MSc. Finance Thesis
Do Firms Manage Their Working Capital Optimally?
An Analysis of the European Market
By R.A. van Seventer
1
Keywords:
Working Capital Management, Cash Conversion Cycle, Corporate
Profitability.
JEL Classifications:
G31, G32 and G34
Date:
January 12, 2016
Supervisor:
prof. dr. R.E. Wessels
I would like to thank all my colleagues at PwC (in particular Stefan, Arie and Martijn) for their time, support and interesting discussions. Most of all, I would like to thank prof. dr. Roberto Wessels for our constructive
Table of Contents
1. Introduction 4
1.1. The influence of working capital management on a firm’s profitability 4
1.2. The relevance of this paper 5
1.3. The contributions of this paper 5
1.4. The outline of the paper 6
2. Theoretical concepts 6
2.1. The relationship between the cash conversion cycle and profitability 6
2.2. The constituents of the cash conversion cycle 8
2.3. The market performance of firms 10
2.4. Other factors influencing working capital management 10
2.5. Literature review 11
3. Hypotheses 12
3.1. The null hypothesis: do firms manage their working capital optimally? 12
4. Empirical Model 13
4.1. Endogeneity and regression method 13
4.2. Profitability 14
4.3. Working capital management 14
4.4. Control variables 15
4.5. Regression equations 16
5. Results 17
5.1. First stage regression 18
5.2. Second stage regression 19
5.3. Reduced form regressions 20
5.4. Robustness and verification 20
6. Conclusions 22
6.1. Working capital management is not optimal 22
6.2. Future research 23
Literature 24
1.
Introduction
This thesis examines the relation between working capital management and a firm’s profitability. The subject of working capital management interests me because in recent years, firms focus on working capital management more than ever, with projects that deliver a negative level of working capital2. What is the gain? Is there a relationship between the level of working capital and the profitability of a firm? Research on this relationship is not new. However, when reading recent literature, I found flaws in the applied research methods. The two main arguments against the most recent papers is that they do not take endogeneity issues into accounts and the authors treat the proxy for working capital management as an exogenous variable. Furthermore, papers written before 2014 focus on one country and fail to investigate on a European level. This paper investigates prior findings, using a sound research method. If a firm manages its working capital optimally, then we should not expect to find any relation between gross operating income and the cash conversion cycle. The results in this paper indicate that working capital management is conditionally correlated with measures of the firm’s profitability and that working capital management is not optimal. Furthermore, firms can adjust exogenous factors in such a way that firms hold less working capital and therefore make the firm more profitable.
1.1.
The influence of working capital management on a firm’s profitability
Managers can make significant impact on their profitability by applying more efficient working capital management. As such, working capital management cannot be observed in the data and therefore, this paper relies on the cash conversion cycle, which paragraph 2.1 discusses in detail. Working capital management refers to management of current assets and current liabilities. The money invested in current accounts and inventory is used for operational purposes. Working capital has an influence on the profitability of a firm because of the large amount of cash current assets and inventories consumes. Consequently, a lower working capital level results in an opportunity to release cash with sustainability and within a relatively short period of time. It also lowers the direct cost associated with large accounts receivable and inventory, and the indirect cost of accounts payable. The shorter average period it takes for firms to receive cash for sales, the less money a firm needs to support day-to-day operations. Consequently, more cash is available for the
firm and this is used for three options; debt repayment, dividend payment or investments. First, lower debt levels make firms more flexible in financing needs. Moreover, lower debt results in lower interest payments. Second, shareholders value the firm more if dividend payments are larger. Lastly, money that is consumed by working capital can be invested in projects with a positive net opportunity.
1.2.
The relevance of this paper
As mentioned in paragraph 1.1, lowering the cash conversion cycle has potential to create value. However, there are also reasons why a firm might not want to decrease its working capital beyond a certain limit, since this can damage day-to-day operations and therefore lower efficiency and profitability of a firm. The paper discusses this trade-off, which results in a firm specific optimal level of working capital management in paragraph 2.2. When firms come to the point where the marginal benefit of either shortening or elongating the cash conversion cycle is negative, firms have an optimal working level of working capital and thus manage their working capital optimally. The key issue this paper addresses is the sub-optimal management of working capital. Other papers have studied this issue (Shin and Soenen, 1998, Deloof (2003), Lazaridis and Tryfonidis (2006), Lima et al. (2015) and País and Gama (2015), amongst others), however, this paper shall argue in section 2.5 that their empirical methods are flawed. Hence, this paper attempts to find an answer to the question “do firms manage their working capital optimally?” with an empirical model that takes endogeneity issues into account in combination with several robustness tests. The results from this analysis can be used by managers to take decision on their level of working capital.
1.3.
The contributions of this paper
database including 1064 small-, medium- and large-sized stock-listed firms from nine different European countries in the period 2000 – 2014. Furthermore, this research takes country- and industry-specific effects into account.
1.4.
