .7 7 2

0 .7 6 0

0 .7 7 0

0 .7 7 7

0 .7 4 7

0 .7 7 7

0 .7 6 5

0 .7 7 2

0 .7 5 8

B o sn ia

(B A )

0 .8 6 2

0 .8 0 5

0 .8 5 7

0 .7 7 6

0 .8 6 3

0 .8 4 7

0 .8 5 5

0 .8 6 2

0 .7 8 4

B u lg a ri a (B G )

0 .6 8 4

0 .6 3 0

0 .6 8 0

0 .6 5 4

0 .6 9 1

0 .6 8 0

0 .6 5 9

0 .6 8 4

0 .6 3 2

C h in a , S T

co m p a n ie s

0 .9 8 5

0 .9 1 1 —

0 .9 8 3

0 .9 5 8 –

0 .9 7 7

0 .9 7 8

0 .9 8 7

0 .9 8 5

0 .9 6 8

C o lo m b ia

(C O )

0 .7 2 6

0 .7 2 4

0 .7 2 7

0 .7 1 5

0 .7 5 8

0 .7 2 8

0 .7 2 6

0 .7 5 7

C ro a ti a (H

R )

0 .8 4 4

0 .8 1 2

0 .8 3 9

0 .8 0 3

0 .8 3 7

0 .8 3 5

0 .8 3 2

0 .8 4 4

0 .8 0 1

C ze ch

R ep u b li c (C

Z )

0 .8 1 1

0 .8 1 3

0 .8 1 1

0 .8 1 9

0 .8 2 8

0 .8 0 7

0 .8 2 0

0 .8 1 1

0 .8 3 8

D en m a rk

(D K )

0 .8 0 3

0 .7 9 8

0 .8 0 0

0 .7 8 1

0 .8 1 3

0 .8 0 2

0 .8 0 1

0 .8 0 3

0 .7 9 6

E st o n ia

(E E )

0 .8 2 3

0 .8 2 7

0 .8 2 3

0 .8 4 7

0 .8 6 6

0 .8 2 6

0 .8 3 3

0 .8 2 3

0 .8 9 0

F in la n d (F I)

0 .8 6 7

0 .8 6 4

0 .8 6 6

0 .8 3 5

0 .8 6 2

0 .8 7 0

0 .8 7 8

0 .8 6 7

0 .8 5 3

F ra n ce

(F R )

0 .7 3 9

0 .7 2 3

0 .7 3 5

0 .7 4 9

0 .7 7 1

0 .7 4 1

0 .7 6 2

0 .7 3 9

0 .7 9 9

G er m a n y (D

E )

0 .6 7 3

0 .6 5 8

0 .6 6 6

0 .6 9 5

0 .6 5 6

0 .6 8 4

0 .6 7 7

0 .6 7 3

0 .6 8 8

H u n g a ry

(H U )

0 .7 4 2

0 .7 4 6

0 .7 4 0

0 .6 6 0

0 .7 3 8

0 .7 5 5

0 .7 3 5

0 .7 4 2

0 .6 9 6

Ic el a n d (I S )

0 .6 6 4

0 .6 7 4

0 .6 6 6

0 .6 9 4

0 .6 7 8

0 .6 7 3

0 .6 7 2

0 .6 6 4

0 .7 1 6

Ir el a n d (I E )

0 .6 7 9

0 .6 7 2

0 .6 7 6

0 .7 0 8

0 .6 7 7

0 .6 8 1

0 .6 8 8

0 .6 7 9

0 .7 1 2

It a ly

(I T )

0 .8 0 6

0 .7 9 9

0 .8 0 4

0 .8 3 3

0 .8 3 5

0 .8 0 6

0 .7 9 9

0 .8 0 6

0 .8 4 9

L a tv ia

(L V )

0 .6 7 8

0 .6 9 1

0 .6 7 8

0 .7 0 4

0 .6 7 6

0 .6 8 6

0 .6 9 8

0 .6 7 8

0 .7 2 4

N et h er la n d s (N

L )

0 .7 5 2

0 .7 5 4

0 .7 5 0

0 .7 7 5

0 .7 6 9

0 .7 5 4

0 .7 4 6

0 .7 5 2

0 .7 8 7

N o rw

a y (N

O )

0 .7 1 6

0 .6 9 4

0 .7 1 3

0 .6 5 8

0 .6 8 2

0 .7 2 0

0 .7 1 5

0 .7 1 6

0 .6 4 5

P o la n d (P L )

0 .9 0 3

0 .9 0 4

0 .9 0 8

0 .9 0 2

0 .9 0 3

0 .8 9 9

0 .9 0 3

0 .9 0 4

P o rt u g a l (P T )

0 .7 4 1

0 .7 2 4

0 .7 3 6

0 .7 4 9

0 .7 7 3

0 .7 3 8

0 .7 5 5

0 .7 4 1

0 .7 8 5

R o m a n ia

(R O )

0 .7 5 8

0 .7 4 0

0 .7 5 4

0 .7 0 9

0 .7 4 9

0 .7 5 5

0 .7 4 8

0 .7 5 8

0 .7 0 3

R u ss ia n F ed er a ti o n

(R U )

0 .8 1 1

0 .8 0 2

0 .8 1 2

0 .8 4 3

0 .7 9 9

0 .8 0 7

0 .7 9 9

0 .8 1 1

0 .8 1 4

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

T a b le

4 ( C o n ti n u ed )

