An analysis of financial risk assessment of globally listed football clubs

Abstract

The global football market has grown in the past three decades, and football clubs' sustainable financial operations have gradually gained attention. This study aims to construct a financial risk assessment model applicable to the football industry, explore globally listed football clubs' overall financial operating characteristics, and analyse the leading causes of the club's financial crisis. We selected a sample of 24 currently listed football clubs worldwide and an exploratory factor analysis (EFA) model to construct the model of financial risk assessment for football clubs. The model identified and classified the financial risk components for listed football clubs, thus facilitating risk warning and prevention for modern professional clubs. This study found that football clubs are at higher financial risk overall, with the following general characteristics: (1) small amount of listed capital; (2) high asset-liability ratio; (3) low net profits and a large proportion of clubs make losses; and (4) weak asset liquidity. Finally, the study discussed the leading causes of the financial crises of football clubs in both external and internal dimensions, providing a reference for the self-sustainability of clubs and football authorities.

1. Introduction

The sports industry has become an important support point for the global economy. According to official statistics, the football industry accounts for more than 40 % of the total annual value of the global sports industry, with a market share of nearly US$500 billion, accounting for half of the entire sports industry and ranking alone as the 17th largest economy in the world [1]. The sports performance industry, with clubs as the primary vehicle, is the focus of public attention. Since the 21st century, with the increasing commercialisation of football and the influx of social capital into the industry, the financial performance of football clubs has become an essential topic of discussion in the field of professional sports and corporate management.

Currently, clubs and football governing institutions are increasingly concerned about the financial health of their teams, as financial crises can leave clubs with severe financial losses, and sporting competitiveness can be severely affected and lead to clubs being unable to attract talented players and struggling to plan new infrastructure investments [2,3]. In addition to performing in the game and winning championships, it is the responsibility of the manager of each football club to promote the correct allocation of resources to obtain good management to ensure good performance in the field and financially. Moreover, for the entire national development, financial health guarantees fairness in the professional league and is the basis for the sustainable development of the football industry. For this reason, the Union of European Football Associations (UEFA) introduced the Financial Fair Play (FFP) financial regulation in 2010, which aims to reduce debt defaults to other clubs or employees, reduce irrationally significant investments of club funds and focus on the control of club financial statements [4]. This means that football clubs must be organised to generate income and not receive funds from sources other than football operations (e.g., parent companies), as this does not reflect the actual financial position.

Modern enterprises have extensive research on using models to predict financial risks. Different industries, with corresponding theoretical support, should establish targeted financial risk evaluation models based on their own financial data characteristics. However, financial early warning models are not tailored explicitly to professional football clubs. This is a significant motivation for conducting our study, and a detailed literature review of the research will be presented in the next chapter.

The main feature of our research is taking the financial data of publicly listed clubs as the starting point, focusing on their performance in the trading market. We use exploratory factor analysis (EFA) and dimension reduction techniques to summarize complex and overlapping indicators that are not directly related. We evaluate financial risks by scoring the classification factors based on the importance of factors. Admittedly, financial indicators and data may not fully reflect all aspects of a football club. However, the financial data in the annual reports comprehensively reflects the operational capacity and level of the clubs, encompassing various factors such as on-field performance, coaching, and management capabilities.

It is important to emphasise that listed and unlisted clubs are initially part of the same study as professional football clubs. Related research used a resource utilisation model to examine the correlation between sporting performance and the financial performance of listed Premier League clubs from 1998 to 2002 [5]. He showed that club listings could improve financial performance. Later, further demonstrated that listing has only a short-term positive impact on club revenues [6]. In contrast, the long-term revenue impact is insignificant. Furthermore, the listing had no significant effect on the club's performance in UEFA competitions [7]. Therefore, the experience of assessing the financial performance of listed clubs can be applied and extended to most professional football clubs worldwide, providing a “microcosm”. For this study, another important reason for the selection of listed clubs by the research subjects was the comprehensiveness and availability of financial data.

As mentioned above, this work aims to create a new financial performance analysis model for professional football clubs in response to the growing need for financial performance in the management of football clubs. The study uses a sample of all currently listed clubs worldwide for the analysis, which includes a system of indicators that measure clubs' financial performance along four dimensions (profitability, cash capability, solvency and operational capability). The study used an exploratory factor analysis approach as the primary analytical model. EFA has been used extensively in other areas of financial performance assessment, and the findings suggest that the assessment methods in this paper could be helpful in football governing authorities and any other interest group of football clubs.

2. Related literature

2.1. Financial performance and assessment methods

Financial performance is a primary issue that affects any company, and risk assessment is an essential process in the daily management of a business. For many years, the study of predicting the financial performance of companies has been famous among academics and professionals, with academics, accountants and personal finance professionals struggling to find methods that can predict and evaluate the financial performance of companies [8]. Today, these assessment methods have been well used in several industries or sectors, for example, industry [9], manufacturing [10,11] and banking [12,13].

In assessing the financial performance of general industries, two types of analysis methods can usually be distinguished: (1) ratio analysis (indicator), which determines how well an enterprise is doing by analysing financial indicators, such as the Wall Method or DuPont Analysis, and (2) the development of univariate and multivariate risk models that incorporate Z scores, logistic regressions, and neural networks [[14], [15], [16], [17]]. Recently, new methods for assessing financial risk have emerged in statistics and operations research. For example, Strong and Steiger introduced Monte Carlo simulation methods in financial risk assessment [18]. Sorwar and Dowd created a new model for evaluating the financial risk of options positions, including ordinary and singular options [19]. S. Kim used an AdaBoosted decision tree portfolio model research approach to investigate the determinants of financial distress [20]. Among the above methods, modern companies frequently use the model prediction method, where multivariate analysis is better than univariate analysis. However, the former has the limitation that it has to meet a variety of assumptions for statistical analysis and requires more data. The football industry needs more valid and specific models and related assessment indicators to analyse clubs' financial performance individually [21].

2.2. Football market and management practices

In recent decades, empirical research in the field of sport management has focused on the following dimensions: (1) debates on effectiveness maximisation versus profit maximisation [[22], [23], [24]], (2) evaluating overall club efficiency [[25], [26], [27], [28]], and (3) the relationship between financial operations and competition performance (sporting performance) [[29], [30], [31]].

In the first aspect, Profit maximisation [32,33] or win maximisation [22,34] has traditionally been assumed by researchers as the objective function for national league teams. Neale was the first to implicitly assume profit on behalf of professional sports teams' maximisation [35]. El-Hodiri and Quirk [36] formalised Neale's [35] discussion by developing a general economic model of a sports league with profit-maximising owners, and he developed a competition success function to model the competition and interaction between clubs. In contrast to the profit maximisation assumption, one stream of research assumes utility or profit maximisation. As noted by Fort [37], the formal concept of utility maximisation was introduced by both Sloane [22] and Quirk and El Hodiri [34]. Recent scholars have added specific parameters such as competitive equilibrium effects and market size [38] and reformulated the relationship between profit, winning and attendance in a utility maximisation function [39], but essentially built on the original work. Fort and Quirk argue that the difference between profit-maximising and winning-maximising leagues in the football talent market is higher team investment and greater demand for talent in a winning-maximising environment [40]. Moreover, regardless of the goal orientation on which a team is based, the issue of financial health is always an essential prerequisite for the proper operation of a club.