The outline of the paper
The paper is organized as follows: the next section discusses previous literature and reviews the research methods used in prior literature. From this, an answer to the research question is proposed. Following this information, section three presents the null hypothesis and the alternative hypothesis that this paper tests. Section four discusses the empirical model used to investigate the hypotheses, section five presents the results of the empirical analysis and presents the finding that answer the research question. Section six concludes and discusses possible areas of further research.
2. Theoretical concepts
The first four paragraphs in this section attempt to find an answer to the research question by examining preceding literature on the relationship between working capital management and operating profitability. The section first discusses early research on the cash conversion cycle, the proxy for working capital management. The second paragraph discusses findings on the relationship between constituents of the cash conversion cycle and profitability. After that this section discusses the link between working capital management and market performance of firms. Paragraph 2.4 discusses miscellaneous findings and paragraph 2.5 gives a review of the literature. The review of the literature forms the basis for the main research question, which results in the two hypotheses proposed in section three.
2.1.
The relationship between the cash conversion cycle and profitability
selling a product. Second, days sales in inventory resembles the period between the start of production and for the final product to be sold to the customer. Third, days payables outstanding indicates how long it takes for a firm to pay the bills from the moment the firm receives the bill from the supplier. Shin and Soenen (1998) are the first ones to investigate the relationship between working capital and performance. They use the net trade cycle (NTC; also known as cash conversion cycle), as proxy for working capital management and return on investment (ROI) as a measure of firm performance. The dataset covers 58,985 firm-year observations in the United States from 1975 – 1994, which is analyzed using ordinary least squares. The results show an inverse relationship between NTC and ROI; shareholder value is created by reducing the firms’ net trade cycle. Moreover, they find that the true benefit comes from reducing current assets, rather than lengthening the period of payment. The paper by Deloof (2003) investigates the relationship between the credit conversion cycle and the profitability of 1,009 Belgian firms in the period 1992 – 1996. He finds a strong negative relationship between gross operating income (GOI) and the net trade cycle using a fixed effects model. He also suggests that managers can increase profitability by reducing the cash conversion cycle. Furthermore, accounts receivable, inventory and accounts payable all show a negative correlation between gross profitability when analyzed separately. The author concludes that the constituents can separately influence gross operating income.
2.2.
The constituents of the cash conversion cycle
There is a trade-off between lengthening or shortening the trade accounts. First this section discusses the incentives for a longer cash conversion cycle. That is; either an increase in accounts receivable and inventory or an extension of accounts payable. After that, this section discusses the benefits of a decrease in accounts receivable and inventory and an increase in accounts payable, resulting in a shorter cash conversion cycle. In an economic downturn, firms squeeze money from trade credit (Love et al., 2007). This is because of the need for cash and a squeeze in the supply of money from banks. In an effort to lower the cash conversion cycle, firms can attempt to decrease the accounts receivable, known to customers as trade credit. It is used as a price-cut mechanism (Petersen and Rajan, 1997) and it encourages customers to buy products at times of low demand (Emery, 1987). Moreover, trade credit strengthens the long-term relationship between buyers and sellers (Wilner, 2000). It is a mechanism that allows customers to check for quality before making the payment (Long et al., 1993). Following Blinder and Maccini (1991), a larger inventory can be beneficial to firm performance. It can reduce supply cost, protect the firm from price fluctuations and prevent interruptions in the manufacturing process or loss of business due to a scarcity of products. Furthermore, Shiff and Lieber (1974) argue that a large inventory help stabilize the production process, thus lowering the cost of fluctuations in production. Lastly, Corsten and Gruen (2004) show that 21% to 43% of a firms’ customers faced with a stock-out, go to another store. As suggested by Wilner (2000), there is a benefit to paying suppliers early rather than delaying the payment date. Firms are able to get large discounts if firms make their payments on time. Accounts payable is different from accounts receivable and inventories, since it does not consume resources. Instead it is used as source of short-term finance. It hence is an opportunity cost to reduce the operating cycle of cash. The opportunity cost of its use is the implicit discount rate (if offered) and the implied value of the benefit of having a good supplier relationship.
using cash efficiently creates value, which is why shareholders require working capital to be managed optimally. The longer the cash conversion cycle is, the more financing must be allocated to working capital, which thus hampers the ability of firms to invest in value-enhancing projects (Deloof, 2003). In addition, Kieschnick et al. (2013) find that an additional dollar invested in working capital is worth less than a dollar, because of the cost of capital and the opportunity cost of keeping cash in trade credit.
In the working capital management literature, the link between accounts payable and performance is ambiguous. Deloof (2003) finds a negative relationship between profitability and days payable outstanding, as do Enqvist et al. (2011) and García-Teruel and Martínez-Solano (2007), amongst others. However, Mathuva, 2010 finds a positive relationship between days payable outstanding and a proxy for profitability. Keeping cash on hand is favored over early payment. However, the importance of the relationship with suppliers is important as well. The empirical results in the literature indicate that working capital management is important for firms. Firms that pay attention to working capital management and manage to shorten the cash conversion cycle are associated with higher operating profits.
2.3.
