Insolvency in Brazilian Football Clubs: Proposition of Models Based on Neural Networks

Minatto, Fábio; Borba, José Alonso

doi:10.15728/bbr.2021.18.6.2

Fábio Minatto

Universidade Federal de Santa Catarina, Florianópolis, SC, Brasil

http://orcid.org/0000-0003-4004-4086

José Alonso Borba

Universidade Federal de Santa Catarina, Florianópolis, SC, Brasil

http://orcid.org/0000-0001-6068-342X

fabio_minatto@hotmail.com

jalonsoborba@hotmail.com

AUTHORS’ CONTRIBUTION

First Author: Contextualization, Method, Results, analyses, concluding remarks. Second Author: Problem and Objective definition, literature review, and support in applying the method.

CONFLICTS OF INTEREST

The authors declare that there is no conflict of interest.

Rank	Club	Rank	Club	Rank	Club
1	Palmeiras	13	Fluminense	25	Atlético Goianiense
2	Cruzeiro	14	Vasco da Gama	26	Paraná
3	Grêmio	15	Bahia	27	Paysandu
4	Santos	16	Sport	28	Santa Cruz
5	Corinthians	17	Vitória	29	Criciúma
6	Flamengo	18	Ponte Preta	31	Juventude
7	Atlético Mineiro	19	América Mineiro	34	Vila Nova
8	Athletico Paranaense	20	Coritiba	36	Náutico
9	Internacional	21	Avaí	39	Joinville
10	Chapecoense	22	Figueirense	41	Brasil de Pelotas
11	Botafogo	23	Ceará	44	Guarani
12	São Paulo	24	Goiás

