Insolvency Prediction

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Insolvency prediction for assessing

corporate financial health

DRAGAN SIMIC , Department of Transportation, Faculty of Technical Sciences,University of Novi Sad, 21000 Novi Sad, Serbia.

E-mail: [email protected]

ILIJA KOVACEVIC, Department of General Technical Discipline, Faculty of

Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia.

E-mail: [email protected]

SVETLANA SIMIC, Department of Neurology, Medical School, University of

Novi Sad, 21000 Novi Sad, Serbia.E-mail: [email protected]

Abstract

The prediction of corporate financial failure, crucial for the prevention and mitigation of economic downturns in a national

economy, requires the categorization of healthy and unhealthy companies. This study examines the case of Serbia and applies

multivariant statistical methods and specific artificial neural network architecturesthe self-organizing map (SOM)to

assess the corporate financial health of various companies. Financial ratios drawn from corporate balance sheets become the

independent variables in a multivariate discriminant analysis (MDA). These financial ratios and the discriminant Z-score in

the MDA form the input for the SOM, which creates a hybrid MDA-SOM model that is capable of predicting corporate

financial insolvency. The experimental results of this research correctly estimate company financial health in 95% of cases.

These are reliable predictions that are comparable with similar studies in other countries.

Keywords: Corporate financial health, corporate insolvency, multivariate discriminant analysis, self-organized maps.

1 Introduction

Corporate financial failure prediction is crucial for the prevention or mitigation of negative economic

cycles in a national economy. Corporate insolvency prediction models help to identify future business

failures and provide early warnings of future financial distress. They assess corporate financial health

in industry, and especially the financial sector. The absence of early warning systems does nothing

to stop financial upheaval such as the collapse of the Thai financial sector in 19971998 [30]. During

the recent East Asian economic crisis, trading in the shares of 58 out of 91 financial companies was

suspended in the second half of 1997, and another 12 finance companies suffered the same fate in

1998. The only bright side of any economic crisis, however, is the opportunity to examine failure

prediction models for financial institutions.

In general, predictive models warn auditors of a companys vulnerability and protect them against

charges of wilful breach of duty for failure to disclose potential insolvency and corporate distress

[19]. Corporate distress can, within a relatively short time span of one or two financial years, lead

a company to bankruptcy, which entails significant private costs for many parties, including share-

holders, debt financers, employees, suppliers, customers and managers. All of these stakeholders

share a legitimate interest in monitoring the financial health of a company.

The Author 2011. Published by Oxford University Press. All rights reserved.

For Permissions, please email: [email protected]:10.1093/jigpal/jzr009


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Assessing corporate financial health 537

Economic and financial theories on company failure do not provide a rigorous basis for selecting

particular ratios. As a result, empirical studies on failure prediction models for industrial companies

usually examine standard sets of financial ratios that are considered important when explaining cor-

porate financial health. Indeed, a very natural set theory, which overcomes certain formal limitations

of contemporary economic theory, may exist [32]. All of these models serve to help managers tracka companys performance over many years and identify important trends.

The research of Chung et al. [11] into insolvency prediction for the financial industry in New

Zealand is the inspiration for this article, which proposes improvements to their model by applying

a specific artificial neural network (ANN) topologyself-organizing maps (SOMs) [20]. This study

aims to apply multivariant statistical methods and ANNs topology (SOM) to the assessment of

corporate financial health over two financial years (20082009) in Serbia.

The following sections provide a brief overview of hybrid artificial intelligence (AI) systems and

some background models for corporate insolvency prediction. Section 3 sets out a comprehensive

review of the literature on financial failure prediction. Section 4 describes the conceptual model for

insolvency prediction advanced by Chung et al. along with some suggestions for its improvement.

Section 5 presents an applied empirical analysis, the development of the data set, a discriminantestimation model and the batch training algorithms. Section 7 presents the experimental results,

while section 8 contains the final conclusions and outlines future areas of research.

