The financial performance of European companies: Explanatory factors in the context of economic crisis

Madaleno, Mara; Bărbuţă-Mişu, Nicoleta Article The financial performance of European companies: Explanatory factors in the context of economic crisis Ekonomika Provided in Cooperation with: Vilnius University Press Suggested Citation: Madaleno, Mara; Bărbuţă-Mişu, Nicoleta (2019) : The financial performance of European companies: Explanatory factors in the context of economic crisis, Ekonomika, ISSN 2424-6166, Vilnius University Press, Vilnius, Vol. 98, Iss. 2, pp. 6-18, https://doi.org/10.15388/Ekon.2019.2.1 This Version is available at: https://hdl.handle.net/10419/323071 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ 6 Ekonomika ISSN 1392-1258 eISSN 2424-6166 2019, vol. 98(2), pp. 6–18 DOI: https://doi.org/10.15388/Ekon.2019.2.1 The Financial Performance of European Companies: Explanatory Factors in the Context of Economic Crisis Mara Madaleno University of Aveiro, Portugal Nicoleta Bărbuţă-Mişu* “Dunărea de Jos” University of Galati, Romania Abstract. Financial accounting information plays an important role in assessing and forecasting firms’ financial performance. But besides that, there are other external factors affecting the performance of firms, such as economic and financial crises, which cause imbalances over the economy and affects the business environment. Thus, based on financial statements data, in this paper, the determinants of financial performance are examined, and the impact of a financial crisis on these factors is analyzed, using the fixed and random effects panel estimators. A sample of non-financial firms from European countries considering annual data for the period of 2006 to 2015 was used for this research. The results achieved by panel data analysis show that a crisis exerts a significant positive effect over financial performance as well as liquidity, assets turnover, and labor productivity, meaning that firms tend to put in greater efforts to maintain financial performance in the face of a crisis. Financial performance is significantly and negatively influenced by leverage independently of the crisis effect, showing return on assets to be lower than the average interest rate. Keywords: Return on Assets; Return on Equity; Leverage; Liquidity; Crisis. Received: 31/07/2019. Revised: 28/8/2019. Accepted: 10/10/2019 Copyright © 2019 Constantinos Challoumis. Published by Vilnius University Press This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Contents lists available at Vilnius University Press * Corresponding author: “Dunărea de Jos” University of Galati, 47 Domneasca Street, 800 008, Romania Email: [email protected]; Phone No.: +40-336-130-242 Acknowledgments: This work was supported by the project “Excellence, Performance, and Competitiveness in the R&D&I activities at ‘Dunarea de Jos’ University of Galati,” acronym “EXPERT,” financed by the Romanian Ministry of Research and Innovation, Programme 1 – Development of the national research and development system, Sub-programme 1.2 – Institutional Performance – Projects for financing excellence in R&D&I, Contract No. 14PFE/17.10.2018 and by the SOP IEC, under Grant SMIS-CNSR 1815-48745, No. 622/2014. We would like to thank the editor and the reviewers for their valuable comments which helped us to improve the present article. Mara Madaleno, Nicoleta Bărbuţă-Mişu. The Financial Performance of European Companies 7 1. Introduction In a company, the accounting information provided by financial statements foresees the critical judgments that influence the decision to facilitate information for management control (Bushman, Chen, Engel, & Smith 2004). The accounting information has, as a starting point, two different and uneven sources in terms of quality: (i) information coming as a result of transactions carried out on various markets being seen during the financial year, having a rigorous and objective character, and (ii) information known at the end of the year that is largely the result of estimates and reflects the accounting policies of the firm’s management (Danos, Holt, & Imhoff 1989). Managers primarily use accounting information to develop knowledge of their work environment in order to prepare for future decisions and activities (Hall 2010). For investors, accounting information has a valuation role, providing them with information for making informed investment decisions. Private-equity firms require accounting information to control the conflicts of interest both within the private-equity