Short-term management and performance: evidence from South European SMEs

Universidade do Minho Escola de Economia e Gestão Cláudia Rafaela Henriques Silva Short-Term Management and Performance: Evidence from South European SMEs may 2023 Short-Term Management and Performance: Evidence from South European SMEs UMinho|2023 Cláudia Rafaela Henriques Silva Universidade do Minho Escola de Economia e Gestão Cláudia Rafaela Henriques Silva Short-Term Management and Performance: Evidence from South European SMEs Master's Dissertation Master in Finance Master dissertation under the guidance of Professor Sónia Silva may 2023 ii DIREITOS DE AUTOR E CONDIÇÕES DE UTILIZAÇÃO DO TRABALHO POR TERCEIROS Este é um trabalho académico que pode ser utilizado por terceiros desde que respeitadas as regras e boas práticas internacionalmente aceites, no que concerne aos direitos de autor e direitos conexos. Assim, o presente trabalho pode ser utilizado nos termos previstos na licença abaixo indicada. Caso o utilizador necessite de permissão para poder fazer um uso do trabalho em condições não previstas no licenciamento indicado, deverá contactar o autor, através do RepositórioUM da Universidade do Minho. Licença concedida aos utilizadores deste trabalho Atribuição-NãoComercialSemDerivações CC BY-NC-ND h tt p s ://cr ea t iv ec o mm o n s .or g/ li c en s e s /b y - nc - nd /4 . 0 / iii Acknowledgments The accomplishment of this dissertation was one of the biggest challenges of all my academic life and because of that, I would like to take this opportunity to show my appreciation to everyone that in any way helped me to complete it. Firstly, I would like to thank my supervisor, Professor Sónia Silva, as she was fundamental throughout the process. Besides helping me finish this study, she also understood all my difficulties that came up along the way and was incredible and comprehensive when things were complicated. To my family, I want to thank them for all the financial and emotional support. Highlighting the part of my parents and grandparents that were with me in every step, giving me love, support, and encouragement to never give up. In particular, my grandfather António whom I lost during the process of this dissertation, was, and still is, a true inspiration for the type of person I want to be. Also, to my friends: Bruna Manuela, Francisca Camelo, Sofia Pereira, João Santos, Mário Guilherme, Jéssica Leitão, Mariana Almeida, Inês Pinto, and Carolina Ramon I would like to thank them for all their patience, support, and kind words when I needed them the most. Lastly, two of the people that were crucial throughout this process, were Miguel Monteiro, and Adriana Gaspar. To Miguel Monteiro, I would like to thank him for being a constant presence in every step of my life and being the best friend, I could ever ask for. About Adriana Gaspar, I would like to thank her not only for her friendship, understanding, and support every step of the way, but also, for encouraging me and helping me to overcome the difficulties that came along in this master's. iv STATEMENT OF INTEGRITY I hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledged the Code of Ethical Conduct of the University of Minho. v Short-Term Management and Performance: Evidence from South European SMEs Abstract The main goal of this dissertation is to examine the impact of working capital management on South European SMEs’ profitability. The return on assets (ROA) and the return on equity (ROE) are adopted as proxies for financial performance. Working capital management is measured using the net trade cycle. It was gathered a longitudinal panel data from Orbis Europe across the period between 2013 and 2021 for SMEs from Greece, Italy, Portugal, and Spain, analyzed using panel data methodologies. Consistent with previous literature, this study uncovered a negative relationship between profitability and working capital management. Moreover, it was also tested if the relationship between profitability and working capital management is non-linear; the results show evidence of a convex relationship for the group of smaller firms, meaning that insufficient investment in working capital might affect performance negatively. Keywords: Net Trade Cycle; Working Capital; Profitability; Working Capital Management; Small and Medium Enterprises vi Gestão de Curto Prazo e Desempenho: Evidência das PME do Sul da Europa Resumo O principal objetivo desta dissertação é examinar o impacto da gestão do fundo de maneio na rendibilidade das Pequenas e Médias Empresas (PME) do Sul da Europa. A rendibilidade do ativo (ROA) e a rendibilidade dos capitais próprios (ROE) são as proxies utilizadas para medir o desempenho financeiro. O indicador de gestão de fundo de maneio foi medido usando o Ciclo Financeiro de Exploração. Foi recolhido um painel longitudinal de dados da Orbis Europe entre 2013 e 2021 para as PME da Grécia, Itália, Portugal e Espanha, analisado utilizando metodologias de dados em painel. Consistente com a literatura anterior, este estudo revelou uma relação negativa entre a rendibilidade e a gestão de fundo de maneio. Além disso, também foi testada se a relação entre a