Evaluating Bankruptcy Prediction Models in Football: A Thesis

This master thesis investigates the accuracy of bankruptcy prediction models, specifically those of Ohlson (1980), Zmijewski (1984), and Altman (2000), within the Dutch professional football industry between the 2009/2010 and 2013/2014 seasons. The study aims to determine the effectiveness of these models, originally developed for different industries and time periods, in predicting financial distress in Dutch football clubs. The research analyzes financial statements from Dutch football clubs and compares the accuracy rates of the three models, finding that Zmijewski’s probit model (1980) performed most accurately. The thesis also highlights the financial challenges within the Dutch professional football industry, noting liquidity, profitability, and leverage issues faced by many clubs, suggesting a high risk of financial distress based on the bankruptcy prediction models.

September 2015
MASTER THESIS
BUSINESS ADMINISTRATION – FINANCIAL MANAGEMENT
ACCURACY RATE OF
BANKRUPTCY PREDICTION
MODELS FOR THE DUTCH
PROFESSIONAL FOOTBALL
INDUSTRY
PATRICK GERRITSEN
S0118869
UNIVERSITY OF TWENTE, THE NETHERLANDS
1st SUPERVISOR: prof. dr. R. KABIR
2nd SUPERVISOR: dr. X. HUANG

I
MANAGEMENT SUMMARY
Bankruptcy and financial distress are chronicle problems for the Dutch professional football
industry. Since the establishment of Dutch’s professional football in 1954 nine clubs have been
declared bankrupt (four since 2010) and many others were facing financial distress last few years.
Club failure identification and early warnings of impending financial crisis could be very important
for the Dutch football association in order to maintain a sound industry and to prevent competition
disorder. As financial ratios are key indicators of a business performance, different bankruptcy
prediction models have been developed to forecast the likelihood of bankruptcy. Because bankruptcy
prediction models are based on specific industries, samples and periods it remains a challenge to
predict with a high accuracy rate in other settings. Therefore, the aim of this study is to assess the
accuracy rate of bankruptcy prediction models to an industry and period outside those of the original
studies namely, the Dutch professional football industry.The study draws on the information from
financial statements (e.g. annual report and season reports) as publicly provided by the Dutch
professional football clubs since 2010. The accuracy rate of three best suitable (i.e. commonly used
and applicable to the Dutch football industry) accounting-based bankruptcy prediction models of
Ohlson (1980), Zmijewski (1984), and Altman (2000) were tested on Dutch professional football
clubs between the seasons of 2009/2010 - 2013/2014. The sample size on the Dutch professional
football industry throughout the different seasons fluctuates between 30 and 36 depending on the
available data in a particular season. The study assumed that there is no difference in accuracy rate
between the three accounting-based bankruptcy prediction models. Alternatively is assumed that the
Z” model of Altman (2000) will outperform the other models and hereby follows the studies of
Vazquez (2012) and Barajas & Rodríguez (2014) who claim that the Z” model is the best choice for
football clubs. The accuracy rates for the Dutch professional football industry on Ohlson (1980),
Zmijewski (1984) and Altman (2000) are depending on the prediction time frame between 17% and
19% (Ohlson), 61% and 66% (Zmijewski), 38% and 49% (Altman Z’), and 23% and 26% (Altman
Z”). Overall, Zmijewski’s probit model (1980) performed most accurate on the Dutch professional
football industry within the five seasons of investigation. This implies that Zmijewski’s model is the
best predictor for bankruptcy likelihood for the Dutch professional football industry. However, the
accuracy rates are quite low and therefore should be set into perspective and studied cautiously.
Furthermore this study shows that the Dutch professional football industryhas some huge financial
problems. The majority of the clubs have liquidity, profitability and leverage problems and are based
on the results of the different bankruptcy prediction models facing bankruptcy since they are having
financial distress.