N.	Indicator	Operationalization	Thoeretical Background
I1	Immediate Liquidity	$\frac{C a s h a n d E q u i v a l e n t s}{C u r r e n t L i a b i l i t i e s}$	(Ecer & Boyukaslan, 2014Ecer, F., & Boyukaslan, A. (2014). Measuring Performances of Football Clubs Using Financial Ratios: The Gray Relational Analysis Approach. American Journal of Economics, 4(1), 62-71. https://doi.org/10.5923/j.economics.20140401.06 https://doi.org/10.5923/j.economics.2014... )
I2	Current Ratio	$\frac{C u r r e n t A s s e t s}{C u r r e n t L i a b i l i t i e s}$	(Alaminos & Fernández, 2019Alaminos, D., & Fernández, M. Á. (2019). Why do football clubs fail financially? A financial distress prediction model for European professional football industry. PLOS ONE, 14(12), e0225989. https://doi.org/10.1371/journal.pone.0225989 https://doi.org/10.1371/journal.pone.022... )
I3	Net Working Capital	$\frac{C u r r e n t A s s e t s - C u r r e n t L i a b i l i t i e s}{T o t a l A s s e t s}$	(Alaminos & Fernández, 2019Alaminos, D., & Fernández, M. Á. (2019). Why do football clubs fail financially? A financial distress prediction model for European professional football industry. PLOS ONE, 14(12), e0225989. https://doi.org/10.1371/journal.pone.0225989 https://doi.org/10.1371/journal.pone.022... )
I4	General Liquidity	$\frac{C u r r e n t A s s e t s + L o n g T e r m A s s e t s}{C u r r e n t L i a b i l i t i e s + L o n g T e r m L i a b i l i t i e s}$	(Kanitz, 1976Kanitz, S. C. (1976). Indicadores contábeis financeiros - previsão de insolvência: a experiência da pequena e média empresa brasileira. Universidade de São Paulo.)^*
I5	Asset Composition	$\frac{C u r r e n t A s s e t s}{T o t a l A s s e t s}$	(Gutiérrez-Fernández, Talavero-Álvarez, & Coca-Pérez, 2017Gutiérrez-Fernández, M., Talavero-Álvarez, F. J., & Coca-Pérez, J. L. (2017). Economic and Financial Analysis of Bankruptcy of Football Teams. In M. Peris-Ortiz, J. Álvarez-García, & M. de la C. Del Río-Rama (Eds.), Sports Management as an Emerging Economic Activity (pp. 155-182). https://doi.org/10.1007/978-3-319-63907-9_10 https://doi.org/10.1007/978-3-319-63907-... )
I6	Fixed to Total Assets	$\frac{F i x e d A s s e t s}{T o t a l A s s e t s}$	(Alaminos & Fernández, 2019Alaminos, D., & Fernández, M. Á. (2019). Why do football clubs fail financially? A financial distress prediction model for European professional football industry. PLOS ONE, 14(12), e0225989. https://doi.org/10.1371/journal.pone.0225989 https://doi.org/10.1371/journal.pone.022... )^**
I7	Intangible to Total Assets	$\frac{I n t a n g i b l e A s s e t s}{T o t a l A s s e t s}$	(Guo, Kubick & Masli, 2018Guo, F., Kubick, T. R., & Masli, A. (2018). The effects of restatements for misreporting on auditor scrutiny of peer firms. Accounting Horizons, 32(1), 65-85. https://doi.org/10.2308/acch-51934 https://doi.org/10.2308/acch-51934... )^*
I8	Total Debt	$\frac{S h o r t a n d L o n g T e r m L o a n s}{T o t a l A s s e t s}$	(Alaminos & Fernández, 2019Alaminos, D., & Fernández, M. Á. (2019). Why do football clubs fail financially? A financial distress prediction model for European professional football industry. PLOS ONE, 14(12), e0225989. https://doi.org/10.1371/journal.pone.0225989 https://doi.org/10.1371/journal.pone.022... )
I9	Debt Composition	$\frac{C u r r e n t L i a b i l i t i e s}{C u r r e n t L i a b i l i t i e s + L o n g T e r m L i a b i l i t i e s}$	(Martins, Diniz, & Miranda, 2017Martins, E., Diniz, J. A., & Miranda, G. J. (2017). Análise avançada das demonstrações contábeis: Uma abordagem crítica. Atlas.)^*
I10	Net Debt	$\frac{N e t D e b t}{T o t a l R e v e n u e}$	(Szymanski, 2017Szymanski, S. (2017). Entry into exit: insolvency in English professional football. Scottish Journal of Political Economy, 64(4), 419-444. https://doi.org/10.1111/sjpe.12134 https://doi.org/10.1111/sjpe.12134... )
I11	Asset Turnover	$\frac{T o t a l R e v e n u e}{T o t a l A s s e t s}$	(Alaminos & Fernández, 2019Alaminos, D., & Fernández, M. Á. (2019). Why do football clubs fail financially? A financial distress prediction model for European professional football industry. PLOS ONE, 14(12), e0225989. https://doi.org/10.1371/journal.pone.0225989 https://doi.org/10.1371/journal.pone.022... ; Ecer & Boyukaslan, 2014Ecer, F., & Boyukaslan, A. (2014). Measuring Performances of Football Clubs Using Financial Ratios: The Gray Relational Analysis Approach. American Journal of Economics, 4(1), 62-71. https://doi.org/10.5923/j.economics.20140401.06 https://doi.org/10.5923/j.economics.2014... )
I12	Return on Assets	$\frac{N e t I n c o m e}{T o t a l A s s e t s}$	(Ecer & Boyukaslan, 2014Ecer, F., & Boyukaslan, A. (2014). Measuring Performances of Football Clubs Using Financial Ratios: The Gray Relational Analysis Approach. American Journal of Economics, 4(1), 62-71. https://doi.org/10.5923/j.economics.20140401.06 https://doi.org/10.5923/j.economics.2014... )
I13	Net Margin	$\frac{N e t I n c o m e}{T o t a l R e v e n u e}$	(Plumley, Wilson, & Ramchandani, 2017Plumley, D., Wilson, R., & Ramchandani, G. (2017). Towards a model for measuring holistic performance of professional Football clubs. Soccer & Society, 18(1), 16-29. https://doi.org/10.1080/14660970.2014.980737 https://doi.org/10.1080/14660970.2014.98... ; R. Wilson, Plumley, & Ramchandani, 2013Wilson, R., Plumley, D., & Ramchandani, G. (2013). The relationship between ownership structure and club performance in the English Premier League. Sport, Business and Management: An International Journal, 3(1), 19-36. https://doi.org/10.1108/20426781311316889 https://doi.org/10.1108/2042678131131688... )
I14	EBIT to Total Revenue	$\frac{E B I T}{T o t a l R e v e n u e}$	(Alaminos & Fernández, 2019Alaminos, D., & Fernández, M. Á. (2019). Why do football clubs fail financially? A financial distress prediction model for European professional football industry. PLOS ONE, 14(12), e0225989. https://doi.org/10.1371/journal.pone.0225989 https://doi.org/10.1371/journal.pone.022... )