The ownership structure has always played a critical role in the operational efficiency of football clubs. The importance of ownership structures is based on the availability of resources and the motivation of owners to contribute these resources. According to property rights theory, owners have a higher incentive to invest if they own a majority stake in an entity [41] (pp. 354–355); Franck conducted an owner study of European professional football [42]. He argues that private investors increase team investment and debt but reduce profitability. Furthermore, he suggests that privately owned leagues are characterised by competition based on spending power rather than profitability. In a competition theory study, Lang, Grossmann, and Theiler confirmed that ‘sugar daddies' (i.e. individuals who invest large sums of money into a club and become its owners) increase winning and profitability [43]. Individuals who invest large sums of money in the club and become owners) increase win rates and revenues. Sugar daddies in large market teams also increase social welfare but reduce the competitive balance.

Further theoretical and conceptual research on sugar daddy ownership has found positive effects on soft budget constraints, investment risk and revenue volatility [44,45]. Hamil examined ownership structures in five European countries and identified membership association ownership as a way to rescue insolvent clubs from a growing phenomenon [46]. In contrast, Rohde and Breuer examine the ownership structure of elite European football clubs and find that the private majority ownership model is increasingly prevalent among the top 30 highest-earning clubs [2].

Some researchers have consistently acknowledged the importance of managers in the day-to-day running of clubs [47,48]. However, only a few empirical studies have analysed the impact of managers on club efficiency. Those that compare managers' efficiency usually attribute it to individual managers based on efficiency scores but often lack statistical explanations for managerial inefficiency [49,50].

Although there is extensive literature on listed football clubs, the main focus has been on the impact of on-field performance on share prices [[51], [52], [53], [54]]. Some inferences can be drawn from Refs. [55,56] regarding the asset class question. All these studies provide empirical evidence supporting football stocks being an asset class. However, whether this unique group has a sustainable financial and operational capability has not yet been addressed directly in academic research.

In this regard, football clubs have begun to understand that they need to focus on aspects related to their capital structure and liquidity to manage resources efficiently and obtain maximum financial results [57]. Relevant studies demonstrated the importance and role of accounting science in sports management, which can determine sports organisations' financial value and expenses of presentation rules [58]. Second, accounting produces accounting reports (more widely known as financial statements) that show sports companies' equity, financial and cash flows (clubs). These data are essential for analysing the operating situation of the business.

In the existing empirical studies that have individually assessed the financial performance of professional football clubs, the standard approach is to combine some financial data or accounting indicators and other ratio analysis methods to measure and estimate the financial risk of clubs. For example, Emery used ratio analysis to compare and analyse the profitability of Premier League and non-Premier League clubs [59]. The study found a growing revenue gap between Premier League and non-Premier League football clubs, a gradual increase in financial risk for lower league clubs and the need to develop sustainable management strategies to ensure a financially viable future. Dimitropoulos analysed the financial statements of football clubs in the Greek First Division between 1993 and 2006 to understand the specific factors contributing to the recent financial crisis in the league [60]. The results show that Greek football clubs are highly leveraged, have serious liquidity and profitability problems, and face increased financial risk attributed to financial mismanagement and political inefficiency over the last 15 years.

Da Costa Jahara developed a financial performance index to analyse the financial performance of Brazilian football clubs using data from 2014 [61]. In addition to analysing clubs' solvency, they used liquidity, profitability and indebtedness indicators. The results show that the financial performance of these clubs could be better when analysing liquidity, debt, profitability and solvency indicators. Perechuda used the alary and revenue coefficient (S/R) and the revenue asset coefficient (R/A) to assess financial risk in European football clubs. The study aimed to find the optimal salary scale and allocate resources efficiently for better sporting performance [62]. This study provides a specific empirical analysis of the financial performance of a particular sector of professional football clubs using financial data from listed clubs worldwide. Additionally, we ranks and analyses the financial risks of professional clubs in the same category or country worldwide, analysing the leading causes of financial crises in clubs from a financial and management perspective. It's more important that this study identifies differences in financial performance between clubs. Each sample club can be analysed individually and thoroughly from our model and identify weaknesses in financial performance.

3. Method

Assessing financial risks for publicly listed football clubs primarily involves constructing the EFA model. This chapter will focus on the methodology and pre-processing work involved in building the model, including selecting and evaluating publicly listed global football clubs, the criteria for choosing evaluation indicators and design variables, data sources, and data pre-processing. Additionally, we will explain the specific operations and mathematical expressions of the factor analysis model during the evaluation process.

3.1. Sample

The listing of football clubs can be traced back to the late 20th and early 21st centuries, during which several professional sports organisations, exemplified by multiple football clubs in the United Kingdom, eagerly pursued the option of going public. Their primary objective was to raise substantial capital and enhance their influence by trading stocks (or bonds) on the financial markets, laying the groundwork for securing increased commercial partnerships. As of March 1, 2021, there are 24 football clubs still listed worldwide. Many are well-known clubs from various European leagues, including Manchester United, Juventus, and Borussia Dortmund. Other clubs on the list include Ajax in the Dutch First Division, Porto in the Portuguese Super League, and Guangzhou Evergrande Football Club, the only Asian club. Three clubs were excluded from the total study sample, AF Braganca (Portugal), FC Copenhagen (Denmark) and Ruch Chorzow (Poland), because there needed to be more relevant financial data or more financial indicators available.

In the sample of football clubs (as in Table 1), most are from European countries and listed on European stock exchanges. From a national perspective, Turkey and Denmark each have four teams, while Portugal and Italy have three. Regarding their establishment time, except for Guangzhou Evergrande, all the clubs were founded over 50 years ago, with an average establishment time of 109 years (as of 2023). Manchester United has the most extended establishment history, reaching 145 years. Regarding the backgrounds of the surveyed investors, different clubs have investors and shareholders from various industries, including but not limited to finance, real estate, manufacturing, and others. Cross-investments and diversified investments are also observed. For instance, the primary shareholder of Manchester United is the Glazer family, which has interests in several different industries, with the most notable ones being finance, retail, and real estate.

Table 1.

Summary information on selected listed professional football clubs.