2 Background

Artificial intelligence techniques have demonstrated a capability to solve real-world problems in

science, business, technology and commerce. The integration of different learning techniques and

their adaptation, which overcomes individual constraints and achieves synergetic effects through

hybridization or fusion, has in recent years contributed to a large number of new intelligent system

designs [1]. The hybridization of intelligent techniques, drawn from different areas of computational

intelligence, has become prevalent because of the growing awareness that they outperform individual

computational intelligence techniques. In a hybrid intelligence system, a synergetic combination of

multiple techniques is used to build an efficient solution to deal with a particular problem [12].

Nowadays, hybrid AI systems research encompasses many domains: cardiologyhybrid decision

support system for endovascular aortic aneurism repair follow-up [21]; neurologyrule-based fuzzy

logic system for diagnosis of primary headaches [29]; and meteorologyhybrid soft computing

models to identify typical meteorological days [13].

One innovative research area that uses fusers as part of a multiple classifier system, which is dis-

cussed in designing fusers on the basis on discriminantsevolutionary and neural network methods

of training [36], could form part of future research into corporate insolvency prediction and the

assessment of corporate financial health. Assessment presents a categorization problem in order to

categorize companies as either healthy or unhealthy.

The leading causes of corporate failure are often divided into: economic, financial, fraud or disaster

[4]. Among the economic factors figure industrial weaknesses and poor locations, while the most

widely recognized financial factors are excessive debt and cash flow problems. Financial difficulties

are often the result of managerial neglect.

2.1 Financial ratios analyses

Financial ratio analyses are a stimulus to good managerial practice and are often applied in various

corporate appraisals. They are particularly useful in strategic management, which seeks to address the


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538 Assessing corporate financial health

future survival of corporate businesses. Financial ratio analysis, therefore, forms a very important

aspect of corporate diagnosis and general business information and provides a very quick and

effective snapshot of a companys operations and performance. Conventional ratio analysis involves

calculating single ratio values by employing two financial values. There are four broad categories

of traditional ratios:Leverage: equity market value over total debt (MKTCAPTL), equity market value over total assets

(MKTCAPTA), equity market value over total equity (MKTCAPEQ), debt over equity ratio (DER-

ATIO), financial leverage multiplier (TAEQ), fixed assets over equity and long-term liability (FAE-

QLTL) and retained earnings over total assets (RETAINTA).

Profitability: return on assets (ROA), return on equity (ROE), gross profit margin (GRMARGIN),

net profit margin (NPMARGIN), operating profit margin (EBITSALE) and EBIT over total assets

(EBITTA).

Turnover: working capital over sales (WCSALES), inventory turnover (INVETURN), fixed assets

turnover (FATURN), total assets turnover (TATURN), equity turnover (EQTURN), inventory over

sales (INVSALES), receivable turnover (RECETURN), quick assets over sales (QUISALES) and

current assets over sales (CASALES).

Liquidity: working capital over total assets (WCTA), cash ratio (CASHCL), cash over total assets

(CASHTA), cash over sales (CASHSALE), current ratio (CCRATIO), current assets over total

assets (CATA), current liability ratio (CLEQUITY), quick ratio (QUIRATIO), quick assets over

total assets (QUITA) and inventory over current assets (INVECA).

Equally, the use of other groups of ratios depends on the nature of the financial study, which may

use similar or different financial ratios such as, for example, the CAMELS rating system: capital

adequacy, asset quality, management quality, earnings, liquidity and sensitive market risk.

2.2 Insolvency prediction models2.2.1 Univariate and multivariate statistical methods

The earliest insolvency prediction models employed financial ratios using univariate statistics.

Beaver designed experimental models that use financial ratios for examining corporate failure [6].

They follow a univariate analysis that examines the relationship between individual financial ratios

and insolvency. He concluded that single best predictor is cash flow to debt ratio. However, models

which focus on a single ratio are simplistic and unable to grasp the complexity of financial failure.