II
LIST OF ABBREVIATIONS
Table 1. List of abbreviations used in this master thesis
Abbreviation Written entirely Description and/or English Translation
AGOVV Alleen Gezamenlijk Oefenen Voert Verder Only Exercising Together Performs Further
AMM Amortization Amortization of a club’s intangible assets
BV Betaald Voetbal Professional Football
BE/TL Book Value Equity / Total Liabilities Leverage ratio
CHIN Change Net Income = (NIt - Nit-1) / (NIt + Nit-1), where t is the year
CA/CL Current Assets / Current Liabilities Liquidity ratio
CL/CA Current Liabilities / Current Assets Liquidity ratio
DEP Depreciation Depreciation of a club’s tangible assets
EBIT/TA Earnings Before Interest and Taxes / Total Assets Profitability ratio
FC Football Club -
FU/TL Funds from Operations / Total Liabilities FU = NI + DEP + AM - GSP, Liquidity ratio
FRS Financial Rating System Rating system developed by the KNVB in 2010
GSP Gains on Sales of Property -
HFC Haarlemsche Football Club Name of Dutch football club
INTWO INTWO 1 If NI was negative for the last 2 years, 0 otherwise
KNVB Koninklijke Nederlandse Voetbal Bond Royal Dutch Football Association
MDA Multiple Discriminant Analysis Statistical method
MVE/TL Market Value Equity/Total Liabilities Leverage ratio
N.A. Not Available -
NI/TA Net Income / Total Assets Profitability ratio
NI/TL Net Income / Total Liabilities Profitability ratio
NWC/TA Net Working Capital / Total Assets Operating liquidity ratio
OENEG OENEG = 1 If TL > TA , 0 otherwise
OSIZE Ohlsen Size = LOG(total assets/GNP price-level index)
RBC Roosendaalse Boys Combinatie Roosendaalse Boys Combination
RE/TA Retained Earnings / Total Assets Profitability ratio (RE = net profit – dividends,
where dividends in Dutch football are null)
RFS Russia Football Union -
SALES/TA Sales / Total Assets Profitability ratio
TL/TA Total Liabilities / Total Assets Profitability ratio
UEFA Union of European Football Associations Leverage ratio
WC/TA Working Capital / Total Assets Liquidity ratio

TABLE OF CONTENT
MANAGEMENT SUMMARY ............................................................................................................. I
LIST OF ABBREVIATIONS ............................................................................................................... II
1. INTRODUCTION ......................................................................................................................... 1
1.1 Dutch Professional Football Industry and Financial Issues .................................................... 4
1.2 Problem Statement .................................................................................................................. 5
1.3 Objective ................................................................................................................................. 5
1.4 Research Questions ................................................................................................................. 6
1.5 Contribution and Justification ................................................................................................. 7
1.5.1 Theoretical Contribution .................................................................................................. 7
1.5.2 Practical Contribution ...................................................................................................... 7
1.5.3 Justification ...................................................................................................................... 7
2. LITERATURE REVIEW .............................................................................................................. 8
2.1 Terminology and Definitions .................................................................................................. 8
2.1.1 Default, Failure, Insolvency and Bankruptcy .................................................................. 8
2.1.2 Financial Distress and Bankruptcy Prediction ................................................................. 9
2.2 Bankruptcy Prediction Models .............................................................................................. 10
2.3 Accounting-based Bankruptcy Prediction Models ................................................................ 12
2.3.1 Altman’s Z-score Model (1968) .................................................................................... 12
2.3.2 Ohlson’s O-score Model (1980) ................................................................................... 16
2.3.3 Zmiejewski’s Model (1984) .......................................................................................... 18
2.3.4 Conclusion Accounting-based Bankruptcy Prediction Models ..................................... 20
2.4 Market-based Bankruptcy Prediction Models ....................................................................... 20
2.4.1 Shumway’s Hazard Model (2001) ................................................................................. 21
2.4.2 Hillegeist et al’s BSM-prop model (2004)..................................................................... 22
2.5 Comparing Accounting-based and Market-based Bankruptcy Prediction Models ............... 22
2.6 Conclusion Literature Review ............................................................................................... 24

