Predicting financial distress across the football industry

• Main Author: Conde, Pedro de Almeida
• Publication Date: 2023
• Format: Master thesis
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/10400.14/41454
• 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

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.