As is well known, the financial status of a company is multidimensional nowadays and no single

measure is able to grasp all of its dimensions.

Extensive research into the prediction of financial failure has continued apace since the late 1960s,

by applying a variety of statistical methods to solve bankruptcy prediction problems. Statistical meth-

ods include linear discriminant analysis (LDA), multivariate discriminant analysis (MDA), quadratic

discriminant analysis (QDA), multiple regression, logistics regression analysis (LRAlogit), prob-

abilistic regression (probit), k-means cluster analysis (CA) and factor analysis (FA).

Altman built on Beavers work, but instead of a univariate approach he used MDA to examine

corporate failure [2]. Multivariate statistical methods such as MDA require uncorrelated variables.

However, to construct an optimal multivariate predictive model, one must determine which ratios are

best at detecting potential failures, and the most appropriate model weightings in each case. MDA

computes the discriminant coefficients and selects the appropriate weights, which will separate out

the advantage values of each group, while minimizing the statistical distance of each observation


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and its own group means. Altman employed MDA to construct a predictive algorithm based on five

key financial ratios (WCTA, RETAINTA, EBITTA, MKTCAPTL and TATURN) to calculate the

Z-score (1). Cash flow and debt ratios appeared to be important predictors of bankruptcy. In his first

research, the predictive algorithm correctly differentiated 94% of failed companies with data 1 year

ahead of failure.

Z=0.012 WCTA+0.014 RETAINTA+0.033 EBITTA+0.006 MKTCAPT+0.999 TATURN

(1)

Other studies that adopt the MDA approach include [8] and [15]. These studies inspired other

research models to predict corporate failure including: LRA [25] and recursive partitioning analysis

[17]. A variety of statistical methods and ANN topologies have been applied to solve the bankruptcy

prediction problem.

2.2.2 Challenges for an ANN prediction model

In spite of the success of ANN models, the research community should address a number of openissues. Even though prediction of a default event is in itself very useful, an estimate of a default

probability is also highly desirable. Banks typically have several prediction systems in place as they

are essential for computing loss levels in portfolio credit risk estimation. They also make lending

decisions based on the contribution of these predictions. Thus, a default probability rather than a

(binary) default prediction is a valuable resource for banks. Even though there are some objective

function measures to achieve that, such as the cross-entropy error function [7], experience with this

objective function is not always favourable. ANN and data-mining techniques can complement and

improve the accuracy of financial distress prediction models [10]. This opens up new possibilities

in the modelling of hybrid systems for corporate insolvency prediction.

Another avenue of research considers macroeconomic indicators as input to the neural networks

(NN). The prevailing economic condition (as well as the current interest rates, gross domestic product(GDP), unemployment rates, price indices, inflation, investment, international trade and international

finance) can have a significant effect on the probability of bankruptcy. However, very few studies

consider these factors in conjunction with NN models.

3 Literature review

The development of Artificial Intelligence has contributed to the emergence of new techniques

such as ANNs for financial distress prediction. In 1993, an ANN methodology for the analysis

and prediction of bankruptcy was introduced in Spain for the first time. The model was applied

to 66 banks, 29 of which went bankrupt. Its authors obtained a very high classification percentage

for this financial service industry. Their approach to the problem was to establish a procedure that

incorporates NN in the classification of company health.

Martinez [23] describes a neural network of 70 insurance companies (35 bankrupted and 35 active).

The same 24 ratios in the earlier study were applied. The results correctly classified around 96%

between the first and the fourth year preceding the crisis, while the percentage in the discriminant

analysis varied between 72% in the second year and 92% 1 year prior to distress.

Rey [27] applied a neural network model for financial distress prediction which operates by

selecting a sample of non-financial companies with suspended payments or that went bankrupt

during a 3-year period (from 1994 to 1996). The sample consisted of 194 companies and involved


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the calculation of 71 financial ratios. The model correctly classified 84 (95%) out of 88 firms 1 year

before crisis and 70 (83%) out of 84 companies in the following year.