N.	Indicator	Operationalization	Thoeretical Background
E1	Szymanski and Smith (1997Szymanski, S., & Smith, R. (1997). The English football industry: Profit, performance and industrial structure. International Review of Applied Economics, 11(1), 135-153. https://doi.org/10.1080/02692179700000008 https://doi.org/10.1080/0269217970000000... ) Indicator	The indicator formulated in the study by Szymanski and Smith (1997Szymanski, S., & Smith, R. (1997). The English football industry: Profit, performance and industrial structure. International Review of Applied Economics, 11(1), 135-153. https://doi.org/10.1080/02692179700000008 https://doi.org/10.1080/0269217970000000... ) is used.	(Alaminos & Fernández, 2019Alaminos, D., & Fernández, M. Á. (2019). Why do football clubs fail financially? A financial distress prediction model for European professional football industry. PLOS ONE, 14(12), e0225989. https://doi.org/10.1371/journal.pone.0225989 https://doi.org/10.1371/journal.pone.022... ; Szymanski, 2017Szymanski, S. (2017). Entry into exit: insolvency in English professional football. Scottish Journal of Political Economy, 64(4), 419-444. https://doi.org/10.1111/sjpe.12134 https://doi.org/10.1111/sjpe.12134... ; Szymanski & Smith, 1997Szymanski, S., & Smith, R. (1997). The English football industry: Profit, performance and industrial structure. International Review of Applied Economics, 11(1), 135-153. https://doi.org/10.1080/02692179700000008 https://doi.org/10.1080/0269217970000000... )
E2	Brazilian Championship Position	Position in the Brazilian Championship. The position of the 20 clubs that played in the first division varies between 1 and 20, of the 20 clubs that played in the second division varies between 21 and 40, of the 20 clubs that played in the second division varies between 41 and 60 and of the 20 clubs that played in the third division, and fourth division ranges from 61 to 128.	(Scelles et al., 2018Scelles, N., Szymanski, S., & Dermit-Richard, N. (2018). Insolvency in French soccer: the case of payment failure. Journal of Sports Economics, 19(5), 603-624. https://doi.org/10.1177/1527002516674510 https://doi.org/10.1177/1527002516674510... )
E3	Earned points percentual	Represents the percentage of points won by the club in the Brazilian Championship. The victory of a match represents 3 points, the tie 1 point and the defeat have no score. The indicator is calculated from the points earned divided by the possible number of points earned.	(Plumley, Wilson, & Ramchandani, 2017Plumley, D., Wilson, R., & Ramchandani, G. (2017). Towards a model for measuring holistic performance of professional Football clubs. Soccer & Society, 18(1), 16-29. https://doi.org/10.1080/14660970.2014.980737 https://doi.org/10.1080/14660970.2014.98... )
E4	Division	Dummy variable to reflect the effects of disputing lower divisions.	(Alaminos & Fernández, 2019Alaminos, D., & Fernández, M. Á. (2019). Why do football clubs fail financially? A financial distress prediction model for European professional football industry. PLOS ONE, 14(12), e0225989. https://doi.org/10.1371/journal.pone.0225989 https://doi.org/10.1371/journal.pone.022... ; Dantas et al., 2015Dantas, M. G. da S., Machado, M. A. V., & Macedo, M. A. da S. (2015). FATORES DETERMINANTES DA EFICIÊNCIA DOS CLUBES DE FUTEBOL DO BRASIL. Advances in Scientific and Applied Accounting, 8(1), 113-132. https://doi.org/10.14392/asaa.2015080106 https://doi.org/10.14392/asaa.2015080106... )
E5	Fans’ Attendance	Average paying attendance for the Brazilian Championship.	(Alaminos & Fernández, 2019Alaminos, D., & Fernández, M. Á. (2019). Why do football clubs fail financially? A financial distress prediction model for European professional football industry. PLOS ONE, 14(12), e0225989. https://doi.org/10.1371/journal.pone.0225989 https://doi.org/10.1371/journal.pone.022... )
E6	Size	Dummy for the 12 major teams in the country (Atlético-MG, Botafogo, Corinthians, Cruzeiro, Flamengo, Fluminense, Grêmio, Internacional, Palmeiras, Santos, São Paulo and Vasco). (1 for the aforementioned and 0 for the others).	(Dantas et al., 2015Dantas, M. G. da S., Machado, M. A. V., & Macedo, M. A. da S. (2015). FATORES DETERMINANTES DA EFICIÊNCIA DOS CLUBES DE FUTEBOL DO BRASIL. Advances in Scientific and Applied Accounting, 8(1), 113-132. https://doi.org/10.14392/asaa.2015080106 https://doi.org/10.14392/asaa.2015080106... )
E7	Relegation	Dummy for relegated clubs (1 for relegation and 0 for the others).	(Dantas et al., 2015Dantas, M. G. da S., Machado, M. A. V., & Macedo, M. A. da S. (2015). FATORES DETERMINANTES DA EFICIÊNCIA DOS CLUBES DE FUTEBOL DO BRASIL. Advances in Scientific and Applied Accounting, 8(1), 113-132. https://doi.org/10.14392/asaa.2015080106 https://doi.org/10.14392/asaa.2015080106... ; Ruta, Lorenzon, & Sironi, 2019Ruta, D., Lorenzon, L., & Sironi, E. (2019). The relationship between governance structure and football club performance in Italy and England. Sport, Business and Management: An International Journal, ahead-of-p(ahead-of-print). https://doi.org/10.1108/sbm-10-2018-0081 https://doi.org/10.1108/sbm-10-2018-0081... )
E8	Promotion	Dummy for promoted clubs (1 for promoted clubs 0 for the others)	(Ruta et al., 2019Ruta, D., Lorenzon, L., & Sironi, E. (2019). The relationship between governance structure and football club performance in Italy and England. Sport, Business and Management: An International Journal, ahead-of-p(ahead-of-print). https://doi.org/10.1108/sbm-10-2018-0081 https://doi.org/10.1108/sbm-10-2018-0081... )
E9	Total Games	Total number of games played in the season.	(Plumley, Wilson, & Shibli, 2017Plumley, D., Wilson, R., & Shibli, S. (2017). A Holistic Performance Assessment of English Premier League Football Clubs 1992-2013. Journal of Applied Sport Management, 9(1). https://doi.org/10.18666/jasm-2017-v9-i1-7353 https://doi.org/10.18666/jasm-2017-v9-i1... )