T es t d a ta

A U C

fo r d iff er en t m o d el s

C o u n tr y

B en ch m a rk

M o d el

1 M o d el

2 M o d el

3 M o d el

4 M o d el

5 M o d el

6 M o d el

7 M o d el

S lo v a k ia

(S K )

0 .7 7 7

0 .7 7 4

0 .7 7 6

0 .7 8 0

0 .8 1 1

0 .7 6 9

0 .7 8 6

0 .7 7 7

0 .8 0 8

S lo v en ia

(S I)

0 .7 3 7

0 .7 2 5

0 .7 3 3

0 .6 7 4

0 .7 4 0

0 .7 4 7

0 .7 1 8

0 .7 3 7

0 .7 2 1

S p a in

(E S )

0 .7 3 4

0 .7 1 3

0 .7 3 2

0 .7 0 7

0 .7 9 3

0 .7 2 8

0 .7 5 3

0 .7 3 4

0 .7 7 4

S w ed en

(S E )

0 .8 1 3

0 .8 0 1

0 .8 0 9

0 .7 9 9

0 .7 8 4

0 .8 1 7

0 .8 2 3

0 .8 1 4

0 .8 0 0

U k ra in e (U

A )

0 .7 0 8

0 .7 1 4

0 .7 1 0

0 .7 1 5

0 .7 2 1

0 .7 0 8

0 .7 0 2

0 .7 0 8

0 .7 2 2

U n it ed

K in g d o m

(G B )

0 .6 9 9

0 .7 1 9

0 .6 9 9

0 .6 8 6

0 .7 3 6

0 .6 9 5

0 .7 0 6

0 .6 9 9

0 .7 2 9

U n it ed

S ta te s (U

S )

0 .7 1 0

0 .7 0 1

0 .7 1 1

0 .7 0 9

0 .7 2 2

0 .7 0 5

0 .7 1 6

0 .7 1 0

0 .7 2 3

C o u n tr ie s o n ly

in te st

d a ta

E u ro p ea n

G re ec e (G

R )

0 .7 1 5

0 .6 7 0

0 .7 0 2

0 .7 2 5

0 .7 1 1

0 .7 1 3

0 .7 1 7

0 .7 1 5

0 .7 1 8

L it h u a n ia

(L T )

0 .7 6 7

0 .7 8 2

0 .7 6 7

0 .7 6 4

0 .7 6 8

0 .7 6 9

0 .7 7 5

0 .7 6 7

0 .7 7 8

S er b ia

(R S )

0 .7 3 6

0 .7 1 3

0 .7 3 0

0 .6 0 3 –

0 .8 2 6

0 .7 2 0

0 .7 5 3

0 .7 3 6

0 .7 3 8

U .K

., li q u id a ti o n

d a ta se t

0 .6 0 6

0 .6 2 1

0 .6 0 3

0 .6 2 0

0 .6 1 8

0 .6 1 0

0 .6 0 7

0 .6 3 5

N o n -E u ro p ea n

C h in a (C

N )

0 .5 5 8

0 .5 7 0

0 .5 5 7

0 .5 7 2

0 .5 4 3

0 .5 5 4

0 .5 6 7

0 .5 5 8

0 .5 5 6

C h in a , d el is te d

fi rm

s, D L

0 .5 2 9

0 .5 4 6

0 .5 1 9

0 .5 6 3

0 .7 4 0 + + + +

0 .5 4 2

0 .5 2 0

0 .5 2 9

0 .7 0 7 + + +

S ig n ifi ca n ce : A U C

b et te r th a n b en ch m a rk : 0 .0 0 0 1 = + + + + , 0 .0 0 1 = + + + , 0 .0 1 = + + , 0 .1

= +

A U C

w o rs e th a n b en ch m a rk : 0 .0 0 0 1 = —

, 0 .0 0 1 = — , 0 .0 1 = – , 0 .1

= –

M o d el s:

B en ch m a rk

= T h e L R

m o d el

es ti m a te d

fo r a ll

d a ta

w it h

Z ” -m

o d el

(1 9 8 3 ) v a ri a b le s;

M o d el

1 =

T h e o ri g in a l A lt m a n

(1 9 8 3 ) Z ” -S co re

M o d el ; M o d el

2 =

T h e M D A

m o d el

es ti m a te d fo r a ll d a ta ; M o d el

3 =

T h e L R

m o d el

es ti m a te d fo r a ll d a ta

w it h y ea r d u m m ie s;

M o d el

4 =

T h e

L R

m o d el

es ti m a te d fo r a ll d a ta

w it h si ze

v a ri a b le s;

M o d el

5 =

T h e L R

m o d el

es ti m a te d fo r a ll d a ta

w it h a g e ca te g o ry

d u m m ie s;

M o d el

6 =

T h e

L R

m o d el

es ti m a te d fo r a ll d a ta

w it h in d u st ry

d u m m ie s;

M o d el

7 =

T h e L R

m o d el

es ti m a te d fo r a ll d a ta

w it h co u n tr y ri sk

ra n k in g s;

M o d el

8 =

T h e L R

m o d el

es ti m a te d fo r a ll d a ta

w it h a ll v a ri a b le s.