Gonzales et al. [18] examines a neural network technique applied to 444 companies, half of

which were insolvent. Financial ratios for profitability, liquidity, growth and structure were used as

independent variables. The model correctly classified 92% of companies, as well as the financialratios with significant predictive power and those for profitability and generation of cash flow.

A study of 100 companies from Galicia (Spain) in [14] used a neural network model. Its objec-

tive was to correctly distinguish between companies in distress and financially sound companies.

Introducing 17 financial ratios, the authors obtained ratings of 95 and 93%, respectively, for the first

and the second year prior to the crisis.

In [33], the results from a logit model and from a neural network model are compared, both of

which were applied to a sample of 450 companies over a 2-year period (19981999). The study

concluded that neither model was superior.

Andres [5] conducted a comparative study of the classification percentages for a neural network

model that uses both logit and MDA. This study employed 25 ratios drawn from the financial

statements of 1636 companies, half of which were in distress. The MDA was a better predictivemodel than the neural network which in turn outperformed the logit model.

The use of NNs for bankruptcy prediction began in the 1990s and continues to be an active area of

research. Various reasons suggest that a non-linear rather than a linear approach is the best option;

one of which relates to the saturation effect in the relationships between the financial ratios and the

prediction of default.

Both [34] and [35] discuss NNs in business applications and contain sections on bankruptcy

prediction. Moreover, [16] conducts a survey on the classical empirical approach, while [37] includes

a concise review of existing work on NN bankruptcy prediction. The majority of the NN approaches

to default prediction use multilayer networks.

In one of the first such studies, [24] looked at the bankruptcy prediction problem in the 1990s.

In this study, Altmans financial ratio (described above) is the input to the NN, and his methods

are applied to a number of bankrupt and solvent US firms, along with MDA for comparative pur-

poses. The data used for the bankrupt firms were taken from their last financial statements before

bankruptcy was declared. A total of 128 firms were included, and several experiments were per-

formed, in which the proportion of bankrupt/healthy firms in the training set has been varied. The

NN achieved a Type I correct classification accuracy in the range of 7781% (depending on the

training setup), and a Type II accuracy in the range of 7886%. The corresponding results for

MDA were in the range of 5970% for Type I accuracy, and in the range of 7886% for Type II

accuracy.

The reason why the particular indicators in [24] (the same as Altmans) were chosen are because

they are frequently used as a normalizing factor (as in Altmans 1, 2, 3 and 5 indicators). Current

assets can and will usually be converted into cash fairly fast. Finally, the firms sales are probably the

least effective of Altmans five indicators, because sales to total assets can vary a lot from industry

to industry.

The problem of bank failure prediction is considered in [31]. The study compares several methods:

MDA, LR, K-nearest neighbour (KNN), a decision tree classification algorithm (ID3), single-layer

network and multilayer network. The multilayer network was the best in the case of 1-year ahead

predictions, while LR was proven to be the best in the case of 2-year ahead predictions. When they

used a leave-one-out procedure instead of a hold-out sample, the clear winner was the multilayer

network. KNN and ID3 were almost always inferior to the other methods. A comparative analysis

of ANN models and their applications in bankruptcy prediction is discussed in [9].


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The problem of bank failure prediction in Thailand is considered in [30]. The authors compared

three neural networks: learning vector quantization, probabilistic neural network and feedforward

network with back propagation learning. This study applied principal component analysis in order

to reduce dimensionality. The data with dimensions of between 5 and 9 are the most efficient,

while the data with dimensions of below 5 provided the lowest level of accuracy. Comparing thesemodels, learning vector quantization outperforms the two other models, in terms of prediction

accuracy and bias.

Investigations into financial distress predictions are characterized by a common adoption of dis-

criminate and bivariate logit analysis in the research methodology. All of the models presented

above have certain limitations and disadvantages that concern the definition of their dependent

and independent variables, the reliability of the financial information they use, and statistical and

methodological limitations.