Panel A - Continuous Variables Descriptive Statistics
Solvent		E1	E2		E3	E5
Minimum		-1,01	1,00		0,13	1614,00
First Quartile		1,17	7,00		0,39	5310,00
Median		1,83	15,00		0,46	11467,00
Mean		1,96	18,48		0,46	13193,33
Third Quartile		2,76	25,00		0,54	18220,00
Maximum		4,85	74,00		0,70	47140,00
Standard Deviation		1,16	14,74		0,12	9475,89
Insolvent		E1	E2		E3	E5
Minimum		0,14	1,00		0,12	1799,00
First Quartile		1,17	10,00		0,39	7028,25
Median		1,67	18,00		0,44	10854,50
Mean		1,81	19,70		0,45	11961,21
Third Quartile		2,31	27,75		0,52	15016,75
Maximum		4,85	60,00		0,71	34150,00
Standard Deviation		0,97	12,33		0,11	6966,29
General		E1	E2		E3	E5
Minimum		-1,01	1,00		0,12	1614,00
First Quartile		1,17	8,50		0,39	6017,00
Median		1,75	17,00		0,46	11225,00
Mean		1,89	19,16		0,45	12654,47
Third Quartile		2,48	26,50		0,53	17202,50
Maximum		4,85	74,00		0,71	47140,00
Standard Deviation		1,07	13,59		0,12	8337,46
Panel B - Dummy Variables Descriptive Statistics
	Size (E6)	Relegation (E7)	Promotion (E8)	Brazilian Championship Titles	Brazilian Cups Titles	Libertadores Cup participations
Solvent	45	18	14	5	2	18
Insolvent	51	21	21	3	6	13
General	96	39	35	8	8	31