Financial Distress Prediction 157

Table 5. Differences of Medians Between Non-Failed and Failed Groups

Country WCTA RETA EBITTA BVETD AUC of Z”-Score

Austria (AT) 0.448 0.405 0.126 0.487 0.788 Belgium (BE) 0.223 0.264 0.077 0.431 0.760 Bosnia (BA) 0.106 0.249 0.088 0.580 0.805 Bulgaria (BG) 0.114 0.291 0.094 0.436 0.630 China (CN) 0.068 0.032 0.016 0.280 0.570 China, delisted data, DL 0.089 �0.061 �0.032 0.083 0.546 China, ST data 0.298 0.293 0.139 0.468 0.911 Colombia (CO) 0.226 0.235 0.099 0.705 0.724 Croatia (HR) 0.275 0.335 0.044 0.274 0.812 Czech Republic (CZ) 0.404 0.397 0.069 0.667 0.813 Denmark (DK) 0.267 0.356 0.046 0.733 0.798 Estonia (EE) 0.331 0.388 0.113 1.033 0.827 Finland (FI) 0.402 0.606 0.207 0.907 0.864 France (FR) 0.140 0.213 0.065 0.424 0.723 Germany (DE) 0.131 0.136 0.032 0.262 0.658 Greece (GR) 0.171 0.262 0.049 0.349 0.670 Hungary (HU) 0.175 0.224 0.052 0.587 0.746 Iceland (IS) 0.246 0.261 0.051 0.337 0.674 Ireland (IE) 0.181 0.266 0.039 0.560 0.672 Italy (IT) 0.277 0.164 0.073 0.207 0.799 Latvia (LV) 0.117 0.120 0.042 0.254 0.691 Lithuania (LT) 0.246 0.218 0.051 0.569 0.782 Netherlands (NL) 0.204 0.253 0.077 0.432 0.754 Norway (NO) 0.157 0.219 0.115 0.329 0.694 Poland (PL) 1.340 0.920 0.124 1.351 0.904 Portugal (PT) 0.215 0.200 0.052 0.318 0.724 Romania (RO) 0.222 0.271 0.056 0.298 0.740 Russian Federation (RU) 0.350 0.242 0.069 0.245 0.802 Serbia (RS) 0.120 0.148 0.045 0.389 0.713 Slovakia (SK) 0.256 0.184 0.061 0.431 0.774 Slovenia (SI) 0.111 0.172 0.035 0.326 0.725 Spain (ES) 0.143 0.143 0.076 0.285 0.713 Sweden (SE) 0.255 0.346 0.099 0.663 0.801 Ukraine (UA) 0.204 0.200 0.031 0.449 0.714 United Kingdom (GB) 0.211 0.245 0.033 0.472 0.719 U.K., liquidation dataset 0.156 0.234 0.031 0.447 0.621 United States (US) 0.195 0.378 0.248 0.722 0.701 Average of column items 0.245 0.265 0.073 0.481 0.740 Correlation with Z”-Score AUC

0.611 0.681 0.516 0.574 1.000

WCTA, Working Capital/Total Assets; RETA, Retained Earnings/Total Assets; EBITTA, EBIT/Total Assets; BVETD, Book Value of Equity/Total Liabilities; SALTA, Sales/Total Assets; AUC, Area under the ROC curve; Z”-Score, Altman (1983) Z”-Score in the test data.

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

results (AUC > 0.8) for China (ST firms), Poland, Finland, Estonia, the Czech Republic, Croatia, Bosnia, Russia, and Sweden. Its perfor-

mance is quite low (AUC < 0.7) for Norway, Latvia, Iceland, Ireland, and Germany. The lower part of the table shows the AUCs for the

countries included only in the test data. The performance of the score

is very low in the Chinese CN (primarily private) and DL (delisted)

samples and for liquidation firms in the United Kingdom.

The Appendix shows the medians of the four ratios (X1–X4) by sta- tus and country. Table 5 presents the differences of these medians

between non-failed and failed firms by country. This table also pre-

sents the AUC of the Z”-Score and its correlation with the difference

of medians, which is high for each financial ratio, showing that the

effects of the ratios on AUC are well balanced. For China’s ST firms,

the differences are not exceptionally large, except for EBITTA, which

implies, with the exceptionally high AUC, that ST firms systematically

differ from non-ST firms, although the differences are not extremely

large. The differences between the medians are very large in Poland for

each ratio, justifying the high AUC, and in Finland and the Czech

Republic, where the difference in EBITTA is average. In Germany,

Latvia, China (CN and delisted), and the U.K. (liquidation), the differ-

ences in all four ratios are below average, which is obviously associ-

ated with a low AUC. In the sample of Chinese delisted firms, the

differences in RETA and EBITTA are even negative. In Iceland and

Ireland, the differences only in EBITTA are exceptionally small.

Model 2 in Table 4 is the re-estimated Z”-Score model, where the

coefficients are estimated by MDA for all data. Its AUC (0.745) is

only slightly higher than that for the original model (0.743), supporting

H1 only very weakly, if at all. The classification accuracy in terms of

AR (0.490) is at approximately the average level. The re-estimation of

the coefficients has led to improved classification accuracy in a number

of countries, especially Bosnia, China (ST), Norway, and Greece.

However, it has impaired the classification accuracy in the United

Kingdom and China (delisted). The “Benchmark” column reports the

results for the benchmark model (Z”-Score LR model), showing the

effect of the estimation method. For the benchmark model, the AUC

in all data is 0.748, which is higher than that for Models 1 and 2. The

differences among AUCs are very small, only weakly supporting H2

(estimation method). The LR model (benchmark) and the MDA model

(Model 2) give nearly identical AUCs for each country. This result

Financial Distress Prediction 159

rable. The similar results for the models may also indicate that the

independent variables conform to multinormality. Nevertheless, these

similarities support what most researchers in the field of default classi-

fication models have concluded: that the accuracy levels of MDA and

logistic regression models are extremely similar.