4 The conceptual model proposed by Chung for insolvency prediction

Research by Chung et al. [11] into insolvency prediction for the financial industry in New Zealand

has inspired this article. Their research uses a hybrid MDA model and ANN and presents an approach

to model building and the conceptual model for insolvency prediction. Although the application of

macroeconomic variables is proposed in the conceptual model, these variables were not used. This

research reveals not only the great advantages of a hybrid MDA-ANN model of this type, but

it also reveals certain shortcomings of the ANNs themselves. As with any system, ANN has its

limitations [3]:

(1) The learning stage can be very drawn out.

(2) The system might not achieve a stable absolute minimum configuration, but could stay with

local minimums without being able to move to the optimum configuration.

(3) The system may begin to oscillate in the learning phase.

(4) It is necessary to repeat the learning phase when significant changes take place in the actualsituation compared with the implicit situation in the training examples or when the set of

examples is not representative of reality.

(5) The analysis of the weightings is complex and difficult to interpret. There is little network

transparency in the examination of the system logic, making it difficult to identify the causes

of the errors/defective responses.

The main problem of all neural network algorithms in general is that, they are rather unstable.

Running the same algorithm, even using the same parameters; can lead to quite different results.

Using the proposed conceptual model in research and aware of the aforementioned limitations

of the ANN, we propose the use of the SOM rather than the ANN. Chungs conceptual model of

insolvency prediction for corporate finance with our modifications is presented in Figure 1.

Furthermore, research has shown that the SOM yields better results than the ANN when the

problem has fewer dimensions. The model proposes using Altmans Z-score as an input to the ANN

which represents a 5D or 6D data set. SOM architecture has been determined as very efficient for

such a small number of dimensions.

5 Empirical analysis

The objective of this study is the evaluation and the classification of the financial health of a random

sample of medium-sized and large companies in Serbia. In 2009, legal regulations allowed public


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OutputSpecific classifier:

Solvent vs. Insolvent

companies

Learning system

Input

Financial Ratios

MDA

Input

Macroeconomic

variables

Knowledge Base

Not implemented

Self-Organising Maps - New ANN topology

FIG. 1. Chungs conceptual model of insolvency prediction for corporate finance with our modifi-

cations (in grey area).

access to the balance sheets of medium- and large-sized companies. However, the register does not

include financial reports for small companies, which is why the data sets only refer to medium-sized

and large companies over the 2-year period (20082009) under study.

The definition of corporate failure in this case study is a court filing by the firm under Chapter 7 or

11 of the US Bankruptcy Code. Both of these chapters are share the same meaning and significance

in Serbia as they do in the USA. Chapter 7 of the US Bankruptcy Code covers corporate liquidations,

and Chapter 11 refers to corporate Reorganization (called Restructuring in Serbia), which usually

occurs after a period of financial distress.

The unemployment rate in Serbia remains uncertain and no clear definition exists as yet. Frequent

modifications to legal regulations have led to changing definitions of unemployment categories(i.e. different definitions of unemployed status). Thus, the current situation is that the number of

unemployed people seemingly remains unchanged, as does the number of employed people. Very

large numbers of people are officially recorded as living without social security provision, training

opportunities and assistance, although the official statistics would rise steeply if everybodys details

were held on record.

5.1 Data set development

Information made public in 2010 shows that, since 2004, the accounts of 10,400 companies have

been blocked or the companies themselves have been declared insolvent or illiquid. However, these

companies still manage to survive.

In the present study, the corporate data consists of the financial statements of 59 companies taken

from the Serbian registry of companies. Most medium-sized and large companies in this random

sample are solvent, although a smaller number are insolvent. Chapter 7 of the US bankruptcy

code has been applied to seven companies, and Chapter 11 of the US bankruptcy code to three

companies.

This verification was possible only for companies with financial reports over the period

20082009. Naturally, the register contains a much larger number of companies that are bankrupt,

but for which no financial records exist.