The market performance of firms
Several papers, which I will discuss in this paragraph, describe the relationship between the cash conversion cycle and the market performance of firms, rather than some measure of profitability. They measure the impact of the level of cash conversion cycle (and its components) on the perceived value of a firm. However, the use of Tobin’s Q is not included in this paper since the perceived value of a firm is subjective and it is influenced by more than overall operating performance of a firm. The use of Tobin’s Q does, however, illustrate an alternative dependent variable and serves as a robustness check on the literature. Mohamad and Saad (2010) and Baños-Caballero et al. (2014) use Tobin’s Q as a measure of corporate performance. Tobin’s Q is the ratio of the replacement value of assets over book value. It illustrates the perceived market value of a firm. Nazir and Afza (2009) evaluate the return on assets and Tobin’s Q by analyzing the aggressiveness of working capital management measures. Both studies find that investors value firms that adopt aggressive working capital management measures more highly than firms with laxer working capital management. The research, conducted in Pakistan, include 204 firms during the period 1998 – 2005. The results in Mohamad and Saad (2010), using data from 172 firms listed in Malaysia during the period 2003 to 2007, indicate that there is a significant and negative link between the cash conversion cycle and three indicators for corporate performance and profitability; Tobin’s Q, return on assets (ROA) and return on invested capital (ROIC).
Baños-Caballero et al. (2014) also examines the relationship between Tobin’s Q and the cash conversion cycle, using a database consisting of 3846 observations of Spanish SME’s. The paper finds that there is an inverted U-shape relation between working capital and corporate performance which suggests that there is a non-linear relationship between firm performance and working capital management. Together with País and Gama (2015), this is the only paper that takes endogeneity issues into account by using an instrumental variable approach. The technique of using instrumental variables to deal with endogeneity issues is discussed in paragraph 4.1.
2.4.
Other factors influencing working capital management
are lower. Hence, optimal levels of working capital change to reflect business conditions. Also, Einarsson and Marquis (2001) and Love et al. (2007) find that firms are more dependent on bank financing in times of distress. Consequently, the demand of working capital financing is countercyclical. These results underline the importance of working capital management.
Moreover, Enqvist et al. (2011) suggest that the cash conversion cycle is a better way of measuring the need of cash, other than liquidity ratios, since the cash conversion cycle recognizes life expectancies of working capital and its components. Moreover, the cash conversion cycle illustrates the fact that production, distribution and collection are not synchronized and instantaneous, since the process comes with a time lag (Richards and Laughlin, 1980). Lastly, the cash conversion cycle is an operating variable, just like gross operating income. Some of the research described in the literature section adopts return on assets and/or return on invested capital as a measure of profitability. Return on assets is considered as an overall indicator of performance. However, it also includes the return on financial assets and others. Thus, this paper uses gross operating income, since it measures operational performance, directly associated with the failure or success of operational activities of the firm.
2.5.
Literature review
Deloof (2003), Enqvist et al. (2011), Mohamad and Saad (2010), Nazir and Afza (2009), Padachi (2006), Mathuva (2010), García-Teruel and Martínez-Solano (2007), Lima et al. (2015), amongst others, make a direct link between the cash conversion cycle and some form of performance. However, since working capital management and financial performance or profitability are been influenced the same exogenous shocks, implying that both variables are being endogenously determined (as a function of the common exogenous shocks) we have to account for this simultaneity in our estimation model since otherwise our estimates will not be consistent as a result of endogeneity (see Roberts and Whited (2012)). In this paper we propose to address the endogeneity concerns by using the so-called instrumental variables approach, see Woolridge (2012)
they have to add up to the combined cash conversion cycle. The Pearson correlation matrix in appendix A shows the correlation between the constituents combined cash conversion cycle in my database. This problem can be eliminated by including an ‘adding-up restriction’ to the model where the effects of the separate components add up to the effect that the whole cash conversion cycle has on a firm’s financial performance. Thus, I leave this to future research. This paper therefore only examines the effects at the combined level of all the components of the cash conversion cycle.
The review of the literature shows that the negative relationship between working capital management and firm profitability is generally interpreted as an opportunity for firms to increase profitability by decreasing the cash conversion cycle. As my results will show, the negative and significant relation between the cash conversion cycle and the firm’s profitability is also confirmed in my data. However, the conclusions to be drawn from my results are different from what can be found in the literature. It makes no sense to urge firms to decrease the cash conversion cycle in order to improve profitability if the cash conversion cycle itself is an endogenous variable. In that case, firms must carefully examine the determinants of the cash conversion cycle to find instruments that they can manipulate in order to shorten the cash conversion cycle and thus (indirectly) improve profitability.
3. Hypotheses
3.1.
The null hypothesis: do firms manage their working capital optimally?
The review of the literature in the previous section indicates that there is a significant negative relationship between the cash conversion cycle and firm profitability. However, these results are subject to endogeneity concerns so that the purpose of this paper is to make sure that the observed relationship is not due to the endogenous relation between the cash conversion cycle and firm profitability. Therefore, I propose the null- and alternative hypothesis:
H1) Firms do not manage their working capital optimally, i.e. there is room for improvement which will then lead to an improvement of the firm’s profitability.