Model 3 (LR model with year dummies) leads in all test data to a

higher AUC (0.752) than the benchmark model, supporting H3 (bank-

ruptcy year effect). However, the AUC effects are not positive for all

countries. The effects are positive, for example, for Russia, Estonia,

Germany, Ireland, and Latvia, but these effects are not statistically

significant. There are statistically significant negative effects for China

(ST) and Serbia. There are notable negative effects, especially for

Bosnia, Croatia, Hungary, Norway, and Slovenia. These diverse results

are due to the exceptional annual distributions of failed firms in these

countries. For the countries with negative effects, the percent of failed

firms from 2010 (D3 = 1) exceeds 50 per cent, while for the countries with positive effects, it is only a few percent. For each group, non-

failed firms are quite equally distributed over years. When the coeffi-

cient of D3 is very low (�0.666), it strongly decreases the risk estimates of most failed firms in the former countries but of only a few failed

firms in the latter countries, which leads to the observed effects. H3 is

supported by evidence at the level of the whole sample. However, in

some country samples (at the individual country level), H3 is not

supported.

Model 4 (LR model with size variables) performs better than the

benchmark model, which lends support to H4 (size effect) and leads to

AUC = 0.760, indicating AR = 0.520, and to significant improvements in the AUC for, e.g., China (delisted) and Austria. For China

(delisted), the increase in AUC is extremely strong, and the AUC also

improves for, e.g., Estonia, Italy, Slovakia, Spain, and the U.K. Model

4 also led to lower AUCs in a few countries, but this decrease is not

significant. Model 4 is based on the four original variables and the size

effect following a second-order parabola. This kind of size effect is

very small for micro firms but increases when approaching its maxi-

mum value in middle-sized firms. The countries with a positive change

in AUC typically have data in which the percent of failed micro firms

(in all failed firms) is relatively low (40–60 per cent), while there are many middle-sized failed firms. Because the size effect is strongest for

middle-sized firms, the AUC increases. In contrast, the countries with

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

a negative change in AUC have data in which the percentage of failed

micro firms is exceptionally high (70–80 per cent). Model 5 (LR model with age category dummies) gives, for all test

data, nearly the same AUC as the benchmark model (AUC = 0.748). However, the difference is positive and statistically significant, giving

at least marginal support to H5 (age effect). For nearly all countries,

the effect of age on AUC is small. For Austria, however, this effect is

positive and significant. For Austria, the percentage of young firms

(less than 6 years) among non-failed firms is only approximately 10

per cent, whereas this percentage among failed firms is more than 20

per cent. Because Model 5 includes a strong positive age risk effect for

young firms (D2), it increases the risk of many failed firms but of only

a few non-failed firms, which obviously leads to an improvement in

AUC.

Model 6 (LR model with industry dummies) outperforms the bench-

mark model in AUC, supporting H6 (industry effect). It gives an

AUC = 0.751, indicating an AR = 0.502. However, its AUC is notably higher than the benchmark AUC in only a few countries, such as

France, Latvia, Portugal, and Spain. Model 6 has a negative effect on

failure risk for, e.g., restaurants, hotels and the information technology

industry, but it has a positive effect on the construction and manufac-

turing industries. For countries with a positive effect on AUC, the per-

cent of non-failed firms in restaurants, hotels and information

technology is high, while that of failed firms is low. For risky indus-

tries (construction and manufacturing), these distributions are reversed.

Thus, Model 6 gives a positive (negative) risk effect for many failed

(non-failed) firms and a negative (positive) risk effect for only a few

failed (non-failed) firms. Therefore, the AUC increases. This situation

is reversed for the countries with a negative effect on AUC (Austria

and Slovenia). The samples of these countries include a high percent-

age of non-failed firms in the manufacturing industry, leading to a

decrease in AUC.

Model 7 (LR model with the country risk measure) leads to a mar-

ginally higher classification performance (AUC = 0.749) than the benchmark model. This result gives only very weak support for H7

(country of origin effect). However, for each country, the resultant

AUC is nearly identical to that given by the benchmark model. This

result was expected due to the negligible coefficient (�0.003) of the country risk measure (SP country rating rank) in Model 7. This result

Financial Distress Prediction 161

implies that country risk has no effect on the boundary between bank-

rupt and non-bankrupt firms.

Model 8 (LR model with all variables) includes the four financial

ratios and all additional variables and leads to a sizeable increase in

AUC (AUC = 0.771) compared to the benchmark AUC in all test data. However, the effect on the AUCs largely varies and is either neg-

ative or positive in different countries. The positive effect is large in

several countries, such as Estonia, France, Iceland, Italy, Latvia, and

China (delisted). However, it also has a negative effect on AUCs in

several countries, such as Bosnia, Hungary, and Norway. These results

show that the inclusion of additional variables in the original model

will usually increase the AUC, but not in every country.

The “all data” benchmark also performs fairly well for the United

States and Colombian samples (with the U.S.A. firms, unlike the

majority of other firms in this study, being listed or delisted compa-

nies). The poor performance of the predominantly private (CN) and

delisted (DL) Chinese firm samples is associated with very small differ-

ences between the medians of the non-failed and failed groups, as

shown in Table 5. It is clear that the “delisted” status is not compara-

ble with “bankruptcy” status. When the status is defined as “ST”, the

predictability of Chinese listed firms is very high. Prior studies based

on Chinese ST firms have also demonstrated good predictability

(Wang and Campbell, 2010; Zhang et al., 2010). Nevertheless, this

puzzle calls for additional research and modeling work regarding

unlisted and delisted Chinese firms.