Solvent	I1	I2	I3	I4	I5	I6	I7	I8	I9	I10	I11	I12	I13	I14
Minimum	0,00	0,00	-0,99	0,00	0,00	0,02	0,00	0,00	0,11	0,12	0,01	-4,47	-5,98	-1,88
First Quartile	0,00	0,09	-0,28	0,08	0,03	0,32	0,02	0,02	0,31	1,34	0,18	-0,11	-0,22	-0,15
Median	0,02	0,36	-0,18	0,21	0,08	0,53	0,06	0,06	0,43	2,28	0,30	-0,03	-0,07	0,01
Mean	0,05	0,42	-0,21	0,31	0,11	0,53	0,10	0,09	0,45	2,74	0,43	-0,12	-0,15	-0,07
Third Quartile	0,05	0,58	-0,09	0,48	0,16	0,74	0,15	0,14	0,55	3,41	0,46	0,05	0,03	0,09
Maximum	0,95	2,94	0,25	1,25	0,87	0,93	0,51	0,42	1,00	14,22	5,27	0,96	2,16	1,80
Std. Deviation	0,12	0,43	0,21	0,28	0,14	0,25	0,11	0,10	0,20	2,13	0,62	0,53	0,70	0,47
Insolvent	I1	I2	I3	I4	I5	I6	I7	I8	I9	I10	I11	I12	I13	I14
Minimum	0,00	0,01	-4,43	0,01	0,01	0,07	0,00	0,00	0,09	0,21	0,09	-0,90	-2,99	-2,75
First Quartile	0,00	0,10	-0,89	0,07	0,09	0,34	0,05	0,09	0,28	1,27	0,49	-0,07	-0,27	-0,12
Median	0,01	0,23	-0,50	0,12	0,16	0,57	0,12	0,20	0,38	2,27	0,85	-0,02	-0,05	0,01
Mean	0,08	0,30	-0,73	0,18	0,21	0,59	0,16	0,38	0,42	4,37	1,56	-0,03	-0,16	-0,09
Third Quartile	0,05	0,38	-0,26	0,22	0,27	0,84	0,21	0,38	0,51	4,19	1,35	0,01	0,03	0,11
Maximum	2,54	2,04	0,38	0,84	0,85	1,00	0,81	3,91	1,00	64,01	37,04	0,72	0,90	0,66
Std. Deviation	0,28	0,31	0,77	0,18	0,18	0,27	0,16	0,54	0,19	7,84	3,54	0,17	0,44	0,42
General	I1	I2	I3	I4	I5	I6	I7	I8	I9	I10	I11	I12	I13	I14
Minimum	0,00	0,00	-4,43	0,00	0,00	0,02	0,00	0,00	0,09	0,12	0,01	-4,47	-5,98	-2,75
First Quartile	0,00	0,10	-0,56	0,07	0,05	0,33	0,03	0,04	0,29	1,30	0,26	-0,08	-0,23	-0,13
Median	0,01	0,27	-0,27	0,15	0,11	0,55	0,09	0,11	0,40	2,28	0,48	-0,03	-0,05	0,01
Mean	0,07	0,37	-0,46	0,25	0,16	0,55	0,13	0,23	0,43	3,55	0,99	-0,09	-0,17	-0,07
Third Quartile	0,05	0,47	-0,14	0,33	0,21	0,79	0,19	0,24	0,53	3,97	0,93	0,03	0,03	0,11
Maximum	2,54	2,94	0,38	1,25	0,87	1,00	0,81	3,91	1,00	64,01	37,04	0,96	2,16	1,80
Std. Deviation	0,21	0,38	0,62	0,25	0,16	0,26	0,14	0,41	0,19	5,76	2,58	0,40	0,59	0,44