4.
Empirical Model
This paper uses pooled cross-sectional analysis to investigate the relationship between working capital management and profitability. I do not use panel data analysis, since the dataset in unbalanced. Working capital management influences the amount of money used in day-to-day operations and the cash conversion cycle is used as a proxy for working capital management. The research in this paper links this proxy to the gross operating income of a firm. First, this section discusses the possible endogeneity issue and presents the empirical model used to test the two hypotheses. Paragraph 4.2 presents the dependent variable and paragraph 4.3 presents the main explanatory variable. Paragraph 4.4 presents the independent variables and control variables. The last paragraph outlines the regression equations. The dataset constitutes of 1064 firms from nine European countries from the period 2000 – 20014. For data description, summary statistics and a Pearson correlation matrix, the reader is advised to read appendix A.
4.1.
Endogeneity and regression method
highly correlated with the main explanatory variable itself (ρ=0,992), whereas it is not correlated with the dependent variable gross operating income (ρ=0,002). The regression is executed using a two-stage-least-squares estimation (2SLS), rather than an ordinary-least-squares estimation (OLS). The first stage is the estimation of the main explanatory variable by regressing the explanatory variable with its lag, including all control variables. This new, estimated, variable only captures the effects on gross profitability of shifts in working capital management proxies, induced by its lag. The second stage takes the estimation of the main explanatory variable and all control variables, and assesses the influence of the estimated main explanatory variable on the dependent variable gross operating income. In order to further test the relationship between working capital management and gross operating income, this paper used the condensed forms of the cash conversion cycle and gross operating income in a seemingly unrelated regression (SUR) analysis. The relationship between the error terms of this analysis should point in the same direction as the IV-approach. This paper further elaborates on the SUR analysis in paragraph 4.5.
4.2.
Profitability
Following Deloof (2003) and Lazaridis and Tryfonidis (2006), this paper uses gross operating income (GOI) as a proxy for profitability, since it is possible to compare the operational performance of firms. It is measured by dividing gross operating profit (sales minus cost of goods sold) by average total assets minus average financial assets (eq. 1).
𝐺𝑂𝐼 = 𝐺𝑟𝑜𝑠𝑠 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝑝𝑟𝑜𝑓𝑖𝑡
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠−𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠 (eq. 1)
In some of the literature discussed before, the dependent variable return on assets (ROA) is used. However, this paper does not support the use of ROA since some firms have financial assets on their balance sheet that contribute significantly to total return, hence distorting operational profit.
4.3.
Working capital management
management of cash, since less cash is trapped in day-to-day operations. The cash conversion cycle, first introduced by Gitman in 1974, is a combination of days sales outstanding (DSO), days sales in inventory (DSI) and days payable outstanding (DPO). The last variable is negatively related to the cash conversion cycle (CCC), see equation 2.
𝐶𝐶𝐶 = 𝐷𝑆𝑂 + 𝐷𝑆𝐼 − 𝐷𝑃𝑂 (eq. 2) 𝐷𝑆𝑂 = 𝐴𝑐𝑐𝑜𝑢𝑛𝑡𝑠 𝑅𝑒𝑐𝑒𝑖𝑣𝑎𝑏𝑙𝑒 𝑆𝑎𝑙𝑒𝑠 / 365 (eq. 3) 𝐷𝑆𝐼 = 𝐼𝑛𝑣𝑒𝑛𝑡𝑜𝑟𝑦 𝑆𝑎𝑙𝑒𝑠 / 365 (eq. 4) 𝐷𝑃𝑂 = 𝐴𝑐𝑐𝑜𝑢𝑛𝑡𝑠 𝑃𝑎𝑦𝑎𝑏𝑙𝑒 𝑆𝑎𝑙𝑒𝑠 / 365 (eq. 5)
Days Sales Outstanding (DSO) is a financial measure of the performance of a firm. It measures show long it takes for a firm to turn sales into cash. A lower DSO means less money is tied up in day-to-day operations. Days Sales in Inventory (DSI) is a financial measure of the magnitude of inventories. It measures how long it takes for a firm to turn inventory into cash. A lower DSI means less money is tied up in the normal course of business. Days Payable Outstanding (DPO) tells you how long it takes for a firm to pay its bills. The more days you wait to pay the bill, the more cash you control. So, the higher the DPO, the more time the firm has to generate value out of this cash. As described in the literature (paragraph 2.2), there are reasons to pay the bills early as well.
4.4.