2.7 Derivation of Hypotheses ...................................................................................................... 25
3. METHODOLOGY AND DATA ................................................................................................. 28
3.1 Sample Selection and Data .................................................................................................... 28
3.2 Research Methodology .......................................................................................................... 29
3.3 Selected Research Tools........................................................................................................ 31
4. EMPIRICAL RESULTS .............................................................................................................. 35
4.1 Descriptive Statistics ............................................................................................................. 35
4.2 Analysis of the Bankruptcy Prediction Models..................................................................... 38
4.2.1 Analysis Altman’s (2000) models ................................................................................. 40
4.2.2 Analysis Ohlson (1980 model) ...................................................................................... 42
4.2.3 Analysis Zmijewski (1984 model) ................................................................................. 42
4.2.4 Comparison with prior studies ....................................................................................... 43
4.3 Hypotheses and Discussion ................................................................................................... 45
4.3.1 Testing hypotheses ......................................................................................................... 45
5. DISCUSSION AND CONCLUSION ......................................................................................... 48
5.1 Discussion ............................................................................................................................. 48
5.2 Conclusion ............................................................................................................................. 49
5.2.1 Limitations ..................................................................................................................... 50
5.2.2 Suggestions for Future Research ................................................................................... 51
6. REFERENCES ............................................................................................................................ 52
Appendix I – Overview Key Bankruptcy Prediction Models .............................................................. 58
Appendix II – KNVB’S FRS-MODEL REVIEWED .......................................................................... 59
Appendix III – EXTRA DESCRIPTIVE STATISTICS ...................................................................... 61

1
1. INTRODUCTION
This chapter starts with an introduction and some necessary background information of the Dutch
professional football industry. Next, a problem statement follows that lead up to the objective and
research questions of the study. The chapter ends with the contribution and justification of the thesis.
Bankruptcy and financial distress are chronicle problems in the global professional football industry,
one of the world’s most popular sport.1 Recently internationally well know professional football
clubs such as England’s Portsmouth in 2010, Scotland’s Glasgow Rangers in 2012, and Italian’s
Parma FC in 2015 have been declared bankrupt. It is striking to see that the study of A.T. Kearny 2
(2010) about the top football leagues in Europe shows that, when running as normal companies, the
top leagues in England, Spain and Italy would be bankrupt within two years. Two years later in 2012
one can conclude that this did not actually happen because football clubs are not running as normal
companies and seem to have their own set of rules regarding bankruptcy. Still there is an
unquestionable financial problem in European football, Szymanski (2012) underlined that sixty-six
English professional football clubs have been involved in insolvency proceedings during the period
1982-2010. The evidence in the study of Barajas & Rodriquez (2014) suggests that Spanish football
is in very poor financial condition and that an injection for more than €900 Mill. in total is required
as a financial health therapy for a sound Spanish football industry. This is in line with previous
studies within Spanish football of García & Rodríguez (2003), Boscá, Liern, Martínez & Sala (2008)
and Barajas & Rodríguez (2010) who all assert that the economic situation of Spanish football clubs
presents an important fragility. According to the study of Syzmanski (2010), in Spain, most clubs
have significant debt exposure, only Real Madrid and FC Barcelona have real financial strength, and
the rest of the clubs struggle to compete. For Spain’s neighbor Portugal this isn’t much different.
According to Mourao (2012) most Portuguese football teams had increased their debt ratios during
the previous two decades. But also other professional football clubs all over world face similar
problems. Russia’s Football Union (RFS) has financial problems due to the collapse of their
1 Generally known as ‘football’ in most of the world, but also often referred to as ‘soccer’, especially in North America.
Not to be confused with American football which is a complete different ballgame.
2 A.T. Kearny is a global management consulting firm that focuses on strategic and operational CEO-agenda issues
facing, business, governments and institutions around the globe.