Club	Country	Established	Industry type of shareholders	Place of listing transaction
Manchester United	UK	1878	Finance, Retail and Real estate	New York Stock Exchange
Celtic Glasgow	UK	1888	Banking	London Stock Exchange
Borussia Dortmund	Germany	1909	Electricity industry	Frankfurt Stock Exchange
Juventus Turin	Italy	1897	Automotive manufacturing, Industrial	Milan Stock Exchange
AS Roma	Italy	1927	Automotive manufacturing, Telecoms	Milan Stock Exchange
Lazio Rom	Italy	1900	Banking, Services	Milan Stock Exchange
Benfica Lissabon	Portugal	1904	Corporate management, Financial services	Lisbon Stock Exchange
Sporting CP	Portugal	1906	Service industry	Lisbon Stock Exchange
FC Porto	Portugal	1893	Telecommunications	Lisbon Stock Exchange
Ajax Amsterdam	Netherlands	1900	Telecommunications	Amsterdam Stock Exchange
Olympique Lyonnais	France	1950	Finance, Automotive manufacturing	Paris Stock Exchange
Aalborg	Denmark	1885	Services, Finance	Copenhagen Stock Exchange
Aarhus	Denmark	1902	Manufacturing	Copenhagen Stock Exchange
Brondby IF B	Denmark	1964	Finance	Copenhagen Stock Exchange
Fort Silke	Denmark	1917	Sales industry	Copenhagen Stock Exchange
Fenerbahce Istanbul	Turkey	1907	Automotive leasing	Istanbul Stock Exchange
Galatasaray	Turkey	1905	Internet technology, Air transport	Istanbul Stock Exchange
Besiktas Istanbul	Turkey	1903	Household appliances	Istanbul Stock Exchange
Trabzonspor	Turkey	1967	Air transport	Istanbul Stock Exchange
AIK Football	Sweden	1891	Asset management, Floating	Nordic Growth Market
Guangzhou Evergrande	China	1993	Real estate, E-commerce	China New Stock Exchange

Statistical deadline: March 2021; The industry type is to which the main shareholder or sponsor of the club currently belongs and has been briefly listed; In the following, the club's name will be simplified.

The financial reporting cycle of European leagues is generally seasonal, with a balance sheet date of 30 June each year. However, it also contains a few clubs that use the number of natural years as their financial reporting cycle. To make the financial data for the exact season comparable between different clubs and to enrich the data for the study, the financial data selected for this paper are from the same season or year of clubs in different countries, i.e., 2018/19 season or 2018 year.

It must be emphasised that accounting is gradually becoming international and national due to the accelerated development of global accounting and a changing economic environment. Presently, the mainstream global accounting standards are the United States Generally Accepted Accounting Principles (U.S. GAAP) issued by the Financial Accounting Standards Board (FASB) and the International Financial Reporting Standards (IFRS) issued by the International Accounting Standards Committee (IASC). The European Commission required all listed companies in member states to adopt IFRS for preparing financial statements 2002. Accounting standards in other continents differ slightly from international standards due to economic, political and social factors. However, they gradually converge with the mainstream internationally recognised accounting standards in form, structure and content. The football clubs covered in this article are listed on different stock exchanges. However, a review of the information shows that the financial statements of each club are prepared by IFRS so that the same financial ratio indicators are comparable between clubs.¹ Finally, the study finalised a target sample of 21 clubs based on available resources.

3.2. Assessment indicators

The financial performance of professional sports organisations can be assessed in five fundamental vital areas: “Growth”, “Profitability”, “Return on Capital Employed”, and “Liquidity and Debt Servicing Capacity” [28]. This paper follows the above research methodology and the requirements of objectivity, completeness, comparability and sensitivity of financial indicators in the Criteria Value of Enterprise Performance Evaluation (CVEPE).² It selects financial indicators from five dimensions of profitability, growth capacity, operating capacity, cash capacity, and solvency to be included in the alternative range. Furthermore, 12 indicators in four dimensions were finally identified for inclusion in the assessment model.

The following are why the indicators were combined or removed from the category.

• (1)Considering that football clubs' main assets and profitability are derived from players, which means that intangible assets account for a large proportion of total assets, the study added an indicator of intangible asset turnover to the indicator system by referring to Ref. [28].
• (2)Taking into account the unique nature of listed companies (clubs) and reflecting the level of profitability of the listed clubs' issued and outstanding ordinary shares, the study has added an earnings per share (EPS) indicator to the profitability aspect of the assessment system [63].
• (3)Stable cash flow is the basis for the survival and development of modern enterprises. As a result of broken capital chains, countless football clubs go bankrupt every year. It has been shown that cash flow is a better indicator of a club's actual operations than accrual-based accounting profit, so this study examines the three financial indicators that show a club's cash capacity. They are operating net cash flow/total liabilities, operating net cash flow/current liabilities, and operating net cash flow/total assets [64,65].
• (4)The indicators included in the growth capacity were excluded. The reasons are as follows: 1) The growth capacity indicator has a similar meaning and effect to the profitability indicator. This study selects a single season of financial data for analysis, which is a weak examination of the club's growth capacity. 2) The growth capacity factor mainly reflects the average annual growth level of the club's revenue, profit and assets. In contrast, according to the nature of modern football club operations. Fan considered that the club prefers the utility maximisation theory for the daily management of the club, especially after the implementation of FFP. One of the club's attributes is a community-based public benefit enterprise [66].

In addition, following the principle of objectivity and practicality of financial ratios, some financial indicators, although differing in name from one country to another in financial reporting, reflected the same economic content and substance and were, therefore, included in the selection of financial indicators. Other indicators with different arithmetic or tax calculation principles were excluded.

The higher the value of the financial indicators calculated in this study, the more capable the club is in its operations and the higher the management level. Therefore, they are labelled “positive indicators”, while the opposite is a “negative indicator”. In summary, 12 financial indicators were selected for inclusion in the financial risk assessment model from four dimensions: profitability, operating capacity, solvency and cash capacity (as shown in Table 2).

Table 2.

Indicators selected for the financial risk assessment model.

Financial capacity	Indicators	Variables	Indicator direction	Financial indicator formula
Profitability	earnings per share (EPS)	$X_1$	positive	net profit after tax/number of ordinary shares in issue
	return on net equity (ROE)	$X_2$	positive	net profit after tax/[(opening shareholders' equity + closing shareholders' equity)/2]
	return on total assets (ROA)	$X_3$	positive	net profit after tax/[(total assets at the beginning of period + total assets at the end of period)/2]
	net profit margin	$X_4$	positive	net profit after tax/total operating revenue
Cash capability	ratio of operating cash flow to current liabilities	$X_5$	positive	net cash flow from operating activities/total current liabilities
	operating cash flow to total liabilities	$X_6$	positive	net cash flow from operating activities/total liabilities
	operating cash flow to total assets	$X_7$	positive	net cash flow from operating activities/total assets
Solvency	current ratio	$X_8$	positive	current assets/current liabilities
	equity ratio	$X_9$	reverse	total liabilities/total equity
Operational capability	intangible assets turnover ratio	$X_{10}$	positive	total operating revenue/[(opening intangible assets + closing intangible assets)/2]
	current assets turnover ratio	$X_{11}$	positive	total operating revenue/[(opening current assets + closing current assets)/2]
	accounts receivable turnover ratio	$X_{12}$	positive	total operating revenue/[(opening accounts receivable + closing accounts receivable)/2]

3.3. The model

Professional clubs have unique characteristics compared to other industries regarding operational risk. Most existing studies are limited to traditional methods (e.g., single variable models, ratio analysis, artificial neural network models) for establishing and comparing financial early warning models. The EFA model can divide the influence degree of financial risk, quantify financial risk with specific values, and make the results intuitive [67].