rendibilidade e a gestão do fundo de maneio é não linear; os resultados mostram evidência de uma relação convexa para o grupo de empresas mais pequenas, o que significa que um investimento insuficiente em fundo de maneio pode afetar negativamente o desempenho. Palavras-chave: Ciclo Financeiro de Exploração; Fundo de maneio; Indicador de Gestão de Fundo de Maneio; Pequenas e Médias Empresas; Rendibilidade vii TABLE OF CONTENTS 1. Introduction .............................................................................................................1 2. Literature Review ......................................................................................................3 2.1 The Relationship between Working Capital Management and Corporate Profitability ...................................................................................................................3 2.2 Working Capital Management Indicators ..........................................................5 3. Hypotheses and Methodology ...................................................................................8 3.1 Hypotheses .........................................................................................................8 3.2 Methodology ......................................................................................................8 4. Sample Description .................................................................................................10 4.1 Data ...................................................................................................................10 4.2 Variables ..........................................................................................................10 4.2.1 Dependent Variables ...............................................................................10 4.2.2 Independent Variables .............................................................................11 4.2.3 Control Variables ....................................................................................11 4.3 Descriptive Statistics ........................................................................................11 4.4 Pearson’s Correlation .......................................................................................12 5. Empirical Results ....................................................................................................14 5.1 The impact of Working Capital Management on Profitability .........................14 5.2 The non-linear Relationship between Working Capital Management and Profitability .................................................................................................................16 5.3 Robustness Checks ...........................................................................................18 6. Concluding Remarks ...............................................................................................21 References ...................................................................................................................22 viii List of Tables Table 1 - Descriptive Statistics ...................................................................................12 Table 2 - Pearson’s Correlation Matrix ......................................................................13 Table 3 - Results from testing the linear relationship between Profitability and Working Capital Management ....................................................................................15 Table 4 – Results from testing a non-linear relationship between Profitability and Working Capital Management ....................................................................................17 Table 5 – Results from testing the (non) linear relationship between Profitability and Working Capital Management: Robustness Tests .....................................................19 Table 6 – Results from testing the (non) linear relationship between Profitability and Working Capital Management: Robustness Tests by SIZE ........................................20 6 The CCC proposes an approach to liquidity taking into account items collected from the balance sheet and income statement. Unlike the current ratio, which is calculated by dividing current assets by current liabilities, the CCC incorporates the period of time of firms' receivables, inventory, and payables turnover performance. The longer the time to exchange current assets into inflows, the more investment in cash and non-cash assets is needed. Overall, the shorter the CCC, the more healthy a company seems to be, which affects positively profitability (Richards & Laughlin, 1980). Amponsah-Kwatiah and Asiamah (2020) describe company profitability as the capacity to improve the decisions on the operating area and investment strategies toward financial stability. Tran et al. (2017) claimed that stable cash flows are crucial in order to continue a business but also that sufficient working capital can increase profitability. The CCC is calculated as the difference between the number of days of accounts receivable, plus the number of days of inventories, minus the number of days of accounts payable. Extending long