firm and among their investors (Zimmerman 2015). Also, the role of accounting information can be viewed as shaping user perceptions by providing new evidence that the descriptive valence of accounting constructs can impact consumer purchase decisions (Tian & Zhou 2015). The usefulness of accounting information increases if it is comparable, verifiable, readily available, and understandable. But the relevance and faithful representation of accounting information are considered to be fundamental qualitative characteristics considering their use in calculation of a wide range of indicators in financial analysis and diagnosis of financial performance. The financial performance shows the success of a company and the attractiveness of shares on financial markets. It helps investors in making investment decisions in stock markets and managers in making financial decisions (investment, funding and profit distribution) for firm development. Financial performance is usually measured through return on equity (Khan, Khan, & Azad 2015) and return on assets (Nawaz, Salman, & Shamsi 2015), return on sales (Waddock & Graves 1997), return on capital employed (Chetty, Naidoo, & Seetharam 2015), and earnings per share (Cochran & Wood 1984). Financial performance is affected by certain factors, such as liquidity, ownership, age, and size (Deitiana & Habibuw 2015), leverage, assets turnover, capital cost, etc. But other factors may affect financial performance as well, and these may even change in face of economic crisis. The quality of accounting information can reduce a firm’s exposure to systematic liquidity risks and may affect its valuation and capital cost through their impact on different aspects of liquidity, especially when particular events, such as an economic crisis, may arise. Thus, the important role of accounting information during liquidity events is confirmed (Sadka 2011). An economic and financial crisis causes imbalances over the economy and affects the business environment. By spreading to other countries, companies’ financial conditions and performance will be affected on a micro level. Thus, this work also intends to analyze ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2019, vol. 98(2) 8 the factors that influence financial performance accounting for two different financial performance measures; this is done to ensure different model specification effects and taking into account the financial crisis period of 2007–2009. The decline of capital markets, a recession in the housing market, and a rise in unemployment have been observed in many countries across the world, where the UE-28 countries were no exception. Besides accounting for different factors that able to affect financial performance, this research intends to observe which factors change their influence in the face of a economic and financial crisis, taking into account two financial measures: return on equity (ROE) and return on assets (ROA). The present article is distinguished from previous studies in several different ways: (i) by dealing with a larger data set of accounting data on large non-financial companies from the EU-28 countries; (ii) by reliable results achieved from analyzing indicators calculated based on standardized accounting information, as provided by the Amadeus Database; (iii) by including a dummy Crisis as an exogenous variable to account for the effects of the financial and economic crisis felt by European countries in 2007 and mostly 2009; (iv) by using an econometric approach that allows for a simultaneous interaction among variables and panel models considering random and fixed effects that allow us to identify heterogeneous influencing variables and to validate the main factors of influence. The methodology allows to infer the relations between them and the dependent variables, offering a regression-type relation that could also be used for future forecasts and for understanding the existent dynamics. The rest of the article develops as follows. Section 2 presents the relevant literature and our study hypotheses. Section 3 presents the methodology and data, while in Section 4 we present and discuss the achieved results. Section 5 concludes this work by pointing out some policy implications. 