2
monetary unit the Russian Ruble, and according to the NOS 3, the debt of the football clubs in Brazil
is so high that eight of the twelve clubs barely can pay their taxes and salaries. The UEFA 4
acknowledged the financial problems of the football industry in one of their reports called UEFA
Club Licensing Report (2012). According to this report 56% of European clubs participating at the
highest level of national competition were loss-making in 2010 and 36% reported negative net
equity. In order to prevent professional football spending more than they earn, often in the pursuit of
success and in doing so getting into financial problems, which might threaten their long-term
survival, UEFA started the UEFA Financial Fair Play Regulations program in 2011.
In the top division of Dutch football ‘the Eredivisie’ none of the clubs have ever been declared
bankrupt while they were playing in the highest division. Bankruptcy is more common for clubs that
play in the second highest and also the lowest Dutch professional football division ‘the Jupiler
League’. Since the establishment of Dutch’s professional football in 1954 nine clubs participating in
the second highest division have been declared bankrupt, of which four since 2010. When one keeps
in mind that the average amount of clubs playing in one of the two professional divisions was thirty-
eight last decade, four since 2010 (more than 10%) is quite a striking number. Because of the
competitive nature of Dutch football with the possibility to promote and relegate it can and does
happen that a club which has been declared bankrupt has a recent history in the highest division.
Financial distress however is something that both first and second highest division clubs faced now
and then, especially since the last decade.
To maintain a sound Dutch professional football industry the Royal Dutch Football Association5
made together with the clubs an agreement in 2010 to communicate in a transparent way about the
financial situation of Dutch professional football industry and the individual clubs. Since this
agreement clubs are forced to make their financial statements publicly available. An important part
of this transparency is the publicly announcement of the category-division by the KNVB6. This
category-division is based on the financial information from annual reports of the clubs. These,
3 NOS is the abbreviation of Nederlandse Omroep Stichting, which is the Dutch translation of Dutch Broadcast
Foundation. It has a special statutory obligation to make news and sports programmes for the three Dutch public
television channels and the Dutch public radio services. See references for exact source.
4 UEFA is the governing body of European football (Union of European Football Associations – UEFA).
5 The Royal Dutch Football Association is the governing body of football in the Netherlands. It organizes the main Dutch
football leagues.
6 KNVB is the abbreviation of Koninklijke Nederlandse Voetbalbond, which is the Dutch translation of Royal Dutch
Football Association.

3
mostly financial figures, are filled in into a model called the Financial Rating System7, which is
developed by the KNVB in 2010. The individual score of a club will put them in one of the category-
divisions. The division consists of three different categories: category I (insufficient), category II
(sufficient) and category III (good). Every year there are several clubs categorized in the insufficient
category I, which means that a club is likely to head to financial distress and that it needs to work on
financial recovery. The recovery is at the clubs own responsibility and they need to develop a plan of
approach that has the goal to belong to category II or III on a structural bases. The clubs are
supposed to stick strictly to this plan to avoid sanctions of the KNVB. Sanctions could be warnings,
money fines or deduction of league points. The KNVB strives to get all the club at least in category
II within the upcoming years. This is to provide an early warning, for monitoring to avoid
bankruptcy and to maintain a sound industry.
When bankruptcy occurs it has an effect on the followers and supporters of a professional football
club. A ‘die hard’ supporter for example will feel robbed from their love for a club or his/her hobby.
Besides this it has also an effect on the league’s ranking, since in cases of bankruptcy it might
happen that all previous matches of the concerning club during that particular season are counted as
non-played games. This will cause a competition distortion. Business failure identification and early
warnings of impending financial crisis are important to analysts, practitioners, the suppliers of
capital, investors, creditors, management, employees, auditors and in case of the professional
football industry the concerning football association, since these parties are all severely affected by
business failures (Deakin, 1972; Charitou, Neophytou, & Charalambous, 2004). The demand to
predict financial problems such like bankruptcy and financial distress has led to the development of
several bankruptcy prediction models to forecast the likelihood of it. Two approaches; accounting-
based bankruptcy prediction models and market-based bankruptcy prediction models, imply different
views of a club/firm and use financial ratios to estimate the possibility of bankruptcy or financial
distress. Because bankruptcy prediction models are based on specific industries, samples and periods
it remains a challenge to predict with a high accuracy rate in other settings. This master thesis draws
on the information from financial statements (e.g. annual reports, season reports) as publicly
provided by the Dutch professional football clubs since 2010. The goal is to assess the accuracy rate
of the best suitable (i.e. commonly used and applicable to the Dutch football industry) bankruptcy
prediction models for the Dutch professional football industry.
7 An explanation and example of the Financial Rating System (FRS-model) is shown at appendix II.