Second, for this study, listed clubs cover different countries and regions worldwide, each with different levels of economic and social development and with varying principles and approaches to club management, so a uniform “financial crisis”, “bankruptcy”, or “default” criterion cannot be used to analyse and define all clubs (for example, the regulations of the Chinese stock exchange are that listed companies with three consecutive years of operating losses are subject to a delisting risk warning with the stock name preceded by "*S. T.” representing a company that has already experienced a severe financial crisis). Therefore, traditional regression methods, particularly discriminant models, are inappropriate for this study [16,17,68,69]. As a result, EFA results for financial indicators that meet the requirements are more systematic and integral, allowing clubs to compare their ranking in the industry and better understand their risk levels by comparing their ranking. The study uses EFA as the comprehensive financial risk evaluation assessment model based on the above reasons. The public discriminators extracted will be of great practical importance.

For the research sample, the primary function of employing the EFA model is to utilize its dimensionality reduction technique to summarize complex and potentially overlapping financial indicators that are not directly related. Based on the significance of each extracted factor, a scoring system is applied to the classification factors, enabling an overall assessment of financial risk [70]. The steps in constructing a financial risk assessment model for football clubs are as follows:Let $X={\left(X_1,X_2,\dots ,X_{12}\right)}^T$ be an observable random vector, and $E\left(X\right)=\mu ={\left({\mu }_1,{\mu }_2,\dots ,{\mu }_{12}\right)}^T,\text{var}\left(X\right)=\sum ={\left({\sigma }_{ij}\right)}_{12\times 12}$, take into account the general model of factor analysis:

$$\{\begin{array}{c}{X}_1-{\mu }_1=a_{11}{f}_1+a_{12}{f}_2+\dots a_{1m}{f}_m+{\epsilon }_1\\ X_2-{\mu }_2=a_{21}{f}_2+a_{22}{f}_2+\dots a_{2m}{f}_m+{\epsilon }_2\\ \dots \dots \\ X_{12}-{\mu }_{12}=a_{121}{F}_1+a_{122}{F}_2+\dots a_{12m}{f}_m+{\epsilon }_{12}\end{array}$$

where $f_1$, $f_2,\dots ,f_m$ (m < 12) is the public factor, ${\epsilon }_1,{\epsilon }_{2,\dots ,}{\epsilon }_{12}$ are the unique factors, and the equation above can be written as a matrix representation:

$$X=\mu +AF+\epsilon \text{,}$$

where ${F=\left(f_1,f_2,\dots ,f_m\right)}^T$ is the public factor vector (which also becomes the principal factor), ${\epsilon =\left({\epsilon }_1,{\epsilon }_2,\dots ,{\epsilon }_{12}\right)}^T$ is the unique factor vector, and $A={\left(a_{ij}\right)}_{12\times m}$ is the factor loading matrix.

Let $E\left(F\right)=0$, $\text{var}\left(F\right)=I_m$, E($\epsilon$) = 0, $\text{var}\left(\epsilon \right)=D=diag\left({\sigma }_1^2,{\sigma }_2^2,\dots {\sigma }_{12}^2\right)$, $\text{cov}\left(F,\epsilon \right)=0\text{.}$.When performing the club's financial risk analysis, $X={\left(X_1,X_2,\dots ,X_{12}\right)}^T$ is a multidimensional random vector of 12 financial indicators for a sample of 21 clubs that can be tested, and ${F=\left(f_1,f_2,\dots ,f_m\right)}^T$ is a public factor of an unmeasured m-dimensional random vector. Ultimately, the status of the scores and their weights for each public factor were calculated, and the following formula was used to calculate the composite score for the listed club factor:

$$F_{club}=\frac{W_1{F}_2+W_2{F}_2+\dots W_{12}{F}_m}{W_1+W_2+\dots +W_{12}}=\sum _{i=1}^m{\omega }_i{F}_i$$

3.4. Data

Financial data of 21 listed football clubs for the 2018/19 season (2018 year) were selected from the annual financial reports provided by the official websites of the listed football clubs. The rest of the data are from Deloitte & Touche financial reports and databases from relevant financial websites (e.g., https://cn.investing.com). It should be noted that most of the samples are listed on Euronext. Share trading and financial information details are available in the STOXX Europe Football Index. The software used for the statistical analysis of the data was STATA 17.0 (StataCorp LLC, College Station, TX, USA) and SPSS 25.0 (SPSS Inc., Chicago, IL, USA).

Due to continuous losses in the last three years, Roma, Sporting Lisbon, Porto, Fenerbahce, Galatasaray, Besiktas, Trabzonspor, and Guangzhou Evergrande have negative total shareholders' equity on their financial statements. Funds raised from the public offering needed to be increased to cover these losses. This is inconsistent with the actual situation, as reflected in the data. The study has reversed some of the data for the above clubs to be fair and unbiased in analysing financial indicators. Of the 12 final financial indicators, 11 were positive, and one was negative (equity ratio), and the study adopted the inverse method for this indicator to make the assessment criteria uniform.

4. Results

4.1. Feasibility tests for EFA

The Kaiser‒Meyer‒Olkin measure of sampling adequacy (KMO value) for this study was 0.632 (as shown in Table 3). The observed value of Bartlett's test of sphericity (approximate chi-square) was 192.346, with a df value of 66 and a Sig. Of 0.000. The corresponding probability p was close to 0, less than the significance level of 0.01, thus rejecting the null hypothesis that the correlation matrix is a unitary array and that there is a significant correlation between the original variables. Combined with the above test results, the sample data suit EFA.

• c)4.2. Routine Output Results

Table 3.

Feasibility tests for EFA.

KMO and Bartlett's test
The Kaiser-Meyer-Olkin metric for sampling adequacy		0.632
Bartlett's test for sphericity	Approximate cardinality	192.346
	df	66
	Sig.	0.000

The factors were extracted using principal component analysis (PCA). As described in Table 4, four principal components were selected, and the cumulative variance contribution of the first four common factors was 84.227 %, close to 85 %. Therefore, extracting these four factors can better explain the information in the original variables.

Table 4.

Extracted principal components.