credit terms for customers and granting large inventory holding reduces available cash to finance the working capital. Extending the number of days of inventories denotes additional costs in managing inventory and will negatively affect profitability if the revenues generated from large inventory do not cover the additional storing costs. On the other side, a longer number of days of accounts payable might favor firms’ profitability, although it also can damage their reputation. Previous literature (Le, 2019; Prasad et al., 2019; Ren et al., 2019; Fernández‐López et al., 2020; Alarussi & Gao, 2021) find that a larger CCC, i.e, a longer period between paying expenses and receiving inflows, is negatively associated with profitability, supported by the majority of previous research (79%) that found a negative significant relationship between the CCC and profitability. However, Amponsah-Kwatiah (2020) studied manufacturing firms in Ghana and uncover a positive relationship between CCC and ROA; the author defends that longer a CCC can increase profitability since implies a longer period of receivables and a number of days of inventories that overcome the payables period, which translates into an increase in sales. In addition, Amponsah-Kwatiah (2020) also believes that longer credit terms give extra time for customers to check the quality of the stock bought on credit, which could attract sales and eventually increase profitability. Besides, a longer period of inventory stored could drive economies of scale in order to reduce production costs per unit and prevents the loss of sales in case of stock-out, hence increasing profitability. Baños-Caballero et al. (2011) claim that their study proves a nonlinear relationship between the CCC and profitability, arguing that increasing the working capital ratio will 7 enhance profitability but only until it reaches its optimal point; thereafter the optimal point, investing in working capital will have a negative impact on profitability. Such evidence is supported by further studies, like the research conducted by Boțoc and Anton (2017). Besides the CCC, prior literature used the Net Trade Cycle (NTC) as a WCM. Soenen (1993) was the first to claim the advantages of the NTC as a more simplified and effective measure of working capital management (WCM) in comparison with the CCC. The NTC translates the number of days-sales companies need to convert their sales into cash. Therefore, the NTC plays a crucial role in working capital management. Optimizing the net trade cycle involves increasing days of payables outstanding, reducing days of inventory outstanding, and reducing days of sales outstanding. While a negative net trade cycle, where the company receives payments before paying its accounts payable, may seem ideal, its impact on profitability should be taken into consideration. Ultimately, achieving an efficient NTC improves internal operations, profitability, market value, and the net present value of cash flows. The net trade cycle mirrors the efficiency of a company's working capital management. A shorter net trade cycle indicates that the company is able to convert its investments in inventory and resources into cash briefly, which can lead to improved profitability. By minimizing the time between cash outflows and cash inflows, a company can reduce the need for external financing and associated costs. Taking all of this into consideration, the NTC is the measure of working capital management chosen to perform this study. 8 3. Hypotheses and Methodology 3.1 Hypotheses The main purpose of this dissertation is to analyze the impact of working capital management on SMEs' performance in Italy, Portugal, Greece, and Spain. The Net trade cycle (NTC) proxies the working capital management indicator and the performance indicators used in this study are return on assets (ROA) and return on equity (ROE). Following previous literature, (one example is García-Teruel & Martínez-Solano, 2007), the following hypothesis is formulated: Hypothesis 1 – There is a negative relationship between a firm´s profitability and working capital management. The seminal paper of Baños-Caballero et al. (2011) found a non-linear relationship between profitability and working capital. The authors provide evidence that companies have an optimal point of working capital management that maximizes profitability. Hence, regarding the main objective of this study, the next hypothesis is the following: Hypothesis 2 – There is an optimal point of working capital management that maximizes corporate profitability. 