4
1.1 Dutch Professional Football Industry and Financial Issues
The Dutch professional football industry, like any other professional football industry, is an industry
that relies on money from ticket sales, merchandising, broadcast income (demand), sponsorships and
extreme wealthy business people (Szymanski, 2012). The income of a professional football club is
dependent on each of the above mentioned variables. However Szymanski (2010) claims that the
impact of the economic cycle on the professional football clubs is limited, there are still a lot of
things that could happen that influence the income of a club in a negative way. So can there be for
example negative productivity shocks to the investment-performance relationship (bad luck on the
field) or negative demand shocks to the performance-revenue relationship (Szymanski, 2012). The
investment-performance relationship in this case is the relation between the amount of money which
is invested in the player squad (player budget) and the performance on the field which is measured
by the amount of league points or league ranking. Generally the higher the player budget the higher
the position on the league’s ranking. The performance-revenue relationship in this case is the relation
between the performance of a club (position league ranking) and a club’s revenue. Generally the
higher the position on the league’s ranking the higher the revenue (prize money, more sponsors,
more sold tickets etc.).
Furthermore Szymanski (2012, p. 16) found in his study about English professional football clubs
that “negative shocks to productivity or to demand cause wage expenditure to rise relative to income,
a deteriorating balance sheet and a higher probability of insolvency”. Bankruptcy and financial
distress have shocked the Dutch professional football industry several times the last few years. BV
Veendam was in 2013 the 9th Dutch professional football club which has been declared bankrupt
since the establishment of Dutch’s professional football in 1954. Four of these bankruptcies occurred
since 2010. These were HFC Haarlem in 2010, RBC Roosendaal in 2011, AGOVV in 2013 and BV
Veendam in 2013. So far all Dutch professional football clubs which went bankrupt acted in the
second highest football division which is called ‘Jupiler League’. But also the premier league of
Dutch football which is called, ‘Eredivisie’ had some unexpected cases of financial distress within
their league. Last decade a number of teams playing in both leagues have had financial problems, but
they all have been bailed out by local government or local businesses8. Latest case is the weak
financial position of FC Twente which has to cope with extreme financial distress at the moment,
only five years after their first ‘Eredivisie’ championship in 2010. At the moment the club is upheld
by wealthy local business people who lend FC Twente money to pay short term debts. Their
8 Among others; FC Emmen in 2012 and 2013, FC Twente in 2003, Feyenoord in 2005 and 2010, NAC Breda in 2003,
2011 and 2013, and RKC Waalwijk in 2009, 2014 and 2015.