2. Relevant Literature and Hypotheses In this article, we account for factors that are able to influence financial performance in non-financial large firms from the EU-28 countries, taking into account the crisis period effect. We may think about many risk factors that able to affect the financial performance of firms, but we selected only some. Except for crisis, the variables selected are firm, specific factors that have been shown to be significant determinants of firm performance (Egbunike and Okerekeoti 2018). Liquidity may be interpreted as the degree to which an asset can be converted into cash, depending on the asset demand and supply. Liquidity risk is also one of the major causes of financial crises and should thus be considered as an important factor in financial performance. A high Liquidity shows the financial strength of the company, and in the literature, we found a significant positive relationship between Liquidity variables and the profitability of the firm (Holz 2002; Khidmat & Rehman 2014; Lasisi et al. 2017). Mara Madaleno, Nicoleta Bărbuţă-Mişu. The Financial Performance of European Companies 9 Thus, companies that are liquid are able to meet short-term obligations, and a company with a higher current ratio indicates that it will have better performance. Therefore, it will be able to face any shortor long-term financial problems. The opposite is true for weak companies; they have a low current ratio because they have less liquidity (Ramli et al. 2019). As Europe was affected by the financial crisis first, we expect a significant impact relationship between Liquidity and performance. The asset turnover ratio is a measure of how efficiently a company’s assets generate revenue. In general, a low asset turnover ratio suggests problems with excess production capacity, poor inventory management, or weak collection receivables methods. Increases in the asset turnover ratio over time may indicate the company’s growth and a high profitability. Also, firms with high labor productivity are more performant. In this sense, we formulate the first hypothesis: H1: Financial performance is positively influenced by Liquidity, Assets turnover, and Labor productivity. Profitability and Solvency are the results of mediumand long-term strategies of firm development. Since the higher the leverage, the more in-depth is the control undertaken by lenders, there is a positive relationship between Leverage and firm performance (López and Lima 2010). It means that firms with higher financial leverage perform better due to the positive leverage effects, manifested when the ROA is higher than the average interest rate. A high leverage means also a high capital repayment ratio and a low Solvency calculated as total assets divided by total debts. There are many studies developed in diverse sectors and countries that highlighted a positive relation between profitability and leverage (Roden & Lewellen 1995; Odit & Gobardhun 2011; Nawaiseh 2015; Ramli et al. 2019). As such, we formulate the second hypothesis: H2: ROE is positively correlated with the degree of debts. This hypothesis is explained by a positive correlation of ROE with financial leverage and capital repayment ratio and an inverse correlation with Solvency. We found in the literature that the debt to equity ratio has a statistically significant inverse impact on the ROA (Al-Qaisi 2010; Chen 2004; Khidmat & Rehman 2014; Majumdar & Chhibber 1999; Nawaz et al. 2015; Lasisi et al. 2017). Morandi and Paulet (2019) found a significantly negative relation between profitability and the debt to equity ratio, while Ibhaguia and Olokoyob (2018), while studying small-sized firms from Nigeria, found that the negative effect of leverage on firm performance is most eminent and significant in these companies. Their study shows that the evidence of a negative effect of leverage diminishes as a firm grows, eventually vanishing when the firm’s size exceeds its estimated threshold level. Considering these findings, we study this relationship in large European companies, and we consider the third hypothesis: H3: ROA is negatively correlated with the degree of debts. This hypothesis is explained by an inverse correlation of ROA with the financial leverage and capital repayment ratio and a direct correlation with Solvency. ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2019, vol. 98(2) 10 Finally, the direct impact of the financial crisis was a reduction in profitability. The companies immediately responded to the crisis with more prudent financial management; curtailing expenses, cutting dividends, reducing bank borrowings, increasing equity; and to the extent of disposing of assets to mitigate losses (Ying Lai et al. 2014). With respect to the relationship between crisis and financial performance ratios, Seilsepoor & Ahmadi (2016) showed that the profitability ratio, leverage ratio, liquidity ratio, and activity ratio are the main indexes with respect to the impact of the financial crisis of 2007. They found that return on asset, return on equity, the current ratio, the leverage ratio, and the asset turnover ratio were more affected by the financial crisis. So, we formulate our fourth hypothesis: H4: A crisis changes the effects of independent variables on financial performance. Because our goal is to find whether crises influenced ROE and ROA, and if a crisis changes the effects of factors over financial performance, we will analyze each of the first three hypotheses concomitantly with the fourth hypothesis. 3. Data Description and Methodology 3.1. Variables and Their Description Variables selected for the study are described in Table 1. We analyzed the dependent variables (ROE and ROA), which were regressed using the following independent variables: financial leverage (Fin. Lev.), Liquidity ratio (Liquidity), Solvency ratio (Solvency), assets turnover ratio (Ass. Turn.), capital repayment ratio (Cap. Repay.), labor productivity (Labor Prod.), and the exogenous dummy crisis (Crisis). Table 1. The described variables. Description Abbreviation Calculation ROE (%) ROE Net income / Shareholder funds ROA (%) ROA EBIT / Total Assets Financial Leverage (%) Fin. Lev. Total Liabilities / Shareholders funds Liquidity ratio Liquidity (Current assets – Inventories) / Current liabilities Total Solvency ratio Solvency Total assets / Total liabilities Asset turnover ratio Ass. Turn. Turnover / Total assets Borrowed capital repayment ratio Cap. Repay. Financial long-term debts / Self-financing capacity Labor productivity (ThEur/employee) Labor Prod. (Turnover/1000) / Number of employees Crisis Crisis Dummy: 2006 and 2010–2015 = 0 and 2007–2009 = 1 Note: Self-financing capacity = Net income + Depreciation and amortization. Source: Amadeus Database provided by Bureau van Dijk Electronics; calculations made by the authors. Mara Madaleno, Nicoleta Bărbuţă-Mişu. The Financial Performance of European Companies 11 3.2. Model Specification The model used for estimation follows a panel data approach (an unbalanced panel), combining time-series with cross-section data, analyzed through time (years) and for several companies. The standard static model with i = 1,…,N, t = 1,…,T is presented in Eq. 1: yit = β0 + X' i t β + εit . (1) where yit will represent our dependent variables, X' i t is a K-dimensional vector of explanatory variables without a constant term, β0 is the intercept, independent of i and t, β a (K×1) vector the slopes, i and t are also independent, and εit is the error that varies over i and t, where i refers to the company and t to the year. Individual characteristics, which do not vary over time, zi, may also be included, as is presented in Eq. 2: yit = β0 + X' i t β1 + z'i β2 + εit . (2) Moreover, when using panel data, we may use two basic models, one of fixed effects and another of random effects. To find which model is more appropriate, we have performed the Hausman test (Hausman 1978). Having rejected the null (p-value < 0.05) takes us to the conclusion that random effects are inconsistent and that fixed effects are the most adequate model (Kyereboah-Coleman 2007; Manole & Spatareanu 2010). Then, we have run the panel vector autoregression models to examine empirically the interaction between company performance measures (one for ROE and one for ROA) and both financial and non-financial measures accounting for crisis effects. 3.3. Data Description Our data source is the Amadeus Database, provided by Bureau van Dijk Electronics. Large, non-financial companies from the EU-28 countries were selected that operated across all sectors of activity during the period 2006–2015. The selection criteria applied for the year 2015 were the following: a number of employees greater than 250, total assets greater than €43 million, and turnover greater than €50 million. We have found 22 581 active companies matching these criteria. After checking data for availability, inconclusive values, and outliers, 106 510 valid year-observations remained in the study. We have organized the data as a panel, and we have obtained an unbalanced panel. The data descriptive statistical analysis and correlation values are presented in Tables 2 and 3. As reported in Table 2, the average ROE (13.9%) is higher than the average ROA (7.4%), which shows the positive effect of leverage on ROE (ROE increases when leverage increases). The firms are marked by average performance and registered on average high levels of Solvency (2.07), Liquidity (1.27), and Asset turnover (1.55), which shows that firms have the capacity to pay debts. ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2019, vol. 98(2) 12 Table 2. Data descriptive statistics. Variable Obs Mean Std. Dev. Min Max ROE 106,510 0.1388 0.2163 -0.9996 1.0000 ROA 106,510 0.0742 0.0935 -0.9005 0.9750 Fin. Lev. 106,510 2.0684 1.8384 -2.9977 9.9993 Liquidity 106,510 1.2716 0.8531 0.0000 4.9984 Solvency 106,510 2.0668 1.1390 0.6601 9.9697 Ass. Turn. 106,510 1.5487 1.0130 0.0102 9.9235 Cap. Repay. 106,510 0.7342 1.2545 -5.0000 5.0000 Labor Prod. 106,510 0.3978 0.6348 0.0001 9.9858 Crisis 106,510 0.2677 0.4428 0.0000 1.0000 Source: performed by the authors, based on data extracted from the Amadeus Database. Table 3. Pearson correlation coefficients for all firms. ROE ROA Fin. Lev. Liquidity Solvency Ass. Turn. Cap. Repay. Labor Prod. Crisis ROE 1 ROA 0.701*** 1 Fin. Lev. 0.091*** -0.136*** 1 Liquidity 0.044*** 0.154*** -0.335*** 1 Solvency -0.091*** 0.071*** -0.576*** 0.492*** 1 Ass. Turn. 0.163*** 0.158*** 0.212*** -0.112*** -0.247*** 1 Cap. Repay. 0.005* -0.076*** 0.096*** -0.092*** -0.167*** -0.126*** 1 Labor Prod. 0.036*** 0.017*** 0.079*** -0.046*** -0.074*** 0.239*** -0.021*** 1 Crisis 0.014*** 0.007** 0.029*** -0.015*** -0.034*** 0.026*** 0.002 -0.004 1 Source: performed by the authors, based on data extracted from the Amadeus Database; Notes: The table displays the Pearson correlation coefficient values, variables descriptive statistics are shown above the correlation values, while ***, **, and * indicate a significant at 0.01, 0.05, and 0.10 level, respectively. Also observable is a high correlation between the two financial performance measures, a negative correlation between Solvency and all other variables except ROA and Liquidity. ROE is only negatively correlated with Solvency, while ROA with Fin. Lev. and Cap. Repay. The correlation between Crisis and Liquidity, Solvency and Labor Prod. is negative, being positive with respect to ROE and ROA. 4. Results and Discussions The results obtained by panel estimations for both financial performance measures and using both random and fixed effects are presented in Table 4, with the data coefficients and statistical significance attained considering all firms in the sample. Results from the Hausman test are presented in Table 5. Despite the fact that for all model specifications the Hausman test results reveal that the fixed effects model is the one to be applied, we decided to present both model estimation results in Table 4. Mara Madaleno, Nicoleta Bărbuţă-Mişu. The Financial Performance of European Companies 13 Table 4. Panel data estimations for all firms in the sample, considering fixed effects and random effects. Independent variables - All companies Fin. Lev. Liquidity Solvency Ass. Turn. Cap. Repay. Labor Prod. Crisis Constant ROE Fixed Effects Coef. -0.006 0.014 -0.007 0.067 0.013 0.032 0.008 0.020 F-test 435.38 t-stat -10.88*** 10.91*** -6.37*** 43.25*** 21.39*** 14.28*** 7.37*** 5.05*** p-value 0.000 Random Effects Coef. -0.002 0.021 -0.010 0.050 0.011 0.014 0.007 0.045 Wald chi2 3315.69 z-stat -4.19*** 19.48*** -11.08*** 46.24*** 20.01*** 8.93*** 6.41*** 13.04*** p-value 0.000 ROA Fixed Effects Coef. -0.008 0.009 0.001 0.042 0.000 0.016 0.002 0.005 F-test 1249.65 t-stat -34.93*** 18.59 1.69* 72.84*** 0.76 18.58*** 4.59*** 3.28*** p-value 0.000 Random Effects Coef. -0.008 0.011 0.000 0.031 0.000 0.007 0.002 0.025 Wald chi2 8813.03 z-stat -40.95*** 26.47*** -0.53 70.91*** -0.8 10.82*** 4.46*** 18.26*** p-value 0.000 ROE Fixed Effects Coef. -0.006 0.014 -0.007 0.067 0.013 0.031 0.022 F-test 498.58 t-stat -10.52*** 10.83*** -6.54*** 43.50*** 21.42*** 13.90*** 5.58*** p-value 0.000 Random Effects Coef. -0.002 0.021 -0.010 0.050 0.011 0.014 0.047 Wald chi2 3273.36 z-stat -3.95*** 19.43*** -11.2*** 46.39*** 20.04*** 8.69*** 13.58*** p-value 0.000 ROA Fixed Effects Coef. -0.007 0.009 0.001 0.042 0.000 0.015 0.005 F-test 1454.08 t-stat -34.74*** 18.54*** 1.59 73.03*** 0.79 18.36*** 3.61*** p-value 0.000 Random Effects Coef. -0.008 0.011 0.000 0.031 0.000 0.007 0.026 Wald chi2 8791.47 z-stat -40.80*** 26.43*** -0.61 71.03*** -0.78 10.65*** 18.62*** p-value 0.000 Source: performed by the authors, based on data extracted from the Amadeus Database; Notes: The table displays the coefficient of independent variables, t-statistic are shown in the lines below coefficients, while ***, **, and * indicate a significant at 0.01, 0.05, and 0.10 level, respectively.