Predicting financial distress across the football industry
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.14/41454 |
Resumo: | Accurately forecasting financial distress within the football industry holds significant importance for various stakeholders, including creditors, investors, shareholders and local communities. This research employs machine learning algorithms to forecast financial distress within the football industry over a 5-year period and by analyzing clubs' financial ratios. Two machine learning models are performed: a logistic regression and a neural network model. The primary objectives of this study are to test the effectiveness of these models, evaluate the financial performance of football clubs, provide an overview of the industry as a whole and examine the influence of the Covid-19 pandemic on financial distress within the sector. Despite the high levels of debt, unprofitability, irrationality and mismanagement that are prevalent in football clubs, bankruptcies are not such an ordinary event, being relatively rare. The machine learning models implemented in this study yielded interesting and favorable results, with the neural network model demonstrating a slightly higher level of predictive accuracy. However, the significant impact of Covid-19 on the overall industry partially impaired the predictive capabilities of the models, raising questions about their practical applicability. This study suggests that the unique status of football clubs, which shields them from being treated as ordinary businesses, may be the only factor that enables their survival. |
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Predicting financial distress across the football industryFinancial distressFootball industryLogistic regressionNeural networkNon-distressed clubsDistressed clubsFinancial ratiosDificuldades financeirasIndústria futebolísticaRegressão logísticaRede neuronalClubes financeiramente saudáveisClubes com dificuldades financeirasRácios financeirosDomínio/Área Científica::Ciências Sociais::Economia e GestãoAccurately forecasting financial distress within the football industry holds significant importance for various stakeholders, including creditors, investors, shareholders and local communities. This research employs machine learning algorithms to forecast financial distress within the football industry over a 5-year period and by analyzing clubs' financial ratios. Two machine learning models are performed: a logistic regression and a neural network model. The primary objectives of this study are to test the effectiveness of these models, evaluate the financial performance of football clubs, provide an overview of the industry as a whole and examine the influence of the Covid-19 pandemic on financial distress within the sector. Despite the high levels of debt, unprofitability, irrationality and mismanagement that are prevalent in football clubs, bankruptcies are not such an ordinary event, being relatively rare. The machine learning models implemented in this study yielded interesting and favorable results, with the neural network model demonstrating a slightly higher level of predictive accuracy. However, the significant impact of Covid-19 on the overall industry partially impaired the predictive capabilities of the models, raising questions about their practical applicability. This study suggests that the unique status of football clubs, which shields them from being treated as ordinary businesses, may be the only factor that enables their survival.A previsão de potenciais dificuldades financeiras na indústria futebolística contém uma grande importância para todos os participantes no negócio, incluindo credores, investidores, acionistas e comunidades locais. Nesta dissertação foram implementados algoritmos de machine learning para que se efetuasse a previsão de dificuldades financeiras para um período de 5 anos, através do uso de rácios financeiros. Os dois modelos elaborados foram uma regressão logística e uma rede neuronal. Os principais objetivos deste estudo são testar o desempenho destes modelos, avaliar a performance financeira dos clubes de futebol, efetuar uma visão geral da indústria futebolística e examinar o impacto da Covid-19 no setor. Apesar dos elevados níveis de endividamento, prejuízos, irracionalidade e má gestão, a verdade é que o número de falências entres clubes de futebol é reduzida. Os modelos de machine learning aplicados neste estudo apresentaram resultados interessantes e positivos. Contudo, o impacto da pandemia na indústria afetou a capacidade de previsão dos modelos, levantando questões acerca da sua potencial aplicação no mundo real. Este estudo sugere ainda que o estatuto dos clubes de futebol, que os diferencia de um negócio normal, pode ser o único fator que promove a sua sobrevivência.Reis, RicardoVeritati - Repositório Institucional da Universidade Católica PortuguesaConde, Pedro de Almeida2023-06-28T10:53:47Z2023-01-232023-012023-01-23T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.14/41454TID:203253140enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-12T17:47:02Zoai:repositorio.ucp.pt:10400.14/41454Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:34:08.781947Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Predicting financial distress across the football industry |
title |
Predicting financial distress across the football industry |
spellingShingle |
Predicting financial distress across the football industry Conde, Pedro de Almeida Financial distress Football industry Logistic regression Neural network Non-distressed clubs Distressed clubs Financial ratios Dificuldades financeiras Indústria futebolística Regressão logística Rede neuronal Clubes financeiramente saudáveis Clubes com dificuldades financeiras Rácios financeiros Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
title_short |
Predicting financial distress across the football industry |
title_full |
Predicting financial distress across the football industry |
title_fullStr |
Predicting financial distress across the football industry |
title_full_unstemmed |
Predicting financial distress across the football industry |
title_sort |
Predicting financial distress across the football industry |
author |
Conde, Pedro de Almeida |
author_facet |
Conde, Pedro de Almeida |
author_role |
author |
dc.contributor.none.fl_str_mv |
Reis, Ricardo Veritati - Repositório Institucional da Universidade Católica Portuguesa |
dc.contributor.author.fl_str_mv |
Conde, Pedro de Almeida |
dc.subject.por.fl_str_mv |
Financial distress Football industry Logistic regression Neural network Non-distressed clubs Distressed clubs Financial ratios Dificuldades financeiras Indústria futebolística Regressão logística Rede neuronal Clubes financeiramente saudáveis Clubes com dificuldades financeiras Rácios financeiros Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
topic |
Financial distress Football industry Logistic regression Neural network Non-distressed clubs Distressed clubs Financial ratios Dificuldades financeiras Indústria futebolística Regressão logística Rede neuronal Clubes financeiramente saudáveis Clubes com dificuldades financeiras Rácios financeiros Domínio/Área Científica::Ciências Sociais::Economia e Gestão |
description |
Accurately forecasting financial distress within the football industry holds significant importance for various stakeholders, including creditors, investors, shareholders and local communities. This research employs machine learning algorithms to forecast financial distress within the football industry over a 5-year period and by analyzing clubs' financial ratios. Two machine learning models are performed: a logistic regression and a neural network model. The primary objectives of this study are to test the effectiveness of these models, evaluate the financial performance of football clubs, provide an overview of the industry as a whole and examine the influence of the Covid-19 pandemic on financial distress within the sector. Despite the high levels of debt, unprofitability, irrationality and mismanagement that are prevalent in football clubs, bankruptcies are not such an ordinary event, being relatively rare. The machine learning models implemented in this study yielded interesting and favorable results, with the neural network model demonstrating a slightly higher level of predictive accuracy. However, the significant impact of Covid-19 on the overall industry partially impaired the predictive capabilities of the models, raising questions about their practical applicability. This study suggests that the unique status of football clubs, which shields them from being treated as ordinary businesses, may be the only factor that enables their survival. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-06-28T10:53:47Z 2023-01-23 2023-01 2023-01-23T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.14/41454 TID:203253140 |
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http://hdl.handle.net/10400.14/41454 |
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TID:203253140 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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