Composition	Initial Eigenvalues			Extracting the sum of squares of loads			Rotating load sum of squares
	Total	Variance%	Cumulative%	Total	Variance%	Cumulative%	Total	Variance%	Cumulative%
1	5.055	42.128	42.128	5.055	42.128	42.128	3.918	32.654	32.654
2	1.986	16.550	58.679	1.986	16.550	58.679	2.678	22.320	54.974
3	1.804	15.033	73.712	1.804	15.033	73.712	1.804	15.032	70.006
4	1.262	10.515	84.227	1.262	10.515	84.227	1.706	14.220	84.227
5	0.761	6.340	90.567

Extraction method: principal component analysis (PCA).

The eigenvalues and contributions changed between pre- and post-rotation, and the cumulative contributions of the four common factors did not change. This can be interpreted as there is no commonality of the original variables, just a redistribution of the variance of each male factor explaining the original variables, changing the variance contribution of each factor and making the extracted factors easy to interpret.

Then, let $F_1$, $F_2$, $F_3$ and $F_4$ be the four common factors extracted to explain better the factor variables based on the principle of maximum variance, the extraction method is PCA, the rotation method is Kaiser normalised orthogonal rotation, and the rotation converges after six iterations.

As shown in Table 5, Composition (before rotation) is the initial unrotated factor loading matrix and Composition (after rotation) is the rotated factor loading matrix. It can be observed that the assignment of loadings on each factor is more apparent after rotation (shaded areas in the table), thus making it easier to interpret the significance of each factor than without rotation.

Table 5.

Rotated pre and post factor loading matrices.

Extraction method: PCA. Rotation method: Kaiser normalised the maximum variance method. a. The rotation converged after six iterations.

Based on the above-rotated factor loading matrix (see Table 5), the specific process of naming each common factor is as follows.

• (1)In factor $F_1$, the loadings of $X_1$ (earnings per share), $X_2$ (return on net equity), $X_3$ (return on total assets) and $X_4$ (net profit margin) are 0.847, 0.887, 0.906 and 0.948, respectively, which are larger than the loadings of other indicators, so $F_1$ is mainly represented by the four indicators $X_1$, $X_2$, $X_3$, and $X_4$. Hence, the factor $F_1$ is the most critical indicator of the company's profitability.
• (2)Similarly, factor $F_2$ is mainly represented by three indicators, $X_5$ (ratio of operating cash flow to current liabilities), $X_6$ (operating cash flow to total liabilities) and $X_7$ (operating cash flow to total assets), which are named the “cash capacity factor".
• (3)Factor $F_3$ is mainly represented by two indicators, $X_{10}$ (intangible assets turnover ratio) and ${\mathrm{X}}_{11}$ (current assets turnover ratio), which are named the “operating capacity factor".
• (4)Factor $F_4$ is mainly represented by three indicators, namely, $X_8$ (Current ratio), $X_9$ (Equity ratio) and $X_{12}$ (Accounts receivable turnover ratio), which are named the “debt servicing and liquidity factor".

4.2. Establishing the financial risk assessment model

The factor score weights for each public factor were derived using the factor score coefficient matrix. The scores for each sample on the public factors and the composite factor scores were calculated via Excel (see Table 6) and aggregated, ultimately allowed for the ranking of each club's financial risk profile and the assessment of the composite factor score Model F for each club.

Table 6.

Matrix of coefficients for factor scores.

Financial Indicators	Composition
	${\mathrm{F}}_1$	${\mathrm{F}}_2$	${\mathrm{F}}_3$	${\mathrm{F}}_4$
${\mathrm{X}}_1$: earnings per share (EPS)	0.231	0.030	0.035	−0.108
${\mathrm{X}}_2$: return on net equity (ROE)	0.242	−0.099	0.045	0.065
${\mathrm{X}}_3$: return on total assets (ROA)	0.269	−0.006	−0.015	−0.111
${\mathrm{X}}_4$: net profit margin	0.271	−0.042	0.046	−0.120
${\mathrm{X}}_5$: ratio of operating cash flow to current liabilities	−0.080	0.409	0.014	−0.060
${\mathrm{X}}_6$: operating cash flow to total liabilities	−0.068	0.367	0.030	−0.002
${\mathrm{X}}_7$: operating cash flow to total assets	0.060	0.261	−0.053	−0.084
${\mathrm{X}}_8$: current ratio	0.115	−0.174	−0.340	0.321
${\mathrm{X}}_9$: equity ratio	−0.043	0.032	−0.103	0.450
${\mathrm{X}}_{10}$: intangible assets turnover ratio	0.104	−0.212	0.363	0.135
${\mathrm{X}}_{11}$: current assets turnover ratio	−0.009	0.061	0.511	0.001
${\mathrm{X}}_{12}$: accounts receivable turnover ratio	−0.129	−0.051	0.161	0.579

Extraction method: PCA. Rotation method: Orthogonal rotation method with Kaiser normalisation.

Hence, the combined professional club factor score Model F can be expressed as:

$$\mathit{F}=\mathbf{0.32654}\times {\mathbf{F}}_{\mathbf{1}}+\mathbf{0.22320}\times {\mathbf{F}}_{\mathbf{2}}+\mathbf{0.15032}\times {\mathbf{F}}_{\mathbf{3}}+\mathbf{0.14220}\times {\mathbf{F}}_{\mathbf{4}}$$ (5)

4.3. Global listing clubs score ranking

The combined scores of the above factors are used to rank and evaluate each listed club's financial risk comprehensively. The higher the company's score is, the better its operational management capabilities and the lower the expected financial risk. The variables were brought into the factor scoring Model (5) to derive the composite score ability F. It is a ranking for the 21 listed football clubs on the four common factors (Table 7) and a list of the scores and ranking of the listed football clubs on each ability factor.

Table 7.

Summary of scores and ranking of listed football clubs by each factor.

	Club	Profitability factor		Cash capability factor		Operational capability factor		Solvency and capital turnover factor		Comprehensive score
		Factor Score	rank	Factor Score	rank	Factor Score	rank	Factor Score	rank	Factor Score	rank
1	Borussia Dortmund	−0.30	16	2.76	1	0.49	5	1.93	2	0.87	1
2	Fort Silke	1.02	2	−1.23	18	3.03	1	0.45	6	0.58	2
3	Aarhus	0.64	5	0.32	6	−0.13	11	1.28	3	0.44	3
4	Ajax Amsterdam	1.31	1	0.69	4	−0.88	18	−0.24	13	0.41	4
5	Manchester United	0.18	10	1.09	3	−0.82	17	1.11	5	0.34	5
6	Galatasaray	0.28	9	0.46	5	0.63	4	−0.94	16	0.16	6
7	Lazio Rom	−0.01	13	1.23	2	−0.08	10	−0.98	18	0.12	7
8	Olympique Lyonnais	0.43	8	−0.06	13	0.06	7	−0.18	11	0.11	8
9	Trabzonspor	−0.39	17	0.24	8	1.51	2	−0.57	14	0.07	9
10	Celtic Glasgow	0.70	4	−0.34	15	−0.99	19	0.34	7	0.05	10
11	AIK Football	0.43	7	−1.37	19	−0.19	12	1.27	4	−0.01	11
12	Benfica Lissabon	0.93	3	−0.51	17	−1.25	21	−0.16	9	−0.02	12
13	Brondby IF B	−0.83	20	−0.05	11	1.42	3	−0.18	10	−0.10	13
14	FC Porto	0.44	6	−0.06	12	−0.53	16	−1.04	20	−0.10	14
15	AS Roma	0.13	11	0.01	10	−0.08	9	−1.03	19	−0.11	15
16	Juventus Turin	−0.20	15	0.21	9	0.13	6	−0.84	15	−0.12	16
17	Sporting CP	0.03	12	−0.30	14	−0.07	8	−0.98	17	−0.21	17
18	Besiktas Istanbul	−0.48	18	0.31	7	−0.41	14	−1.04	21	−0.30	18
19	Aalborg	−0.66	19	−1.44	20	−0.33	13	2.04	1	−0.30	19
20	Fenerbahce Istanbul	−0.03	14	−1.61	21	−1.01	20	−0.24	12	−0.55	20
21	Guangzhou Evergrande	−3.62	21	−0.35	16	−0.49	15	−0.02	8	−1.34	21

