Financial Performance Analysis in European Football Clubs

Abstract

The financial performance of football clubs has become an essential element to ensure the solvency and viability of the club over time. For this, both the theory and the practical and regulatory evidence show the need to study financial factors, as well as sports and corporate factors to analyze the possible flow of income and for good management of the club’s accounts, respectively. Through these factors, the present study analyzes the financial performance of European football clubs using neural networks as a methodology, where the popular multilayer perceptron and the novel quantum neural network are applied. The results show the financial performance of the club is determined by liquidity, leverage, and sporting performance. Additionally, the quantum network as the most accurate variant. These conclusions can be useful for football clubs and interest groups, as well as for regulatory bodies that try to make the best recommendations and conditions for the football industry.

Neural Networks for Estimating Speculative Attacks Models

“…Qubit is defined as the smallest unit of information in quantum computation, which is a probabilistic representation. A qubit may either be in the “1” or “0” or in any superposition of the two [ 36 ]. The state of the qubit can be defined as follows in the Equation (24): where α and β are the numbers that point out the amplitude of the corresponding states such that .…”

“…Upgrading of quantum hidden layer weight in quantum backpropagation algorithm, the weights are upgraded by quantum gate conforming to Equation (26), so in this case, the equation would be as it appears in the Equation (32): where , the index i represents the number of outputs from quantum neuron and the index j defines the number of outputs from network, , and is the learning rate [ 36 , 37 ]. This ratio usually takes the value of 0.1.…”

“…, the index i represents the number of outputs from quantum neuron and the index j defines the number of outputs from network, γ ij = γ ij + η∆θ ij , and η is the learning rate [36,37]. This ratio usually takes the value of 0.1.…”

Assessing the Financial Performance of English Football Clubs: Arsenal and Manchester City

“…Some studies have investigated the financial performance of football clubs using a variety of techniques, for example, a Malmquist productivity index [6]; Grey Relational analysis [3], [18], [19], neural network [22], financial distress model [13], and Altman financial distress model [10], [20]. [22] examines the performance of more than 200 European football clubs using a neural network. The results demonstrate that the club's financial success is determined by liquidity, debt, and sporting performance.…”

“…Some studies have investigated the financial performance of football clubs in several countries for example [1], [2] in Greek, [18], [19] in Turkey, [3] in Italy, [10], [12] in France, [11] in Czech, [20] in Spanish, [4], [9], [21] in UK., and [8], [13], [22] in European continent. Different from [20], which uses only Altman [15] model, the present study also utilizes other measurements of bankruptcy, i.e., Springate [16] and Zmijewski [17], to see the consistency of the results.…”