5
financial distress has led to a deduction of minus six points for FC Twente in the ‘Eredivisie’ leagues
performance ranking of 2014/2015. This penalty (e.g. deduction of points) was the result of a
violation of the rules from the Financial Rating System as drafted by the KNVB in 2010.
Unfortunately FC Twente is not the only club who is facing financial distress last decade. Every year
there are several clubs categorized by the KNVB in the insufficient category I which means that a
club is likely to head to financial distress and that it needs to work on financial recovery. In chapter
3.1.1. the FRS-model of the KNVB will be elaborated.
1.2 Problem Statement
As mentioned in the introduction, four Dutch professional football clubs went bankrupt since 2010.
These bankruptcies are a major concern for the stakeholders of the organization, the supporters, the
employers and the Dutch football association. Every bankruptcy or moment of financial distress of a
Dutch professional football club is quite a shock for the whole Dutch professional football industry.
The likelihood of financial bankruptcy can be predicted in order to take appropriate actions before an
actual bankruptcy takes place. In literature several models have been developed to predict cases of
potential bankruptcy. Different bankruptcy prediction models that are able to forecast business
failure have been developed after Beaver´s pioneering work in 1966. The problem with those
bankruptcy prediction models is that they have been developed with another methodology and are
dated. Some common used bankruptcy prediction models are even more than forty years old. Since
the accuracy and structure of the models change over periods of time and when the setting of the
study differs (e.g. country, industry, etc.) from the original methodology, it is likely that the accuracy
rate of the bankruptcy prediction models change as well (Grice & Dugan, 2003). Furthermore none
of the found studies have performed a research about the accuracy rate of the bankruptcy prediction
models for a professional football industry. Therefore the professional football industry of the
Netherlands might be a good place to start.
1.3 Objective
The objective of this master thesis is to assess the accuracy rate of bankruptcy prediction models for
the Dutch professional football industry. This objective is achieved by comparing the results of each
club according to the different bankruptcy prediction models to the FRS based category-division of
the KNVB from t +1, t+2 and t+3. The goal is to find out if there are differences between the different
bankruptcy prediction models in order to track down which bankruptcy prediction model performs
best for the Dutch professional football industry.

6
1.4 Research Questions
The focus of this study will be on the best suitable (i.e. can be used in the Dutch football industry)
bankruptcy prediction models. In order to assess the performance of these bankruptcy prediction
models, finding out which one to use and measuring the accuracy rate of them is crucial. The higher
the accuracy rate of a model, the less error it will have. Less error also means that the predictive
power of a certain model is better or worse than the other. This underlying problem lead to the
following research question and sub-questions:
What is the accuracy rate of bankruptcy prediction models for the Dutch professional football
industry?
Accompanying sub-questions are formulated in order to answer the research question and eventually
reach the research goal:
1. Which bankruptcy prediction models exist in literature?
2. Which bankruptcy prediction models can be used for the football industry?
3. What is the Financial Rating System of the KNVB?
4. What is the accuracy rate of the different bankruptcy prediction models?

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1.5 Contribution and Justification
1.5.1 Theoretical Contribution
Numerous studies have been conducted to analyze bankruptcy prediction models since the
development of Beaver’s (1966) pioneering work. Examples are among others 9 the study of Oude
Avenhuis (2010), Wu et al. (2010) and Bae (2012). The focus of those studies differentiates from
firm characteristics (e.g. legal status and firm size) to particular industries and countries. None of the
found studies have conducted a research concerning bankruptcy prediction models and a professional
football industry. Only the study of Barajas & Rodriquez (2014) used Altman’s models to classify
Spanish professional clubs according to their Z-score values, but they did not assess the accuracy rate
of the used models. Therefore this research contributes to the literature because the accuracy rate of
bankruptcy prediction models for the Dutch football industry is assessed.
1.5.2 Practical Contribution
It will be interesting to see if the (Dutch) professional football industry is comparable with other
industries and if the bankruptcy prediction models give justice to this industry. This may help the
KNVB and other similar football associations to discover future ‘problem’ clubs at an earlier stage.
The better bankruptcy or financial distress can be predicted the less damage one of the occasions will
cause to all the interested parties of the football industry.
1.5.3 Justification
The topic of this master thesis “Accuracy rate of bankruptcy prediction models for the Dutch
professional football industry” was chosen because of the personal experience in the world of Dutch
professional football and interest in the field of bankruptcy prediction of the researcher. The financial
data for this industry before 2010 is very limited. This because since 2010 it became obliged for
Dutch professional football clubs to make their financial statements publicly available. Therefore the
timeline of this research is set from the seasons of 2009/2010 until 2013/2014. The most commonly
used and most cited account-based bankruptcy prediction models have been selected to conduct this
research. This because AFC Ajax is the only publicly listed Dutch professional football club which
means that the market-based models are not applicable for the Dutch professional football industry
due to a lack of market data of all the other clubs.
9 Among others; Pongsatat et al. (2004), Canbaş et al. (2006), Gang & Xiaomao (2009) Kumar & Kumar (2012), Strand
(2013), and (Kleinert, 2014)