A histogram of club competency factor scores was drawn based on the data in Table 7. As shown in Fig. 1, Dortmund Club has the highest total score, except for the low score on the profitability factor; the other three competencies are ranked in the top 5. The lowest score was achieved by Guangzhou Evergrande, with a comprehensive score of −1.34. The values of financial risk for each of the listed clubs are shown in Fig. 1; it was found that the leading causes and composition percentage of risk factors for each club's financial crisis are different.

Fig. 1.

Histogram of listed football clubs' scores by each ability factor.

It should be noted that the lack of profitability (like Dortmund's) is not a phenomenon of individual clubs. It has become the norm for listed clubs. In the 2018/19 season, 48 % of the clubs' net profits were negative, among which the loss amount of Besiktas, Blonde by, and Lazio was several times that of the previous season. From the perspective of cash flow, more than half of the clubs' net operating cash flow at the end of the season was less than zero. The fund management level of football clubs does not match their rising importance in the global football market and the sports industry. Factor scores of football clubs are generally low, and our research confirms that the overall financial risks of football clubs are high, and logical fund management remains a significant problem.

4.4. Ability factor score comparison

Grouped line graphs based on individual factor scores are drawn in Fig. 2. Among them, the highest score in the listed clubs' profitability factor is 1.31 for Ajax from the Netherlands. The lowest score is −3.62 for Guangzhou Evergrande from China, while the other clubs are less different, except for the highest and lowest scores. The average score of the 19 clubs is 0.122. The two clubs from the UK, Manchester United and Celtic, and the three clubs from Portugal performed well, while Italy and Turkey League clubs performed mediocre.

Fig. 2.

Trend chart of factor scores of listed clubs in different countries.

The highest score on the cash factor for listed clubs was 2.76 for Borussia Dortmund in the Bundesliga, and the lowest score was −1.61 for Fenerbahce in the Turkish Superliga, with a wide gap between the clubs, with the average score for the 21 clubs being −0.018.

The highest score in the operating factor for listed clubs was 3.03 for F.C. Silkeborg in the Danish Superliga. The lowest score was −1.25 for Benfica in the Portuguese Superliga, except for F.C. Silkeborg. The difference between the rest of the clubs was slight, with an average score of 0.15. The performance of clubs varied slightly between countries, with the UK and Portuguese league clubs scoring lower overall. Their respective average scores were −0.91 and −0.62.

The highest score in the debt servicing and liquidity factor for listed clubs in Aalborg in the Danish Superliga was 2.04. In contrast, the lowest score was Porto in the Portuguese Super League at −1.04. The scores of the 21 clubs showed a wide variation and a bifurcation, with Italy, Portugal, and Turkey league clubs scoring lower and clubs in the Denmark, UK, German (Dortmund) and Swedish (AIK Football) leagues performing better.

5. Discussion

5.1. Applicability of the EFA method

The study used the EFA method to identify the specific dimensions and content of the evaluation indicators in the club's financial risk assessment model. The EFA indicated that a four-factor solution fit the data best. This model adopts the PCA approach to extract common factors, possessing the following unique advantages in practical applications: (1) Dimensionality reduction: PCA transforms high-dimensional data into lower-dimensional data, facilitating a better understanding and interpretation of the financial data structure. (2) Data transformation: Linear transformations convert Original variables into new composite variables. In PCA, these composite variables are linear combinations of the original variables, and they capture the maximum variance in the data by identifying the directions (principal components) that best preserve the information of the original data. In our research, it provides feedback on the essential dimensions of the club in financial assessment.

There is no complete information contained in the common factor in the model assessment process; however, by applying a variance-maximising orthogonal rotation to the factor loading matrix, these four common factors provide a more accurate explanation of the primary information contained in listed clubs' financial risk assessments. This downscaled assessment model provides essential assistance in the subsequent identification and prevention of each club's risk categories and gives managers an overview of the overall operational situation of their football club. Additionally, the evaluation model is primarily based on EFA and financial indicators of each listed club, eliminating the obstacle of inconsistencies in the criteria for determining the financial crisis of clubs in different countries, which allowed uniform ranking and categorisation of all study samples. It allowed for the identification of the differences between clubs of different types and countries, which was one of the reasons for choosing the model.

5.2. General characteristics of financial risk

5.2.1. Small scale of listed capital

The listed football stock segment needs to be more significant for the number and trading volume of listed football clubs, and listed football stocks are the most readily available investment vehicle. This may be true for the current situation, as at the end of 2019, there were 22 listed European football clubs. Furthermore, Prigge and Tegtmeier similarly argue that the advantages of listed finance are significantly offset by the fact that football stocks have returned less than standard stocks over the last 20 years [71]. In other words, comparing the overall economic data with the football sector, it has successfully decoupled from the overall economy. The rationale behind purchasing these shares by the typical holder of football stocks supports the decoupling of football stock returns from the general business cycle. Institutional investors pulled out of listed English football clubs shortly after the IPO wave of the early 1990s [72].

Among the 21 clubs in the sample, Manchester United has 16.457 million ordinary shares, Lazio 67.74 million and Ajax only 18.33 million. Most listed clubs currently raise far less money through their IPO than the ongoing investment given by the club's shareholders (owners), which is potentially why the unique nature of the financial resources of football clubs dictates that owners must keep investing money to remain competitive on the pitch. Since financial investors do not derive additional returns from holding football shares, their bidding behaviour depends almost entirely on the core fundamentals of pure football shares, i.e. dividends and share price movements. However, they are clearly in the minority among the shareholders of European clubs. Fans often own shares in the teams they support not for profit but as a form of “spirituality” [73]. At the same time, clubs choose to go public to expand their brand in the market, enhance their reputation at home and abroad and lay the foundation for more commercial partnerships.

