Research Methodology

The research question of this master thesis is “What is the accuracy rate of bankruptcy prediction models for the Dutch professional football industry?” The accuracy rate is the percentage of correct classifications (bankrupt or non- bankrupt) to the total classification. Well-known researchers such as Altman (1968), Ohlson (1980) and Zmiejwski (1984) do it similarly. A club is defined as bankrupt if it has been removed from the competition and lost its license; this implies a declaration of bankruptcy by court order. Clubs that meet these requirements are easy to find in the news and the leagues rankings. The criteria for non-bankrupt clubs are obviously Dutch professional football clubs which are still in competition. The criteria for financial distressed clubs and healthy clubs are determined by the category-division (classification) according to the FRS-model as founded by the KNVB in 201020. To be able to compare all the models, the three categories which are included in the FRS-model will be re-encoded into two categories; financial distressed, and safe (i.e. sufficient and good). For the sake of this research the assumption is made that this ‘FRS classification’ is always correct. Of each of the studied years (seasons 2009/2010 until 2013/2014) will be examined if a particular club is classified into the right group according to the results of the different models. This will be done by comparing these results to their category-division (classification) according to the FRS-model. The classification periods of t+1, t+2 and t+3 will be used to compare the bankruptcy

prediction models to the financial state a club is in according to this FRS-model. For example; for the accounting data of season 2009/2010 the comparing category-division (classification) according to the FRS-model of one year later is t+1, two years later is t+2, three years later is t+3 . This time frame is

set because the literature of the selected bankruptcy prediction models claim that they perform best one year, two, and three years in advance.21

The research question will be answered by the outcome of the empirical results. The accompanying sub-questions will be answered by the conclusion of the literature review. The total population of this study is small and consists of thirty-eight clubs; therefore the full sample of all the available clubs will be used. Furthermore the full sample includes only three22 bankrupt clubs. One of these clubs went bankrupt in 2011 and two in 2013. Due to the fact the sample of bankrupt clubs is very small per year, these clubs will be tested and included in the full sample and will be classified as bankrupt/financially distressed in that particular year.

20 _{In appendix II. the FRS-model is explained extensively.} 21

Among others; Altman (1968, 2000), Ohlson (1980), Zmijevski (1984), Anjum (2012), and Hussain et al. (2014)

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This means that other than similar research, this study will use the full-sample instead of a holdout sample with an estimation. The number of data points is small enough to use the full-sample design and on the small side to use for a hold-out sample design. Furthermore a full-sample design will provide the better classifier according to Brun et al., 2007. Moreover the goal of this study is simply assessing the accuracy rate of models and finding the best suitable bankruptcy prediction models for the Dutch professional football. To achieve this goal it is sufficient to compare the results of the different models and prediction time frames by accuracy rate. The first statistical test that this study will use is an ANOVA test including a test of homogeneity of variances (i.e. Levene’s test). Levene’s test is often used before a comparison of means. It assumes that all groups have the same or similar variance. A p value less than .05 indicates a violation of this assumption which simply means that the groups are not comparable. When this significance occurs, one should switch to more generalized tests that are free from homoscedasticity assumptions such as non-parametric tests (Levene’s, 1960). If the groups are comparable an analysis of variance (two-way ANOVA) will be performed to analyze whether there are differences between or within the models (groups) by comparing their mean scores. When there is a difference in the accuracy rate between the used bankruptcy prediction models another test called multi comparisons post-hoc analysis two-way ANOVA (Bonferroni) test will be used as the statistical test that will show how the models differ and which one performs best. Altough it is unusual to use this kind of ANOVA testing in this reseach field, performing such test after calculating the accuracy rates for the different models, allows to compare the results of the three models (by their mean scores) for multiple predicting time frames. In this way it is possible to make inferences about the performances of the models with statistical evidence. Moreover studies from other fields with similar goals (i.e. comparing mean scores or assessing the best suitable model) do it similarly (e.g. Godfrey, 1985, Chellapilla et al, 2005 and, Kovatchev et al, 2008).