Control variables
This paragraph discusses the control variables included in the regression. They are used to estimate conditional correlations between the cash conversion cycle and operating profitability, which is the ultimate target of this paper. By including the control variables discussed below, relevant information about the determinants of cash conversion cycle and operating profitability are included into this analysis.
since the economies of scale of working capital management is relevant (Lazaridis and Tryfonidis, 2006). The second control variable is current liabilities over total assets. It measures the magnitude of liabilities on the balance sheet, hence the amount of money that is needed in the near future. The third control variable is current assets over total assets. It captures information about the liquidity of a firm. The fourth control variable is sales growth. A low sales growth indicates that business is slowing down, hence payables are likely to be stretched to increase cash. The fifth control variable is the debt ratio, also known as leverage, indicating the relative importance of debt in the financing of the firm. The higher the ratio, the more debt on the balance sheet and therefore the greater the firm’s financial risk. Hence, a negative sign is expected. It is computed by dividing total debt over total equity. The last control variable is the current ratio. It measures the firm’s ability to pay short-term obligations and is defined as current assets over current liabilities. A current ratio of less than one shows that the firm is not in good financial health, since the firm is likely to have problems paying off its obligations when they become due at some point. A current ratio of more than three may indicate that current assets are not managed properly. Furthermore, there is no such thing as a standard or threshold levels of CCC, DSO, DSI or DPO, because the amount of cash needed for day-to-day operations differs per industry (Filbeck and Krueger, 2005). Therefore, this paper uses industry dummies to take industry specific characteristics into account by using the first two numbers of the corresponding Standard Industrial Classification (SIC) code. Furthermore, this paper uses country dummies to take country specific characteristics into account, since laws and habits can differ between countries. Lastly, the regressions include robust standard errors due to heteroscedasticity issues, on which I will elaborate in appendix A.
4.5.
Regression equations
Following the research methods discussed above, the four equations below show the model used to measure the relation between working capital management and profitability. Equation (6) presents the first stage of the IV model. The results of this regression are used to compute the predicted value of cash conversion cycle as a function of the instrument CCC(t-1) and control variables, which is uncorrelated with the error term 𝜀2 in equation (7). Equation (7) shows the second stage of
regression. The estimate of 𝛽9 in equation (7) then represents the impact of how changes in cash
conversion cycle affect profitability and is the statistic used to test the null hypothesis.
𝐶𝐶𝐶𝑖𝑗 = 𝛽 0+ 𝛽 1𝐶𝐶𝐶(−1)𝑖𝑗+ 𝛽 2log(𝑆𝑖𝑧𝑒)𝑖𝑗+ 𝛽 3𝐶𝐿𝑇𝐴 + 𝛽 4𝐶𝐴𝑇𝐴 + 𝛽 5𝑆𝑎𝑙𝑒𝑠𝑔𝑟𝑜𝑤𝑡ℎ + 𝛽 6𝐿𝑒𝑣 +
𝛽 7 𝐶𝑅 + 𝜀1 (eq. 6)
𝐺𝑂𝐼𝑖𝑗 = 𝛽 8+ 𝛽 9𝐶𝐶𝐶̂ 𝑖𝑗+ 𝛽 10log(𝑆𝑖𝑧𝑒)𝑖𝑗+ 𝛽 11𝐶𝐿𝑇𝐴 + 𝛽 12𝐶𝐴𝑇𝐴 + 𝛽 13𝑆𝑎𝑙𝑒𝑠𝑔𝑟𝑜𝑤𝑡ℎ + 𝛽 14𝐿𝑒𝑣 +
𝛽 15 𝐶𝑅 + 𝜀2 (eq. 7)
Equations (8) and (9) are the reduced form versions of equations (6) and (7) which we use to estimate the conditional correlation between firm financial performance and the cash conversion cycle. With this correlation we can test the robustness of the results provided by the estimates of equations (6) and (7). 𝐺𝑂𝐼𝑖𝑗 = 𝛽 8+ 𝛽 10log(𝑆𝑖𝑧𝑒)𝑖𝑗+ 𝛽 11𝐶𝐿𝑇𝐴 + 𝛽 12𝐶𝐴𝑇𝐴 + 𝛽 13𝑆𝑎𝑙𝑒𝑠𝑔𝑟𝑜𝑤𝑡ℎ + 𝛽 14𝐿𝑒𝑣 + 𝛽 15 𝐶𝑅 + 𝜀3 (eq. 8) 𝐶𝐶𝐶̂𝑖𝑗 = 𝛽 8+ 𝛽 10log(𝑆𝑖𝑧𝑒)𝑖𝑗+ 𝛽 11𝐶𝐿𝑇𝐴 + 𝛽 12𝐶𝐴𝑇𝐴 + 𝛽 13𝑆𝑎𝑙𝑒𝑠𝑔𝑟𝑜𝑤𝑡ℎ + 𝛽 14𝐿𝑒𝑣 + 𝛽 15 𝐶𝑅 + 𝜀4 (eq. 9) Where,
GOI = gross operating income CCC = Cash Conversion Cycle
𝐶𝐶𝐶̂ = WCM proxy estimation from first stage regression Log(Size) = log of annual sales
CLTA = Current liabilities over total assets CATA = Current assets over total assets Salesgrowth = Annual sales growth
Lev = Leverage (debt over assets)
CR = Current ratio (Current assets over current liabilities)
5. Results
management and operating income using the IV-approach. Third, this section discusses the seemingly unrelated regression, which forms the first robustness test of the results. Lastly, this section outlines the robustness of this research including several verifications.