6.3. Country-Level Data: Performance of the Z”-Score Models

The heterogeneity of the firms and their distributions in “all data”

make it difficult for a uniform all data model to increase AUCs across

all countries. Table 6 presents the test data AUCs for the different

models estimated for each country separately (country-level models).

In this table, the “all data” Z”-Score LR model acts as the benchmark.

When the models are estimated from country data, this benchmark is

clearly outperformed by the resulting MDA (Model 1) and LR (Model

2) models in only a few countries (Bulgaria, France, Latvia, Spain,

and Sweden). However, these results give only weak support for H1 at

the country level because the effects are not significant. In addition,

the benchmark leads to higher AUCs than in Models 1 and 2, at least

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

T a b le

6 . T es t D a ta

A U C s fo r D iff er en t E st im

a te d C o u n tr y M o d el s. C o m p a ri so n s a re

W it h th e A U C

o f th e L R

M o d el

E st im

a te d fo r A ll D a ta

( B en ch m a rk )

T es t d a ta

A U C

fo r d iff er en t m o d el s

C o u n tr y

B en ch m a rk

M o d el

1 M o d el

2 M o d el

3 M o d el

4 M o d el

5 M o d el

6 M o d el

A u st ri a (A

T )

0 .8 0 0

0 .7 8 2

0 .7 7 0 –

0 .7 7 4

0 .8 2 9

0 .7 8 7

0 .7 0 7 –

0 .7 6 4

B el g iu m

(B E )

0 .7 7 2

0 .7 7 9

0 .7 8 0

0 .8 2 6

0 .7 7 6

0 .7 8 3

0 .7 8 1

0 .8 3 4

B o sn ia

(B A )

0 .8 6 2

0 .8 5 0

0 .8 3 2

0 .8 3 3

0 .8 3 8

0 .8 2 0

0 .8 5 3

B u lg a ri a (B G )

0 .6 8 4

0 .7 8 5

0 .7 9 6

0 .7 9 4

0 .6 7 7

0 .7 0 7

C h in a , S T

co m p a n ie s

0 .9 8 5

0 .9 8 4

0 .9 8 2

0 .9 8 0

0 .9 8 6

0 .9 8 9

C o lo m b ia

(C O )

0 .7 2 6

0 .7 5 5

0 .7 5 4

0 .8 1 8 +

0 .7 8 9

0 .7 4 9

0 .7 7 4

0 .8 2 4 +

C ro a ti a (H

R )

0 .8 4 4

0 .8 6 2

0 .8 5 8

0 .8 6 5

0 .8 5 3

0 .8 6 3

0 .8 4 7

0 .8 6 3

C ze ch

R ep u b li c (C

Z )

0 .8 1 1

0 .8 1 2

0 .8 2 3

0 .8 3 0

0 .8 1 3

0 .8 1 8

0 .8 4 2

D en m a rk

(D K )

0 .8 0 3

0 .8 0 6

0 .8 0 9

0 .8 3 3

0 .8 1 8

0 .8 0 4

0 .8 1 3

0 .8 4 3

E st o n ia

(E E )

0 .8 2 3

0 .8 2 4

0 .8 3 9

0 .8 6 6

0 .8 7 4

0 .8 3 9

0 .8 4 7

0 .8 9 9

F in la n d (F I)

0 .8 6 7

0 .8 7 0

0 .8 8 5

0 .8 6 8

0 .8 7 3

0 .8 7 9

0 .8 9 4

F ra n ce

(F R )

0 .7 3 9

0 .7 7 3

0 .7 9 9

0 .8 0 5

0 .8 2 6

0 .8 0 0

0 .8 1 0

0 .8 4 5

G er m a n y (D

E )

0 .6 7 3

0 .6 8 4

0 .6 8 5

0 .7 3 4

0 .7 0 4

0 .7 0 1

0 .6 9 0

0 .7 6 2

H u n g a ry

(H U )

0 .7 4 2

0 .7 3 6

0 .7 4 3

0 .7 8 8

0 .7 6 7

0 .7 6 5

0 .7 4 7

0 .8 2 0

Ic el a n d (I S )

0 .6 6 4

0 .6 7 0

0 .6 7 1

0 .7 8 4 +

0 .6 8 3

0 .7 1 4

0 .7 0 8

0 .8 2 4 +

Ir el a n d (I E )

0 .6 7 9

0 .6 6 3

0 .6 6 6

0 .7 1 8

0 .6 6 1

0 .6 6 5

0 .6 8 0

0 .7 2 4

It a ly

(I T )

0 .8 0 6

0 .8 0 2

0 .8 1 0

0 .8 3 5

0 .8 3 9

0 .8 0 9

0 .8 1 6

0 .8 6 8

L a tv ia

(L V )

0 .6 7 8

0 .6 9 0

0 .7 0 7

0 .7 4 5

0 .7 2 0

0 .7 1 1

0 .7 3 0

0 .7 7 1

N et h er la n d s (N

L )

0 .7 5 2

0 .7 5 6

0 .7 6 8

0 .7 8 4

0 .7 6 0

0 .7 5 9

0 .8 0 7

N o rw

a y (N

O )

0 .7 1 6

0 .7 4 1

0 .8 0 3

0 .7 4 9

0 .7 4 4

0 .7 4 3

0 .8 1 4

P o la n d (P L )

0 .9 0 3

0 .8 9 1

0 .8 9 7

0 .9 1 5

0 .8 9 9

0 .8 9 6

0 .8 9 5

0 .9 1 3

P o rt u g a l (P T )