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0

5

10

15

20

25

30

35

SAFE GREY DISTRESS -3,00

-2,00

-1,00

0,00

1,00

2,00

3,00

4,00

5,00

2008

2009

SAFE GREY DISTRESS

2008

2009

(a) (b)

FIG. 2. (a) Number of companies in the Safe, Grey and Distress zones. (b) Average Z-scores.

This study follows the method suggested in [22] to reduce the number of independent variables

or financial ratios that are summarized in Section 2.1. All the variables used in the Z-score model

are included here. However, one of Altmans ratios (Equity market value to total debt) was notselected because of its inappropriate nature for the companies under observation.

5.2 Estimation of discriminant model

A gradual regression to develop an optimal MDA model was implemented. The overall fit of the

discriminant function involves three tasks: (i) calculating a discriminant Z-score for each observation;

(ii) evaluating group differences in discriminant Z-scores; and (iii) assessing the accuracy of the

prediction for group membership.

Following the estimation of the discriminant model, the maximum Z-score values were 7.02 in

2008, and 6.89 in 2009 and the minimum Z-score values were

6.92 in 2008, and

8.04 in 2009.Figure 2a shows the number of companies in the three zones defined by Altman in [2]: the Safe

zone, in which the Z-score is 3.00; the Grey zones, where the Z-score is 1.8 and2.99; and

the Distress zone where the Z-score 1.79. Figure 2b shows the average Z-score for each year.

5.3 Batch training algorithm

The financial ratios and the discriminant Z-score that are used in MDA now become the input to

the SOM. Then, the hybrid MDA-SOM model is used to predict corporate insolvency. The basic

SOM procedure involves the following steps: (i) construct data setfinancial ratios and discriminant

Z-score; (ii) normalize data; (iii) train the map; (iv) visualize the map; and (v) analyse the results.

The batch training algorithm is used in this process.Batch training algorithm is iterative, but instead of using one single data vector at a time, the

whole data set is presented to the map before any adjustments are made. In each training step, the

data set is partitioned according to the Voronoi regions of the map unit weight vectors. Each data

vector belongs to its closest data set on the map unit. The new weight vectors are calculated as

follows:

wk(t+1)=

nj=1(v,k,t)xjn

j=1(v,k,t)(2)


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The new weight vector is a weighted average of the data samples, in which the weight of each data

sample is the neighbouring function value (v,k,t) at its best matching unit (BMU) v. As in the

sequential training algorithm, the missing value is simply ignored when calculating the weighted

average.

It is very important that the new weight vector is simply the average of the Voronoi data set inthe batch version of the k-means algorithm. The above equation is as follows if (v, k, t)=(v,k).

Alternatively, one can first calculate the sum of the vectors in each Voronoi set:

si(t)=

nvi

j=1

xji (3)

where, nvi is the number of samples in the Voronoi set of unit i. Then, the new values of the weight

vectors may be calculated as follows:

wk(t+1)=

mj=1(v,k,t)sj(t)mj=1 nvj(v,k,t)

(4)

where, m is the number of map units. The batch algorithm is implemented in the following way in

this study.

6 Experimental results

A SOM produces a low-dimensional, discretized representation of the input space of the training

samples. This makes SOMs useful for visualizing low-dimensional views of high-dimensional data.

The U-matrix shows distance between neighbouring units and thus visualizes the cluster structure

of the map (Figure 3). Note that the U-matrix visualization has many more hexagons than the

components. A high value on the U-matrix means a large distance between neighbouring mat units,

and thus indicates cluster borders. Clusters are typically uniform areas of low values.

A surface plot of the distance matrix appears below: both colour and z-coordinates indicate a

denormalized average distance to neighbouring map units. Average distances in the distance matrix

for financial health based on corporate insolvency are shown in Figure 4. These are closely related

to the U-matrix.