5.1.
First stage regression
Table 1 shows the output of the first stage regression. The first stage regression is used to compute an estimated value of cash conversion cycle, which is then used in the second stage regression. This is done so because the cash conversion cycle and profitability are expected to show endogeneity issues. Endogeneity issues are expected because some external factors influence both the dependent variable and the main explanatory variable. Using instrumental variables, this issue is mitigated. The lag of the cash conversion cycle is used as the explanatory variable for the cash conversion cycle itself. The relationship is almost one-to-one (0.9668) and significant (p-value = 0.0000). Furthermore the R-squared is almost one, indicating a high goodness-of-fit. The coefficients are then used to estimate the value of the cash conversion cycle in the second stage regression. For both the first stage regression and second stage regression, country dummies and industry dummies are included, to adjust for country- and industry specific characteristics.
Dependent Variable: ccc eq. 6 ccc(-1) 0.9668*** lsize 0,2095 clta 2,1745 cata 1,5115 salesgrowth 0,0611 leverage -0.2124 current ratio 2,0958*** constant -60,371
country dummy yes
industry dummy yes
robust standard errors yes
r-squared 0.9860
F(10,1053) 7404,11***
N= 1064
Table 1: first stage regression of the cash conversion cycle
Table 1 reports the ins trum ental variable regres s ion. goi is gros s operating incom e, ccc is the cas h convers ion cycle in days , ls ize is the log of s ales and captures the s ize of a com pany, clta is current liabilities over total as s ets and cata is current as s ets over total as s ets , s ales growth is the annual s ales growth in percentages , current ratio is current as s ets over current liabilities and leverage is total debt over total as s ets .
Independent Variable ┐
5.2.
Second stage regression
Table 2 shows the output of the linear regression analysis. The estimated cash conversion cycle is regressed against the profitability of the firm, using instrumental variables. The results show a negative (-0,0004) and significant relationship between the cash conversion cycle and gross operating income, which indicates that firms do not manage their working capital optimally. The coefficient is small, however, this is due to the different dimensions of the variables. The results rejects the null hypothesis: There is no relation between operating income and working capital management, implying that working capital management has been optimally determined. The R-squared of the IV-regression is low, thus the fit of this regression is low. This indicates more information is needed to increase the quality of the estimation. The estimation shows that large firms are associated with a higher gross operating income (GOI) as percentage of sales than smaller firms. Moreover, a marginal positive sales growth, leverage and current ratio are associated with a lower GOI, whereas a marginally higher current asset over total assets ratio is associated with a higher GOI.
Table 2: IV regression of profitability
Dependent Variable: goi eq. 7 ccc -0,0004*** lsize 0,0236*** clta -0,1908** cata 0,5217*** salesgrowth -0,0284*** leverage -0,0784* current ratio -0,0679*** constant 7,343
country dummy yes
industry dummy yes
robust standard errors yes
r-squared 0,1363
Wald Chi test 430,52***
N= 1064
Table 2 reports the instrumental variable regression. goi is gross operating income, ccc is the cash conversion cycle in days, lsize is the log of sales and captures the size of a company, clta is current liabilities over total assets and cata is current assets over total assets, salesgrowth is the annual sales growth in percentages, current ratio is current assets over current liabilities and leverage is total debt over total assets.
Independent Variable ┐
5.3.
Reduced form regressions
As stated in paragraph 4.1 and 4.5, this paper uses a seemingly unrelated regression analysis to verify the relationship between the cash conversion cycle and gross operating income and furthermore, it is the first robustness test. First the reduced form equations (8) and (9) are estimated. Table 3 in appendix B shows the output of the analysis. The two systems of regression are statistically significant, as are all but one of the coefficients. After that, this paper looks at the correlation between the error terms. The conditional correlation is negative and not large (ρ=-0,1033) and this finding supports hypothesis 1 (and rejects the null hypothesis): Firms do not manage their working capital optimally, i.e. there is room for improvement which will then lead to an improvement of the firm’s profitability. Therefore, this paper finds that the average firm should be able to marginally increase gross operating income if it can lower the cash conversion cycle. We illustrate this point and therefore the firms in the dataset are –on average– somewhere to the right of the maximum of the parabola. Firms should adjust its exogenous parameters in such a way that it lowers its CCC. Furthermore, a high absolute correlation between the error terms would indicate that an important variable is missing, which is not the case.
5.4.
Robustness and verification
coincidence. The extra test uses return on equity (ROE) as a substitute for the dependent variable gross operating income. Again, the IV-approach is used as well as all other specifications from the research method. ROE shows much net income per share of equity is earned in percentages, just like gross operating income (GOI). The result of the test is shown in table 4 in appendix B. The coefficient in front of the cash conversion cycle is negative and significant (-0,0002 and the p-value=0,0000). The findings are in line with the main results, however, the R-squared is lower. Furthermore, the seemingly unrelated regression analysis shows a negative correlation between the error terms (ρ=-0,0539). These results support the main findings in this paper.