0 .7 4 1

0 .7 5 3

0 .7 5 7

0 .7 6 1

0 .7 8 0

0 .7 5 8

0 .7 6 5

0 .7 9 7

R o m a n ia

(R O )

0 .7 5 8

0 .7 1 8

0 .7 7 3

0 .8 0 5

0 .7 5 7

0 .7 5 5

0 .7 5 3

0 .7 7 4

R u ss ia n F ed er a ti o n (R

U )

0 .8 1 1

0 .8 0 8

0 .8 1 0

0 .8 5 4

0 .8 1 1

0 .8 1 8

0 .8 6 2

S lo v a k ia

(S K )

0 .7 7 7

0 .7 7 3

0 .7 7 5

0 .7 8 2

0 .8 1 0

0 .7 6 1

0 .7 7 3

0 .8 0 7

S lo v en ia

(S I)

0 .7 3 7

0 .7 0 3

0 .7 1 9

0 .7 0 4

0 .7 1 1

0 .7 3 9

0 .6 8 6

0 .6 9 8

S p a in

(E S )

0 .7 3 4

0 .7 5 5

0 .7 6 7

0 .7 6 8

0 .8 4 9

0 .7 6 6

0 .7 7 6

0 .8 5 8

Financial Distress Prediction 163

T a b le

6 ( C o n ti n u ed )

T es t d a ta

A U C

fo r d iff er en t m o d el s

C o u n tr y

B en ch m a rk

M o d el

1 M o d el

2 M o d el

3 M o d el

4 M o d el

5 M o d el

6 M o d el

S w ed en

(S E )

0 .8 1 3

0 .8 2 6

0 .8 2 9

0 .8 4 3

0 .8 3 1

0 .8 9 2

0 .9 0 1

U k ra in e (U

A )

0 .7 0 8

0 .7 1 2

0 .7 2 0

0 .7 4 2

0 .7 1 8

0 .7 5 8

U n it ed

K in g d o m

(G B )

0 .6 9 9

0 .7 0 8

0 .7 1 3

0 .7 1 8

0 .7 6 4

0 .7 1 4

0 .7 3 1

0 .7 8 5

U n it ed

S ta te s (U

S )

0 .7 1 0

0 .6 8 9

0 .6 8 7

0 .6 8 6

0 .8 1 6 +

0 .6 8 9

0 .7 1 4

0 .8 1 6 +

A v er a g e o f A U C

0 .7 6 8

0 .7 7 3

0 .7 7 8

0 .8 0 1

0 .7 9 9

0 .7 8 2

0 .7 7 9

0 .8 2 3

S ig n ifi ca n ce : A U C

b et te r th a n b en ch m a rk : 0 .0 0 0 1 = + + + + , 0 .0 0 1 = + + + , 0 .0 1 = + + , 0 .1

= + ; A U C

w o rs e th a n b en ch m a rk : 0 .0 0 0 1 = — , 0 .0 0 1 = — ,

0 .0 1 = – , 0 .1

= -.

M o d el s:

B en ch m a rk

= T h e L R

m o d el

es ti m a te d fo r a ll d a ta

w it h Z ” -m

o d el

(1 9 8 3 ) v a ri a b le s;

M o d el

1 = T h e M D A

m o d el

es ti m a te d fo r co u n tr y

d a ta ; M o d el

2 = T h e L R

m o d el

es ti m a te d

fo r co u n tr y d a ta ; M o d el

3 = T h e L R

m o d el

es ti m a te d

fo r co u n tr y d a ta

w it h

y ea r d u m m ie s;

M o d el

4 = T h e L R

m o d el

es ti m a te d fo r co u n tr y d a ta

w it h si ze

v a ri a b le s; M o d el

5 = T h e L R

m o d el

es ti m a te d fo r co u n tr y d a ta

w it h a g e ca te g o ry

d u m m ie s;

M o d el

6 = T h e L R

m o d el

es ti m a te d fo r co u n tr y d a ta

w it h in d u st ry

d u m m ie s;

M o d el

7 =

T h e L R

m o d el

es ti m a te d fo r co u n tr y d a ta

w it h a ll v a ri –

a b le s.

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

ferences in AUCs given by Models 1 and 2 are generally small. Model

1 is clearly outperformed by Model 2 in Romania only. Thus, country-

level evidence does not support H2 (estimation method).

Model 3 (LR model with year dummies) leads in most countries to

a clearly higher AUC than the benchmark model. This evidence gives

support to H3 (bankruptcy year effect). Model 3 leads to a lower

AUC than the benchmark model in Austria, Bosnia, Slovenia, and the

United States. Model 4 (LR model with size variables) leads to

improved performance in nearly every country, supporting H4. This

improvement is significant, however, only in the United States, where

the AUC increases to 0.816 in Model 4, in contrast to the benchmark

model, for which it is only 0.710. This improvement is also notable in

Bulgaria, France, Latvia, Spain, and the United Kingdom. Model 5

(LR model with age category dummies) gives higher AUCs than the

benchmark model for several countries, but none of the improvements

are significant. The positive effect is particularly strong in Bulgaria,

France, and Iceland. Although there are also negative effects on AUC,

this evidence only weakly supports H5 because these negative effects

are relatively small.

Model 6 (LR model with industry dummies) shows both negative

and positive effects on AUCs when compared with the benchmark.