3.2 Methodology In order to test the research hypotheses formulated in the previous section, it was applied panel data methodologies. This is a longitudinal study, i.e., several number of enterprises are studied at the same time over a period of time (i.e., a panel database). Each company has several specific characteristics, which increases the sample heterogeneity. According to Baum (2006), the specific firms’ characteristics are fixed over time, although they can also be random. To analyze if those effects are random or fixed will be applied the Hausman test. Specifically, the null hypothesis of this test postulates that effects are random; if the null hypothesis is rejected, the effects are considered to be fixed. The fixed effects model assumes that individual-specific effects are correlated with the independent variables, while the random effects model assumes that individual-specific effects are uncorrelated with the independent variables. The Hausman test measures the difference between the two sets of coefficients, taking into account their respective standard errors. If the difference is statistically significant, meaning the significance level 9 is lower than 0.05, then the fixed effects model is preferred. If the difference is not statistically significant, then the random effects model is the model that fits best 2 . The fixed effects model (FE) is a statistical technique used in panel data analysis to account for individual-specific effects that remain constant over time. The model is used to estimate the relationship between a dependent variable and one or more independent variables while controlling for unobserved individual-specific effects. Its main characteristic is that fixed effects are individual-specific and remain constant over time (Baum, 2006). In addition, the FE model captures unobserved individual-specific factors that affect the dependent variable. One of the FE assumptions is that effects that are invariant over time are (potentially) correlated with the independent variables and need to be included in the model to avoid biased estimates. However, the fixed effects model cannot be estimated for individuals that have no within-group variability Baum (2006). Nevertheless, the unobservable heterogeneity in the fixed effects model is potentially correlated with the independent and control variables, which is a form of endogeneity. If this is the case, that means the independent variables are being affected by the dependent variable, and not vice versa. Endogeneity problems can be mitigated using instrumental variables (IV). This method provides a consistent estimate, supposing the existence of valid instruments. FE models assume a limited form of endogeneity, i.e., it is assumed that the observations on the same company in two different time periods can be correlated, but the observations between two different corporations are not. In this context, previous literature assumed as valid instruments the independent variables lagged one or more periods (García-Teruel & Martínez-Solano, 2007). Moreover, a longitudinal panel is potentially affected by the presence of heteroskedasticity and serial correlation. Following Baum (2006), one way to overcome those issues is the adoption of clustered robust standard errors. 2 In the case the effects are fixed, the appropriate estimator is the OLS (ordinary least-squares method); on the other hand, for random effects models, the proper estimator is the GLS (generalized least-squares method). The purpose of the OLS method is to find the line that best fits the data, in terms of minimizing the sum of squared differences between the observed and predicted values. The GLS provides a more efficient estimator of the parameters of a linear regression model in the presence of correlated errors. 10 4. Sample Description 4.1 Data The information needed to perform this study was retrieved from Orbis Europe across the period 2013-2021. Consistent with the main goal of this study, it was collected data about total assets, sales, receivables, payables, inventories, current and non-current liabilities, net income, and shareholders’ equity. The first step relied on selecting only active private limited companies, partnerships, and sole trader/proprietorships from South European countries: Greece, Italy, Portugal, and Spain. Regarding the industry, it included all NACE codes (reduced to a 2-digit code), except banks, extremely regulated, state-owned, and traded companies. Taking into account the aim point of this study – to examine the impact of working capital management on the profitability of South European SMEs - the size of firms was defined according to the parameters established by the EU Recommendation of 6th May of 2003 about SMEs: the number of employees varies between 10 to 250, and total assets lower than 43 million euros. The dataset was empirically treated in STATA where anomalies such as lack of observations on the main variables - total assets, sales, receivables, payables, inventories, current and non-current liabilities, and shareholders’ equity were eliminated. Besides, in order to limit the extreme values, all variables were winsorized at a 1% level. This screen resulted in about 90,000 companies, counting 820,708 observations. 4.2 Variables According to previous literature (e.g., García-Teruel & Martinez-Solano, 2007; Nazir & Afza, 2009; Baños-Caballero et al., 2016; Pais & Gama, 2015; Leal et al., 2022), and in order to test the hypotheses formulated before, the following variables were included in this study, divided into dependent, independent and control variables. 