The SOM is used for probability density estimation. Each map prototype is the centre of a Gaussian

kernel, the parameters of which are estimated from the data. The Gaussian mixture model is estimated

and the probabilities are calculated. The map grid is in the output space. Three components first

determine the 3D coordinates of the map unit, and the size of the marker is determined by the fourth

component. All values have previously been denormalized. U matrix, Distance matrix, Prototype,

Prototype and data for corporate financial health for 2009 are presented in Figures 36.

The results of this study correctly estimate corporate financial health in Serbia for 95% of com-

panies in 2008 and for 94% of companies in 2009.

These corporate financial health estimations are based on insolvency prediction supported by the

hybrid MDA-SOM model. The result is comparable with studies in other countries: Del Rey (1996)

95% [27]; Crespo (2000) 9395% [14]. However, the results in this research are 7781% better than

in [28].


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FIG. 3. U-matrix for corporate financial health in 2009.

FIG. 4. Distance matrix for corporate financial health in 2009.

7 Conclusion and future development

A hybrid MDA and SOM model for insolvency prediction has been discussed in this article. Financial

ratios and discriminant Z-scores that are used in MDA become the input to the SOM. Then, the hybrid

MDA-SOM model is used to predict the corporate insolvency of companies in Serbia. The results of

this research show correct estimates of the financial health in 95% of all companies in the sample.

Thus, the hybrid MDA-SOM yields very good results. This hybrid applied methodology is not overtly

superior to other techniques, but the approach involves significant advantages. The first advantage

would be the suitability of our research for the analysis of financial ratios. Corporative financial


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FIG. 5. Prototype of corporate financial health in 2009.

FIG. 6. Prototype and data of corporate financial health in 2009.

ratios have significant leverage over the use of a companys independent financial values. Secondly,

MDA can be used for the calculation of Altmans Z-score. This is very important because financial

ratios and Z-score results represent input values for the SOM. Furthermore, the implementation

of the SOM in classification problems is a contemporary technique that represents the outcome of

long-term ANN testing and implementation. It is possible to optimize parameters by considering

a small number of parameters based on another algorithm. In addition to reducing the number of

parameters for optimization, these methods have the potential to control several other properties

such as weight distribution and connection topology [26]. All the above-mentioned characteristics

represent the main technical and technological advantages of the model.Nevertheless, bearing in mind their contribution to the positive predictions obtained in this study,

we should also pay attention to the business, economic and financial reasons which are summarized

below:

(1) The model was discussed and tested in Serbia over a long transition period, from the start of

2000 up until 2010.

(2) Long periods of economic transition produce high numbers of unemployed. This study has

shown that companies that wish to keep up the pace of business development and remain

solvent make between 10 and 20% of their employees redundant on an annual basis. Only 1 out


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of all the 59 companies in the sample increased their workforce, the remainder reduced their

personnel.

(3) During the present global economic crisis, since 2008 to this day, large companies in Serbia

have grown stronger or have retained their competitive advantage over medium-sized and small

companies, and as a result, medium-sized and small companies have suffered greater damage.Large companies came out financially and economically weaker yet they have survived, but

large numbers of small and medium-sized companies are today illiquid, insolvent or bankrupt.

(4) Furthermore, the 10 companies in this research that had recourse to Chapter 7 or 11 of the US

bankruptcy code facilitated the classification of companies and had a significant influence on

the results of this study. They all had large negative Z-scores in the Distress zone, leaving fewer

companies to classify.

This research is far from over and future developments as well as further research will continue

in the area of corporate insolvency prediction models. The hybrid model proposed in this research

improves the implementation of new algorithms and is a step towards new and advanced techniques

for improving company solvency estimates. Further research into the different sectors, such as the

agricultural sector and the agricultural product processing industry can, in the first place, be very

important for a country. Data from the 2010 corporate balance sheets, which will be published in

February 2011, will open up new avenues for research into business activities, company behaviour

and insolvency over longer periods of time.

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Received 4 November 2010

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