However, the only significant effect is the negative effect found in Aus-

tria. In Bulgaria and Slovenia, the AUC notably decreases due to the

industry dummies. However, there are notable positive effects on the

AUC in, for example, France, Iceland, Latvia, and Sweden. Thus, the

effect is not systematic and gives only weak support to H6. Model 7

(LR model with all variables) leads to a remarkable increase in AUC

compared with the benchmark model in Colombia, Iceland, and the

Unites States and to notable improvements in AUC in Belgium, Den-

mark, Estonia, France, Germany, Hungary, Latvia, Norway, Spain,

and the United Kingdom. Negative effects on AUC are found only in

Austria and Slovenia. Thus, classification performance of the Z”-Score

model with “all test” data remarkably increases in most countries

when different effects are taken into account by additional variables.

This increase is found in most European countries and in Colombia

and the United States. In China, the AUC for the ST sample is extre-

mely high for the Z”-Score LR model, and it can be improved only

slightly by additional variables.

Financial Distress Prediction 165

7. Summary of the Study and a Suggested Extension

The purpose of this study was to assess the classification performance

of the Z”-Score model originally introduced by Altman (1983) using a

very large international dataset. We test how the original version of

the Z”-Score model performs in different countries and how re-estima-

tions using another statistical method and different additional variables

affect the classification performance when the data are very heteroge-

neous. For this kind of testing, seven research hypotheses on classifica-

tion performance are formulated. These hypotheses are tested for all

data and separately for country data (country-level analysis). The

estimation data are from 31 countries, and the results are validated for

34 countries. The countries are mainly from Europe, but three

non-European countries are included (China, Colombia, and the US).

The statuses used in the classification are mainly bankruptcy/active,

but receivership firms are also considered to have failed. In the Chinese

data, ST (special treatment) and delisted firms are also separately

analyzed as failed firms.

The analyses for all data show that the original Z”-Score model per-

forms very satisfactorily in an international context. The effects of the

four financial ratios on performance are well balanced, although Book

Value of Equity/Total Liabilities (BVETD) showed a very small contri-

bution in re-estimation. The original model performs very well in sev-

eral countries, such as Poland, Finland, and China (ST firms). The re-

estimation of the coefficients using MDA only marginally improved

the classification performance, thus weakly supporting the obsolescence

hypothesis (H1) or, put differently, showing that the original coeffi-

cients are extremely robust across countries and over time (opposite

to, e.g., Grice and Ingram, 2001). This same conclusion holds for the

re-estimation of the model using LRA because the performance results

are very similar to those of MDA (H2). The use of additional variables

in the model generally improves the classification accuracy of the origi-

nal model, but the results for countries are dependent on the distribu-

tion of failed and non-failed firms. When the coefficients are estimated

for all data, the effects on performance in a country depend on how

the distributions in that country correspond to the distributions in all

data. For all sets of additional variables, the performance is generally

improved, but the improvement is not strong and the effects vary by

country. Thus, the evidence gives weak support for the effects of all

additional variables. For the effects of bankruptcy year (H3) and size

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

(H4), the effects are stronger, but the variations in the effects between

countries are also stronger. The effects of age (H5), industry (H6), and

country (H7) are marginal. When all additional variables are included

in the same model, the performance generally significantly increases,

but at the same time, the variations among countries become stronger.

In summary, our evidence indicates that the original Z”-Score

model performs well in an international context. It is, however, possi-

ble to extract a more efficient country model for most European coun-

tries and for non-European countries using the four original variables

accompanied by a set of additional background variables. Considering

practical applications, it is obvious that while a general international

model works reasonably well, for most countries, the classification

accuracy may be somewhat improved with country-specific estimation

(a conclusion similar to Xu and Zhang, 2009). In a country model, the

information provided even by simple additional variables may help

boost the classification accuracy to a much higher level.

In finance and accounting research, failure prediction models may

be utilized as risk measures in many different contexts. Where failure

prediction modeling is not the primary focus, it would be time-con-

suming, uneconomical, and superfluous to first estimate a failure pre-

diction model (or models) and then study the phenomenon of interest.

In such instances, a well-tested general model that works reliably and

consistently across different countries is highly desirable. Based on our

empirical tests in this study, the original Z”-Score model and its re-

estimated version, containing the four Altman (1983) study variables

with coefficients re-estimated using a large dataset, work consistently

well internationally and are easy to implement and interpret. Thus, this

kind of accounting-based model can be used by all interested parties,

especially internationally active banks or other financial institutions,

not only for failure or distress prediction but also for other managerial

purposes such as provisioning and economic capital calculation. Inter-

nationally active banks need to develop a universal tool that can be

applied in all subsidiaries and branches to control risk across the

whole banking group.

Further research should focus on other modifications and extensions

than those presented in our paper, such as using alternative modeling

techniques (e.g., panel data analysis), introducing new variables (e.g.,

macroeconomic data), and testing its usefulness with data from other

countries (e.g., emerging markets).

Financial Distress Prediction 167

Notes

1. See Altman and Saunders (1997) for a review of research over this 20 year period. 2. Dichev (1998) compares the Altman Z-score and Ohlson O-score approaches. 3. Grice and Dugan (2003) present a re-estimation of Ohlson’s and Zmijewski’s models. 4. We use bankruptcy, failure, default and financial distress as equivalents. 5. Research devoted to the application of the Z-Score model before 2000 was reviewed by Grice and Ingram (2001). 6. Most of the models focused on stock-exchange-listed firms; thus, the Z’-Score and Z”-Score models were not used. 7. See Zhang et al. (2010) for the rationale for using special-treatment firms as a proxy for bankruptcies. These are firms put on probation by the stock exchange for poor operating performance and/or negative equity. 8. This is done because the results about predictability were also good for such a small sample. 9. These firms are included only in the test data because the predictability of failure was exceptionally poor. 10. From the weighting procedure, it follows that the score (cut-off-value) that best separates failures from non-failures is 0.50 (or, alternatively, 50 per cent). Although the score (logit) in principle has a probability interpretation, the “probabilities” estimated using this weighting scheme in this study do not, however, represent empirical PD’s. It would still require calibration procedures for the models to obtain PD’s that corre- spond to associated empirical PD’s in the population. But this is not attempted in the study, as our focus is more general (the classification accuracies of the models across countries). It is also worth noting that the original Z”-Score does not have a PD inter- pretation either.