4.2.1 Dependent Variables Following prior literature, the proxies for performance are profitability variables: Return on Assets (ROA) and Return on equity (ROE). ROA is the ratio between net income and total assets. This ratio is important in order to understand if the company is using its resources efficiently, 11 ROE is the ratio between net income and shareholders’ equity. This ratio reflects if the company is returning profits to its shareholders. 4.2.2 Independent Variables The working capital management (WCM) indicator used in this study is the Net Trade Cycle (NTC). This indicator measures the time it takes a company to transform its net working capital into cash. Therefore: NTC= Days-Sales Receivable Outstanding (DRO) + Days-Sales of Inventory Outstanding (DIO) – Days-Sales Payable Outstanding (DPO), where DRO is the time (in days) it takes a company to receive from its debtors: DRO= (Debtors/Turnover) *365; DIO means the time it takes for a company to sell its inventory, DIO = (Inventories/Turnover) *365; DPO measures, in days, the time it takes for a company to pay back to its creditors: DPO= (Creditors/Turnover) *365. Several prior studies (e.g., Deloof, 2003) uncover a negative relationship between profitability and working capital management components DRO, DIO, and DPO, respectively, from which authors conclude that shortening those WCM components might affect profitability positively. 4.2.3 Control Variables Once, control variables are based on former studies (e.g., García-Teruel & MartínezSolano, 2007), which are the following: • Size is measured as the total assets’ logarithm; • Sales Growth measures the increase or decrease of sales during a certain period: Sales Growth= (Turnover year n/ Turnover year n-1) – 1. There is evidence based on previous literature of a positive relationship between sales growth and profitability, e.g., Deloof (2003), and Liu and Zhao (2014) suggest that sales growth is fundamental on determine the use of trade credit by a company; • Leverage measures the total liabilities used by a company in order to accomplish its obligations: Leverage = Total Liabilities/ Total Assets. 4.3 Descriptive Statistics Table 1 reports descriptive statistics for dependent, independent, and control variables (already defined in the previous section) of the sample, during the period 2013-2021. 12 Table 1 - Descriptive Statistics Variable Obs. Mean Median Std. Dev. ROA 813,171 0.0949 0.0790 0.0948 ROE 820,446 0.0956 0.0791 0.3606 NTC 819,986 81.75 60.34 96.18 SIZE 820,708 7.98 7.97 1.21 Sales Growth 732,630 0.0413 0.0100 0.2243 Leverage 820,305 0.6369 0.6644 0.2511 As can be seen in Table 1, the descriptive statistics show that ROA is, on average, 9.5 percent while ROE is slightly higher at approximately 9.6 percent. The mean size of firms is about 2,925 million euros, meaning this sample is formed of small firms. In what concerns the firm’s sales growth (SG) is, on average, 4.1 percent annually. The Net Trade Cycle (NTC) displays a mean value of 82 days-sales, far from the median of 60 dayssales. Consistent with previous research (e.g., Pais & Gama, 2015) the Leverage ratio displays a mean of 64%, meaning that more than half of SMEs’ total assets are financed by liabilities. 4.4 Pearson’s Correlation Table 2 demonstrates Pearson s correlation coefficients and their significance levels across all variables used in the subsequent multivariate analysis between the period of 2013 and 2021. This table reports descriptive statistics between the period of 2013 and 2021. The descriptive statistics used are the following: number of observations, mean, median, and standard deviation. In order to perform this analysis, it was used the following variables. ROA = Net Income/Total Assets; ROE= Net Income/Shareholders; NTC= Days Receivable Outstanding + Days of Inventory Outstanding – Days Payable Outstanding; SIZE=ln (Total Assets); Sales = (Turnover year n/ Turnover year n-1) – 1; Leverage = Total Liabilities/ Total Assets. 13 Table 2Pearson’s Correlation Matrix Most of the correlation coefficients reported in Table 2 display statistical significance, at least, at the 5 percent level. As expected, i.e., based on previous literature findings (e.g. Baños-Caballero et al., 2016; Pais & Gama, 2015; Leal et al., 2022), profitability measures – ROA and ROE – are negatively correlated with the WCM indicator – NTC, and also negatively correlated with Size and Leverage variables, being positively correlated with Sales Growth. The negative relation between profitability and NTC length denotes that a longer NTC impacts negatively firms’ performance. Despite the evidence presented in Table 2, the correlation analysis does not differentiate between causes from consequences. Therefore, is not possible to conclude whether the NTC influences profitability or vice versa, only regression analysis allows to provide such evidence. Thus, the next Chapter will present the empirical results from regression analysis. Variables (1) (2) (3) (4) (5) (6) (1) ROA 1 (2) ROE 0.4253* 1 (3) NTC -0.1532* -0.0762* 1 (4) SIZE -0.0542* -0.0197* 0.238* 1 (5) Sales Growth (SG) 0.0286* 0.0256* -0.0123* 0.0948* 1 (6) Leverage -0.2447* -0.0145* -0.088* -0.1216* -0.0008 1 This table synthesizes the results from Pearson’s correlation between the period 2013 and 2021. * provides statistical significance, at least, at the 5% level. To perform this analysis, it was used the following variables ROA = Net Income/Total Assets; ROE= Net Income/Shareholders; NTC= Days Receivable Outstanding + Days of Inventory Outstanding – Days Payable Outstanding; SIZE=ln (Total Assets); Sales = (Turnover year n/ Turnover year n-1) – 1; Leverage = Total Liabilities/ Total Assets. 