In order to verify the main results, this paper runs a couple of checks on the estimations. To see if this paper is correct in using an IV-approach, we run a test for endogeneity. The paper runs the Durbin and Wu-Hausman tests to determine if the cash conversion cycle is exogenous and if regular ordinary least squares (OLS) could have been used instead of two stage least squares (2SLS). The two tests investigate whether the residuals of the first and second stage regression are independent. If this test is rejected, this paper is correct in using 2SLS, rather than OLS. The results of the test reject the null and this indicates the estimated cash conversion cycle is found to be endogenous, and the use of the lag of the cash conversion cycle in the first stage regression is correct. Furthermore, the two stage least squares regression is tested in STATA for weak instruments. If the validity of the instruments was rejected, other instruments should be applied to the research. The tests conclude that the instruments are not weak, and this paper is correct in using them.
6. Conclusions
This paper studies the relationship between working capital management on gross operating income, by employing a sound empirical model. I use a dataset of 1064 European stock-listed firms from nine different countries and this paper uses the cash conversion cycle as a proxy for working capital management, which is the main explanatory variable. Furthermore, gross operating income is used as a proxy for operating profitability. The relationship is investigated using an IV-approach, to adjust for possible endogeneity issues. A seemingly unrelated regression analysis, amongst other robustness tests, confirms the findings in this paper since the conditional correlation is in the same direction as the IV-approach.
6.1.
Working capital management is not optimal
Other than the papers written before, this paper uses instrumental variables to adjust for endogeneity issues in combination with a seemingly unrelated regression analysis to find the relationship between working capital management and gross operating income (GOI). This paper finds a negative and significant relationship between the cash conversion cycle (CCC) and gross operating income. Assuming that GOI is a function of CCC but not the other way around, this implies the average firm does not manage its working capital optimally. This result is confirmed by the SUR analysis. This outcome suggests that the average firm should adjust its exogenous parameters in such a way that it lowers its CCC.
6.2.
Future research
This paper reviews the relation between combined level of the cash conversion cycle and firm profitability. Though conceptually it would make sense to use the cash conversion cycle as the variable of interest in the analysis presented here, at the practical level it seems unlikely that firms would try to implement a general policy for all three different components of the cash conversion cycle: DSO, DSI and DPO. In order to estimate the relation of each component with firm profitability we would then need to add an “adding-up constraint” to the model in order to ensure that the sum of the effects of three components are equal to the effects as estimated from the cash conversion cycle. However, this is beyond the scope of the paper and we leave this to be explored in future research.
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Appendix
Appendix A: Data description
Appendix A first discusses the data collection first. Second, this section discusses the sample description and summary statistics. Third, this section discusses the Pearson correlation matrix. The dataset covers 1064 non-financial stock listed firms from major European stock exchanges, with a total of 12.833 firm-year observations. The research is executed using cross-sectional data analysis and therefore, all of the firm-year observations are averaged into one data point per firm. Data is retrieved from the Capital IQ database and processed using STATA. The sample comprises data points from 2000 to 2014 and combines large- to small-cap stocks from European countries. No prior research has been executed on the European market in its entirety and therefore, my research adds to the existing literature. According to Lazardis and Tryfonidis (2006), firms that are listed on the stock exchange have an incentive to maximize accounting profits to make their shares attractive. Moreover, financials of stock-listed firms are transparent and informative and they are obliged to report according to accounting standards, making these statements reliable and comparable. The dataset includes large, medium and small stock listed firms of the following exchanges: Euronext Amsterdam, Euronext Brussels, Euronext Paris, OMX Kopenhagen, OMX Helsinki, OMX Stockholm, Bolsa de Madrid, London Stock Exchange and the Börse Frankfurt. Following Inqvist, Graham and Nikkinen (2012), Lazardis and Tryfonidis (2006) and Deloof (2003), thisp paper excludes all financial companies (SIC-codes 6000 – 6800). This is due to the fact that working capital has a different function in financial industries, since it is one of the products banks supply. After retrieving the data from Capital IQ, the dataset is refined. Following Baños-Caballero et al. (2014), all missing values and cases with unexpected values, like negative inventory or negative assets were excluded from the dataset. As stated in paragraph 4.4, robust standard errors are included in the regression analysis. The robust standard errors are used to adjust for heteroscedasticity, since the error terms do not have the same variance across all observation points. By adding robust standard errors, the standard deviations are adjusted for the difference in variance.
cycle is 63,6 days (median 60,3 days). This is lower than Spanish SME’s (76,3 days), Finnish firms (108,8 days) and stock listed firms in Athens (189 days). However, the mean cash conversion cycle is higher than Belgian firms (44.5 days) (García-Teruel and Martinez-Solano (2007), Enqvist et al. (2011), Lazaridis and Tryfonidis (2006) and Deloof (2003)). It takes on average 64,4 days for a firm to receive it’s payments (median: 60,7 days). Furthermore, it takes on average 48,4 days (median: 39,8 days) to turn its inventory around. Moreover, the average firm waits 49,2 days (median: 39,9 days) to pays its bills. 22,6%). On average, the current liabilities over total assets (CLTA) is 32,4% (median: 30,5%), current assets over total assets ratio (CATA) is 47,3% (median: 47,2%). These high percentages show that working capital, on average, comprises a large portion of the total balance sheet of a firm. The current ratio is 1,78 on average (mean: 1.47), which indicates that the average firm is fairly liquid. Sales growth is 11,3% on average and median sales growth is 5,7%, and therefore skewed right. The leverage of the average firm in this database is 24,6% (median: 22.3%).