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Appendix. Medians of the Altman (1983) model (Z”) variables by status and country.

Non- failed Failed

Country WCTA RETA EBITTA BVETD

Austria (AT) 0.170 �0.278 0.260 �0.145 0.055 �0.071 0.465 �0.022 Belgium (BE) 0.136 �0.087 0.157 �0.107 0.052 �0.025 0.460 0.029 Bosnia (BA) 0.087 �0.019 0.148 �0.101 0.032 �0.056 0.580 0.000 Bulgaria (BG) 0.216 0.102 0.272 �0.019 0.075 �0.019 0.504 0.068 China (CN) 0.105 0.037 0.064 0.032 0.064 0.048 0.870 0.590 China, delisted data, DL

0.158 0.069 0.286 0.347 0.059 0.091 0.923 0.840

China, ST data 0.106 �0.192 0.281 �0.012 0.052 �0.087 0.776 0.308 Colombia (CO) 0.244 0.018 0.282 0.047 0.104 0.005 0.940 0.235 Croatia (HR) 0.093 �0.182 0.130 �0.205 0.030 �0.014 0.274 0.000

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

Appendix (Continued)

Non- failed Failed

Country WCTA RETA EBITTA BVETD

Czech Republic (CZ)

0.196 �0.208 0.230 �0.167 0.050 �0.019 0.591 �0.076

Denmark (DK) 0.128 �0.139 0.306 �0.050 0.005 �0.041 0.758 0.025 Estonia (EE) 0.222 �0.109 0.455 0.067 0.050 �0.063 1.169 0.136 Finland (FI) 0.233 �0.169 0.359 �0.247 0.082 �0.125 0.744 �0.163 France (FR) 0.146 0.006 0.223 0.010 0.059 �0.006 0.501 0.077 Germany (DE) 0.307 0.176 0.150 0.014 0.069 0.037 0.335 0.073 Greece (GR) 0.127 �0.044 0.049 �0.213 0.039 �0.010 0.460 0.111 Hungary (HU) 0.135 �0.040 0.271 0.047 0.054 0.002 0.717 0.130 Iceland (IS) 0.051 �0.195 0.046 �0.215 0.051 0.000 0.196 �0.141 Ireland (IE) 0.198 0.017 0.383 0.117 0.029 �0.010 0.728 0.168 Italy (IT) 0.107 �0.170 0.077 �0.087 0.032 �0.041 0.178 �0.029 Latvia (LV) 0.102 �0.015 0.150 0.030 0.069 0.027 0.342 0.088 Lithuania (LT) 0.205 �0.041 0.273 0.055 0.054 0.003 0.686 0.117 Netherlands (NL)

0.204 0.000 0.303 0.050 0.065 �0.012 0.511 0.079

Norway (NO) 0.185 0.028 0.139 �0.080 0.068 �0.047 0.398 0.069 Poland (PL) 0.198 �1.142 0.258 �0.662 0.076 �0.048 0.850 �0.501 Portugal (PT) 0.164 �0.051 0.133 �0.067 0.028 �0.024 0.343 0.025 Romania (RO) 0.071 �0.151 0.158 �0.113 0.041 �0.015 0.250 �0.048 Russian Federation (RU)

0.088 �0.262 0.106 �0.136 0.043 �0.026 0.194 �0.051

Serbia (RS) 0.093 �0.027 0.129 �0.019 0.027 �0.018 0.389 0.000 Slovakia (SK) 0.137 �0.119 0.185 0.001 0.060 �0.001 0.496 0.065 Slovenia (SI) 0.109 �0.002 0.215 0.043 0.039 0.004 0.434 0.108 Spain (ES) 0.117 �0.026 0.139 �0.004 0.029 �0.047 0.336 0.051 Sweden (SE) 0.266 0.011 0.357 0.011 0.058 �0.041 0.749 0.086 Ukraine (UA) 0.053 �0.151 0.034 �0.166 0.003 �0.028 0.442 �0.007 United Kingdom (GB)

0.179 �0.032 0.294 0.049 0.031 �0.002 0.579 0.107

U.K., liquidation dataset

0.179 0.023 0.294 0.060 0.031 0.000 0.579 0.132

United States (US)

0.164 �0.031 0.374 �0.004 0.003 �0.245 0.800 0.078

Average of medians

a 0.153 �0.092 0.213 �0.050 0.048 �0.025 0.555 0.075

WCTA, Working Capital/Total Assets; RETA, Retained Earnings/Total Assets; EBITTA, EBIT/Total assets; BVETD, Book Value of Equity/Total Liabilities. a Because the two datasets for non-failed U.K. firms are identical, the non-failed medians are used only once in the calculation of the averages.

Financial Distress Prediction 171

Statistics

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Review an Empirical Analysis of Altman’s Z-Score Model doi:10.1111/jifm.12053

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