14 5. Empirical Results 5.1 The impact of Working Capital Management on Profitability According to previous findings (García-Teruel & Martínez-Solano, 2007; Pais & Gama, 2015; Leal et al., 2022), the relationship between working capital management and profitability is expected to be negative, meaning that decreasing the WCM length has a positive impact on firms’ performance. To test Hypothesis 1, i.e., there is a negative relationship between a firm´s profitability and working capital management, it will be followed previous literature and test equations (1) and (2). 𝑅𝑂𝐴𝑖,𝑡 = 𝛽0+ 𝛽1𝑁𝑇𝐶𝑖,𝑡 + 𝛽2𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽3𝑆𝐺𝑖,𝑡 + 𝛽4𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖,𝑡 + 𝜆𝑡+ η𝑖+ 𝜀𝑖,𝑡 (1) 𝑅𝑂𝐸𝑖,𝑡 = 𝛽0+ 𝛽1𝑁𝑇𝐶𝑖,𝑡 + 𝛽2𝑆𝐼𝑍𝐸𝑖,𝑡 + 𝛽3𝑆𝐺𝑖,𝑡 + 𝛽4𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖,𝑡 + 𝜆𝑡+ η𝑖+ 𝜀𝑖,𝑡 (2) Where ROA and ROE are the dependent variables. The independent variable – NTC - is a proxy for working capital management, as described in section 4.2.2. The control variables – Size, Sales Growth (SG), and Leverage – are described in section 4.2.3. 𝜆𝑡 is a set of time dummies that captures time effects. η𝑖 measures the unobservable heterogeneity of the individual specific effects invariant over time that can be related to firm, country and/or industry characteristics. 𝜀𝑖,𝑡 is the error term. Table 3 reports the results from testing equations (1) and (2) – a linear relationship between profitability and working capital management - as formulated by Hypothesis 1. 15 Table 3 - Results from testing the linear relationship between Profitability and Working Capital Management As can be seen in Table 3 above, the null hypothesis of the Hausman test is rejected, meaning that the effects arising from the sample are fixed. Models (3)-(6) control for those fixed effects in different schemes (as described in Table 3). Moreover, most of the coefficients’ estimates of the variable of interest – NTC - are negative (except in model (3)) and statistically significant at the 1 percent level, providing evidence of a negative relationship between working capital management and profitability. Taking models (5) and (6) as examples, an increase of one day in the net trade cycle leads to a decrease of 0.02% in ROA and 0.03% in ROE, holding all else equal. Regarding control variables, SIZE displays inconsistent results across models. As signaled by correlation analysis, the relationship between profitability and sales growth is positive and statistically significant. As already expected, Leverage has a negative Methodology: Pooled OLS Pooled OLS FE FE FE FE Dependent variable: ROA ROE ROA ROE ROA ROE Model: (1) (2) (3) (4) (5) (6) Net Trade Cycle -0.0002*** -0.0003*** 0.0000*** -0.0001*** -0.0002*** -0.0003*** (-90.26) (-47.14) (2.70) (-4.09) (-86.52) (-50.39) Size -0.0037*** -0.0005 0.0012* 0.0161*** -0.0037*** -0.0003 (-19.01) (-1.00) (1.78) (5.90) (-16.95) (-0.54) Sales Growth (SG) 0.0131*** 0.0393*** 0.0068*** 0.0158*** 0.0111*** 0.0262*** (27.24) (20.36) (15.99) (7.52) (21.22) (12.48) Leverage -0.1023*** -0.0304*** -0.1921*** -0.1044*** -0.1130*** -0.0430*** (-111.28) (-11.49) (-105.58) (-12.33) (-114.72) (-14.98) Constant 0.2025*** 0.1440*** 0.1996*** 0.0431** 0.1918*** 0.1212*** (116.72) (31.02) (35.38) (1.98) (76.21) (16.89) Observations 725,225 731,912 725,225 731,912 725,225 731,912 R-squared 0.097 0.007 0.093 0.006 0.133 0.016 Firm FE Yes Yes Year FE Yes Yes Yes Yes Country FE Yes Yes Industry FE Yes Yes Hausman Test (P-value)) 0,0000 0,0000 This table reports the results from the regression analysis using Pooled OLS and the Fixed Effects (FE) methodologies for the period of 2013 and 2021. ***, ** and * demonstrate the statistical significance at the levels of 1%, 5% and 10% correspondingly. To perform this analysis, it was used the following variables. ROA = Net Income/Total Assets; ROE= Net Income/Shareholders Equity; Net Trade Cycle= (Days Receivables Outstanding + Days of Inventory Outstanding – Days Payable Outstanding) *365; SIZE=ln (Total Assets); Sales = (Turnover year n/ Turnover year n-1) – 1; Leverage = Total Liabilities/ Total Assets. The variable constant corresponds to the intercept term. Robust tstatistic is in parentheses, clustered by firm. The Hausman test is performed to compare the consistency and efficiency of a fixed-effects versus random-effects model; P-value from this test is in parentheses. R square is expressed in percentage. Models (3)-(4) control for firm/year FE. Models (5)-(6) control for country/industry/year FE. The industry is a 2-digit NACE code. 22 References Agyei-Boapeah, H., Machokoto, M., Amankwah-Amoah, J., Tunyi, A., & Fosu, S. (2020, July). IFRS adoption and firm value: African evidence. 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