Table 2 presents the Pearson correlation coefficients for all variables in the analysis. The negative relationship between days payable outstanding and gross operating income is not consistent with
Table A1: Summary statistics
mean median
standard
deviation minimum maximum
goi 0,358 0,312 0,235 -0,319 2,077 ccc 63,632 60,325 69,029 -383,879 578,462 dso 64,434 60,724 38,591 0,074 368,796 dsi 48,435 39,753 59,176 0,017 612,428 dpo 49,236 39,297 43,570 0,077 539,109 lsize 2,839 2,861 0,918 -0,759 5,375 clta 0,324 0,305 0,140 0,002 1,018 cata 0,473 0,472 0,197 0,010 0,982 salesgrowth 0,113 0,057 0,933 -0,838 29,067 cr 1,784 1,468 1,221 0,247 11,865 leverage 0,246 0,223 0,176 0,000 1,839
Table 1 reports the summary statistics of all 1064 datapoints. goi is gross operating income, ccc is the cash conversion cycle in days, dso is the days sales
the view that it is beneficial to postpone payments. The alternative view that firms are rewarded for early payments and supplier relationships are valued, is supported here. However, European accountancy standards do not include discounts in their operating income, but in financial income. Therefore, the discounts that are rewarded for early payments is not reflected in gross operating income. Furthermore, the days sales in inventory is negatively related to gross operating income. A higher inventory level protects you from possible interruptions in the production process or scarcity of products. Moreover, it is a hedge against price inflation (Blinder and Maccini, 1991). Furthermore, GOI is negatively related to the control variables size and leverage. This suggest that larger, more leveraged firms do not perform as well as the opposite.
Table A2: Pearson Correlation Matrix
goi ccc dso dsi dpo lsize clta cata salesgrowth leverage
ccc 0,012 1,000 dso -0,162 0,359 1,000 dsi -0,080 0,726 -0,003 1,000 dpo -0,271 -0,281 0,313 0,205 1,000 lsize -0,037 -0,031 -0,155 -0,085 -0,204 1,000 clta 0,236 0,006 0,229 -0,083 0,080 0,133 1,000 cata 0,235 0,338 0,285 0,346 0,186 -0,285 0,478 1,000 salesgrowth -0,084 -0,120 -0,006 0,001 0,187 -0,140 -0,027 0,103 1,000 leverage -0,155 -0,084 -0,091 -0,047 -0,010 0,145 -0,107 -0,475 -0,034 1,000 cr -0,088 0,146 0,028 0,297 0,197 -0,452 -0,405 0,412 0,141 -0,318
Appendix B: Tables
Table 3: reduced form regressions
Dependent Variable: goi (eq. 8) ccc (eq.9)
lsize 0,0244*** 0,0236*** clta -0,0997 -0,1908** cata 0,4144*** 0,5217*** salesgrowth -0,0234*** -0,0284*** leverage -0,1025** -0,0784* current ratio -0,0618*** -0,0679*** constant 5,7317 3,2802
country dummy yes yes
industry dummy yes yes
robust standard errors yes yes
r-squared 0,1251 0,2768
Chi2 152,13*** 407.26***
N= 1064 1064
*** significant at 1% level, ** significant at 5% level, * significant at 10% level Table 3 reports the instrumental variable regression. goi is gross operating income, ccc is the cash conversion cycle in days, lsize is the log of sales and captures the size of a company, clta is current liabilities over total assets and cata is current assets over total assets, salesgrowth is the annual sales growth in percentages, current ratio is current assets over current liabilities and leverage is total debt over total assets.
Independent Variable ┐
Table 4: IV regression of profitability
Dependent Variable: roe eq. 7
ccc -0,0002** lsize 0,0417*** clta -0,0928 cata 0,1101** salesgrowth -0,0015 leverage -0,0784*** current ratio -0,0679 constant 9,8544
country dummy yes
industry dummy yes
robust standard errors yes
r-squared 0,0916
Wald Chi test 99,05***
N= 1064
*** significant at 1% level, ** significant at 5% level, * significant at 10% level
Table 2 reports the instrumental variable regression. roe is return on equity, ccc is the cash conversion cycle in days, lsize is the log of sales and captures the size of a company, clta is current liabilities over total assets and cata is current assets over total assets, salesgrowth is the annual sales growth in
percentages, current ratio is current assets over current liabilities and leverage is total debt over total assets.