5.2.2. High asset-liability ratio

The asset-liability ratio is a financial metric that plays a crucial role in assessing the risk profile of a company or organization. It measures the proportion of liabilities to total assets and provides insights into the company's debt levels and financial stability. By analyzing the asset-liability ratio, investors, analysts, and creditors can evaluate the company's ability to meet its debt obligations and gauge its overall financial health. This ratio helps identify the extent of financial leverage and the potential risk associated with excessive debt. Several studies have highlighted the significance of the asset-liability ratio in risk assessment and financial decision-making [74,75]. Understanding this ratio enables stakeholders to make informed investment, lending, and risk management decisions.

Usually, a corporate asset-liability ratio of approximately 50 % is considered normal. However, in the study of football clubs as a sample, there were 16 clubs with an asset-liability ratio of over 50 %, accounting for 76.19 % of the total number of clubs in the sample; among them, there were eight listed clubs with an asset-liability ratio of over 100 %, namely, Roma, Porto, Galatasaray, Fenerbahce, Besiktas, Trabzonspor Sporting Lisbon, and Guangzhou Evergrande. In particular, Guangzhou Evergrande is listed on the New Third Board of China and has an asset-liability ratio of 216.34 %, which implies that it is at risk of being delisted or even liquidated at any time (in March 2021, Guangzhou Evergrande's shares “ST Hengbao” were delisted); even European giants such as Manchester United and Lazio have asset-liability ratios of nearly 90 %, with nearly half of the listed football clubs in the total sample facing insolvency. This conclusion is similarly supported by Ref. [76], who measured an average asset-liability ratio of 1.06 for 21 European football clubs between 2002 and 2015, showing a chronically high leverage ratio for football clubs compared to the traditional industry. In this respect, football clubs have financial characteristics that differ from those of the general industry. Aglietta found a correlation coefficient of only 0.28 between the STOXX European Football Index and the European STOXX 50 Index from 1991 to 2009 [77]. The beta results of Prigge and Tegtmeier similarly suggest that the correlation between football stocks (European Football Clubs, 2010–2016) and standard stocks is only moderately correlated [56].

5.2.3. Low net profit and a large proportion of clubs with financial deficits

Among the 21 sample clubs selected for the study, ten clubs experienced a decrease in net profit in the 2018/19 season compared to the previous season. Based on the financial data collected over the past three years for these clubs, it was determined that the decrease in net profit was primarily due to a decrease in gross operating income and an increase in operating expenses, as well as significant increases in player wages and equipment depreciation. Similarly, Related studies analysed the European listed professional football clubs and found similar financial risks [56]. The most significant drop in net profit for the 2018/19 season was recorded by Besiktas F C., whose massive drop in profit stemmed from a drop in total operating revenue of 22.28 % for the season, followed by a 49.21 % increase in interest expenses for the year compared to the previous year. This coincides with the relatively low financial performance of listed clubs in Turkey. Of all the clubs in the sample, Guangzhou Evergrande, Trabzonspor, Fenerbahce, and Roma are the four football clubs that made losses for three consecutive years.

5.2.4. Weak asset liquidity

Liquidity is an important indicator to measure the financial performance of clubs, and it has been found that there is a significant negative correlation between liquidity and financial performance [78,79]. However, this study found that of the three liquidity indicators, only current asset turnover was significantly and positively correlated with financial performance (r = 0.455, p < 0.05). The unique developmental rules of professional football can explain this.

• •The training cycle of professional players is long.
• •The opportunity cost is high.
• •The success rate of football players is generally low, which leads to high investment costs, a long construction period, a large proportion of intangible assets and the ability to realise and liquidate assets (weak compared to other industries).

In particular, several fixed assets in developing countries, such as home stadiums and youth training centres, are leased, which makes it more difficult for clubs to obtain long-term loans and puts them under significant financial pressure in the long run [64]. Once a club does not have enough realisable assets to service its debts, it will face serious financial risks or even liquidation at any time.

5.3. Causes of high financial risk for football clubs

5.3.1. Internal dimensions

Zhang showed a significant correlation between ownership structures and financial risk [80]. Furthermore, the financial performance of a club is optimal when ownership is dispersed, and the ownership concentration of professional football clubs has a “U-shaped” relationship with financial performance on the whole [42,81]. The relevant conclusions of this study also indirectly support this point of view. For Serie A, the family-style operation mode leads to the high ownership concentration of many clubs. It is a common phenomenon that the ownership balance is weak. In particular, Roma and Juventus rank only 15th and 16th in total scores. Similarly, the “one dominant share” phenomenon is common in China and Turkey [82]. The highly centralised ownership structure and long-term losses make the club's shareholders (owners) face tremendous pressure for survival.

The financial performance in Fig. 2 also demonstrates that this group of clubs tends to have lower financial performance and higher financial risk. In particular, Guangzhou Evergrande FC has an average score of −1.12 in all four factors, ranking last among all clubs. In this regard, a study by Hamil examined ownership structures in five European countries and identified membership association ownership as a solution to the growing problem of insolvent clubs [46]. Alternatively, Rohde and Breuer examined the ownership structure of elite European football clubs, finding that private majority ownership is increasingly prevalent among the highest-earning clubs [2]. However, in their recent study of the Premier League, they found a downward trend in the financial efficiency of clubs with major foreign investors, with the financial efficiency of foreign-owned clubs falling from 0.53 in 2006 to 0.47 in 2012, while the financial efficiency of other clubs remained more stable at around 0.53 [83]. Therefore, this study has proven that a stable and rational ownership structure is the key driver of the club's financial health.

The EPL is the world's oldest and best-run league. Commercial, broadcast and matchday revenues are the primary revenue streams for professional football clubs in England, with broadcast revenues directly linked to league results. Related research on Premier League club revenues shows that club revenue levels strongly correlate with sporting competition results [84]. For example, Manchester United's gate receipts in 2014/15 were over £10 million lower than in 2013/14 because United finished only seventh in the Premier League, resulting in a drop in gates and total revenue. After all, they lost out on the Champions League and Europa League the following season. Similarly, Berkowitz and Depken found that winning does not necessarily mean winning, while losing does portend losing, directly affecting stock prices [85]. In this regard, extensive literature already demonstrates that on-field performance significantly impacts share prices [3,31,[51], [52], [53], [54]].

Finally, the lack of rational planning and blind investment in clubs directly cause losses. Professional football has industry specificities, as demonstrated by the fact that the expenditure on human resources in clubs accounts for a large proportion of the total expenditure of clubs, especially in competitive leagues where excellent players and coaches are crucial to operations. The team's competitive level and the abnormal flow of intangible assets often make it more challenging to cover the related losses. Data show that the overall expenditure of European football clubs reached €16.4 billion in 2014, of which €9.856 billion was spent on wages, accounting for 60.3 % of total costs. The wage bill for players amounted to €7 billion, accounting for 70 % of the wage bill [86]. As a result, critical internal decisions, such as player transfers and contract renewals and the choice of a new manager, will directly impact a club's investment risk and, therefore, its financial health.