Model	Neurons	Sample	TP	TN	FP	FN	Type I error (%)	Type II error (%)	ACC (%)	AUC (%)
T-1	2	Training	111	103	0	0	0,00	0,00	100	-
T-1	2	Test	12	16	3	3	20,00	15,79	82,35	95,79
T-2	2	Training	99	87	0	0	0	0	100	-
T-2	2	Test	12	16	3	3	20,00	15,79	82,35	91,05
T-3	2	Training	79	69	0	0	0	0	100	-
T-3	2	Test	11	13	6	4	40,00	21,05	71,59	81,58

Panel A - Continuous Variables Descriptive Statistics
Solvent		E1	E2	E3	E5	E9
Minimum		-1,01	1,00	0,13	1614,00	28,00
First Quartile		1,17	7,00	0,39	5310,00	59,00
Median		1,83	15,00	0,46	11467,00	64,00
Mean		1,96	18,48	0,46	13193,33	63,34
Third Quartile		2,76	25,00	0,54	18220,00	69,00
Maximum		4,85	74,00	0,70	47140,00	83,00
Standard Deviation		1,16	14,74	0,12	9475,89	9,80
Insolvent		E1	E2	E3	E5	E9
Minimum		0,14	1,00	0,12	1799,00	38,00
First Quartile		1,17	10,00	0,39	7028,25	60,00
Median		1,67	18,00	0,44	10854,50	64,00
Mean		1,81	19,70	0,45	11961,21	63,00
Third Quartile		2,31	27,75	0,52	15016,75	68,00
Maximum		4,85	60,00	0,71	34150,00	79,00
Standard Deviation		0,97	12,33	0,11	6966,29	7,98
General		E1	E2	E3	E5	E9
Minimum		-1,01	1,00	0,12	1614,00	28,00
First Quartile		1,17	8,50	0,39	6017,00	59,00
Median		1,75	17,00	0,46	11225,00	64,00
Mean		1,89	19,16	0,45	12654,47	62,92
Third Quartile		2,48	26,50	0,53	17202,50	68,50
Maximum		4,85	74,00	0,71	47140,00	83,00
Standard Deviation		1,07	13,59	0,12	8337,46	8,93
Panel B - Dummy Variables Descriptive Statistics
	Size (E6)	Relegation (E7)	Promotion (E8)	Brazilian Championship Titles	Brazilian Cups Titles	Libertadores Cup participations
Solvent	45	18	14	5	2	18
Insolvent	51	21	21	3	6	13
General	96	39	35	8	8	31

[1] fabio_minatto@hotmail.com

Brasil

Brasil

Insolvency in Brazilian Football Clubs: Proposition of Models Based on Neural Networks

ABSTRACT