5.3.2. External dimensions

Any changes in current socioeconomic conditions or fluctuations in international macro football management policies will significantly impact the club's day-to-day operations and financial management. Club sporting revenues and merchandise sales are directly affected by downward macroeconomic trends, falling incomes, and deflation, which result in club bankruptcies from economic downturns, as was demonstrated by the successive bankruptcies of Slovakian giants Zilina, Welsh top-flight champions Lair FC and Belgian giant Lokeren during the COVID-19 [87]. In its analysis of the Bundesliga under the impact of the epidemic in the 2020/21 season, KICKER said that 13 of the 36 clubs in the Bundesliga and Bundesliga 2 would face bankruptcy if the season did not restart. Another example is that since 2008, European football clubs, in general, have been losing money, reaching a staggering €1.7 billion overall in 2011, with only 8 of the 54 top leagues under UEFA making a profit. Fortunately, since 2013, when the FFP came into force, the high spending of European football clubs has been curbed, and the overall losses have gradually decreased, with the first overall profit being made in 2017. Therefore, a sound football management policy ensures that teams break even and maintain healthy development. In this study, the two British clubs with better financial performance, Manchester United and Celtic (as in Fig. 2), also benefited from the early implementation of local financial oversight policies, which largely safeguarded the clubs' stable operations [88].

Second, Liu and Gong demonstrated that wealth and resources are increasingly concentrated in a few clubs [89]. In the case of the top five European leagues, the competitive power of small and medium-sized clubs is gradually weakening, with a few clubs holding the “oligopoly advantage” for an extended period and competition between clubs gradually becoming unbalanced. In this study, clubs such as Juventus and Lazio in Serie A, Manchester United in English Premier League, Benfica and Porto in Portugal's league, and Fenerbahce and Besiktas in the Turkish Premier League are among those with high levels of competitiveness and popularity in their countries, as well as in Europe. Over the years, these clubs have grown a fan base several times larger than other clubs [47,90]. They continue to recruit the best athletes due to their expanding financial resources, raising competition and creating a new virtuous cycle.

In contrast to the unabated momentum of the giants, the nonlisted clubs and the ‘fringe’ clubs in the league's lower and middle echelons have seen little revenue growth due to their popularity and market share and have become increasingly vulnerable and financially strained. The introduction of the Bosman Act in 1995, which led to the continued increase in player prices and transfer fees, made it even more difficult for these clubs to increase their revenues to offset the massive increase in expenditure. As a result, they were exposed to substantial financial risks, with several clubs going bankrupt, a phenomenon more common in the Turkish and Chinese professional leagues.

Katwala argued that giving clubs a stakeholder's voice is central to collaborative sports governance, culminating in institutionalised and unified cooperation [91]. This study also verifies this since the Bundesliga has been very effective in the league governance area, with a policy requiring parent clubs to form limited liability companies (teams) that have over 50 % voting rights, thereby raising funds and keeping the clubs in good shape, while leaving the fans to keep the core of German football intact. It guarantees a steady flow of money out of the clubs and limits the amount of foreign capital that can be invested at any cost. This is why Borussia Dortmund, the Bundesliga's representative, is ranked in the top three competencies, except for the profitability factor, which is average, and in particular, the cash capacity and the debt servicing and liquidity capacity, which rank first and second, respectively, for all clubs. Second, the improvement in the financial operations of European clubs in recent years has also been primarily due to the enactment and implementation of the FFP.

The varying levels of development and styles of football across continents, countries and regions, as well as the differences in the business objectives and operating models of clubs, make each club's financial risk triggers different, and there is no general rule to follow. However, the above analysis can serve as a reference for most clubs.

5.4. Limitations

In this study, the financial indicators are selected from the balance sheet and cash flow statement, which reflect the financial activities that occurred in the past period. The indicators are obtained with a lag. For the model itself, the factor analysis model possesses specific inherent issues, such as the lack of complete alignment between factors (observed variables) and latent variables, leading to subtle random errors when utilizing these factor values for data analysis. Second, the study used financial data from a single season (the year 2018) of 21 listed football clubs. However, due to the global spread of COVID-19, which hit almost all clubs hard, financial reports from subsequent seasons no longer provide a fair and objective reflection of the clubs' operational capacity, severely limiting the significance of the results.

Future research perspectives must be revised to examine whether and what significant differences exist in the factors influencing professional leagues in various countries or at different levels in the same country internationally. Primarily if the data can be used for football clubs in lower leagues or from developing economies, it would be interesting to replicate this study to understand the extent to which these findings can be generalised. This is a suggested area for future research.

6. Conclusion

In this study, 24 listed football clubs worldwide were selected for analysis. We used the EFA model and financial data from listed clubs to construct a club financial risk assessment model. The study reveals that the model is suitable for evaluating such problems. The overall financial risk of listed football clubs worldwide is high, with the following general characteristics: small listed capital size, high asset-liability ratio, low net profit and a large proportion of loss-making clubs, and weak asset liquidity. Additionally, this study analyses the leading causes of the club's financial crisis, both internally and externally. Macroeconomic changes, a lack of competition in leagues, and management issues are the primary external causes. At the same time, the primary internal triggers are the single shareholding structure, fluctuating competition results and incorrect investment decisions. This research has pertinent implications for club risk management and can be used to conduct financial risk assessments for other professional sports organisations.

Endnotes

• 1Most of Turkey's territory is in Asia, but the Turkish Football Federation is affiliated with UEFA. Due to the increase in foreign investment and M&A activity and the implementation of the new Turkish Commercial Code, Turkey has been preparing financial statements by International Financial Reporting Standards (IFRS) (Turkish Financial Reporting Standards) across all industries since 2013. Manchester United is listed on the New York Stock Exchange in the U.S., but IFRS prepares its financial statements. The relevant financial data of Guangzhou Evergrande FC have been partially adjusted for differences in the corresponding reporting standards and period-end exchange rates.
• 2The Criteria Value of Enterprise Performance Evaluation (CVEPE) was made by the Bureau of Financial Supervision and Evaluation of the State-owned Assets Supervision and Administration Commission of the State Council and published by Economic Sciences Press. This assessment standard applies the risk assessment of general global enterprises.

Funding

This research received no external funding.

Data availability statement

The research related data was not stored in publicly available repositories. Data will be made available on request.

CRediT authorship contribution statement

Mu Fan: Writing - review & editing, Writing - original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Xunan Chen: Conceptualization, Supervision, Validation. Bo Liu: Software, Methodology. Fei Zhou: Formal analysis, Data curation. Bo Gong: Supervision, Software, Conceptualization. Rancheng Tao: Supervision, Software, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.