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Working Capital Management: An Exploratory Study

Etiennot, Hernan ;  Preve, Lorenzo A ;  Allende, Virginia Sarria . Journal of Applied Finance ; Tampa  Vol. 22, Iss. 1,   (2012): 161-174.

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Abstract

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Working capital management is an issue in which finance research is scarce. One possible reason behind this fact might relate to the relative ease with which efficient financial markets correct deviations from optimal working capital policies. However, in less efficient financial markets, pervasive among emerging economies, working capital management is critical for both firms' performance and survival. The difference in the markets ability for providing immediate assistance to firms might explain the differential consequences on firms' profitability and financial distress. This article explains the fundamentals of working capital management, the importance of its interaction with financial markets, and how this interaction might explain working capital patterns around the world. [PUBLICATION ABSTRACT]

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Headnote

Working capital management is an issue in which finance research is scarce. One possible reason behind this fact might relate to the relative ease with which efficient financial markets correct deviations from optimal working capital policies. However, in less efficient financial markets, pervasive among emerging economies, working capital management is critical for both firms' performance and survival. The difference in the markets ability for providing immediate assistance to firms might explain the differential consequences on firms' profitability and financial distress. This article explains the fundamentals of working capital management, the importance of its interaction with financial markets, and how this interaction might explain working capital patterns around the world.

*Working capital management is probably one of the most basic and least studied topics in corporate finance. It should involve the analysis of the investments in operating assets and its corresponding financing. Nevertheless, this investment is carried out, most of the times without following a formal investment analysis, and the financing alternatives are not adequately evaluated. This paper digs into the fundamentals of working capital management exploring the relative importance of capital markets efficiency and industry and firm patterns.

There is some relevant research on the individual components of working capital, but little academic effort has been devoted to develop a comprehensive view.1 There is, for example, a large stream of literature on trade credit, (both receivables and payables). It starts with the early contribution of Meltzer (1960) on the relation between monetary conditions and trade credit, and continues with numerous papers developing theories of trade credit; these, aiming to explain why firms decide to use trade credit, provide good insights on the usefulness of offering and/or accessing such a credit.2 Additionally, some studies covering the dynamic of trade credit in times of financial distress or widespread financial turmoil, illustrate the consequences of these operating/financial decisions.3 There is another stream of literature that discusses the importance of cash holdings, highlighting not only the typical transaction arguments, but also many modern theories that could help explaining the significant cash balances held by numerous firms (including agency, asymmetric information, hedging and many other concepts).4 Finally, the other two working capital components -inventory and short-term debt- have been also widely covered by the literature.5 Once again, none of these offers an integrated view of working capital management, rather, most of these studies tend to concentrate on specific topics or contexts.6 An evolution in this aspect is the recent publication of two papers that consider trade receivables, inventories, and trade payables.7

An integrated analysis of working capital management is not facilitated at the business level, either. Business managers frequently talk about the working capital requirements of their businesses, but the first intuition that comes to their minds -based on a more computational view that defines working capital as current assets minus current liabilities - is typically associated to an investment component, i.e. as the operating investment of the firm. We consider this conception to be incomplete and misleading. In order to operate, the firm needs not only its working capital (i.e. current assets minus current liabilities) but its overall investment in current assets. Amore accurate intuition of working capital emerges when we define working capital from the liabilities side (as long-term capital minus fixed assets, which is mathematically equivalent). The net result, still on the right side of the balance sheet, can be interpreted as a financial component, and therefore, as part of the capital structure decision of a firm. More specifically, working capital can be understood as the amount of long-term capital devoted to the financing of current assets.8

This refinement of the working capital intuition is not merely semantic; on the contrary, its impact on corporate finance practices could be very significant. The reason is that the focus is shifted from short-term operating decisions towards more structural ones. Moreover, framing working capital practices within the financing of operating investment helps to understand its key drivers, and to differentiate them -and their relative importance- from the key factors that shape the operating investment of a firm. Thus, in order to understand firms' working capital policies, it is imperative to identify (and differentiate) the investment and the financing components.

The operating investment of the firm, in general terms, includes cash balances, account receivables and inventory, and it is usually estimated as net of operating liabilities (which naturally emerge in any running business).9 We call this figure Financial Needs for Operation (FNO), since it represents the operating investment that needs to be actively financed by the firm. The FNOs are critically affected by the firm's activity level; however, there are other potentially significant influences from: (i) the company, (ii) the industry, and (iii) the region in which the firm operates. The industry effect should be fairly clear: depending on whether firms operate in the manufacturing or service sector, the level of competitiveness and concentration of the industry and its supply chain, the type of goods they sell (durable vs. perishable), the cost or price of goods or services (in both absolute and relative terms), etc., they may have a propensity to have higher or lower levels of FNOs. Firms' specific decisions, on the other hand, may also have important effects and can even produce significant variations within industry patterns. Hence, we believe the differences in the FNOs to be primarily driven by industry characteristics and firm specific choices. In contrast, we consider the magnitude of country or regional effects to be of secondary order; mainly with an indirect link through the financing channel, as suggested by Meltzer (1960), or through other specific country characteristics that might affect operating investments.

Working capital, as we suggested, should be understood as the long-term capital a finn chooses to apply to the financing of the net operating investment, and therefore as part of the capital structure decision of a firm. To analyze the determinants of working capital patterns, it is important to keep this intuition in mind. In agreement with any other financing decision, we would expect firm and industry characteristics to have an influence on the level (or share) of working capital; however, given the worldwide ample disparity in financial markets' relative development, we expect country effects to be particularly relevant in shaping this choice. The reason is that one would expect firms to adjust capital structure decisions depending on issues such as capital markets development, stability of the local financial markets, volatility risk, country risk, quality of the governance, etc. Moreover, being at the core of the financing decisions, setting a wrong level of working capital can cause liquidity and profitability problems, which will depend on the efficiency of the capital markets in which the firms operate. In efficient markets, firms failing to establish the correct level of working capital can easily solve any emerging problem by going to the market to adjust their financing mix; at most, some minor costs of financial distress might occur in the meantime. On the contrary, a suboptimal financing of the operating investment for firms located in less efficient financial markets can cause serious financial problems, which might drag them into financial distress and potentially deep liquidity issues.

We have thus far identified an investment component - the FNO- and its financing counterpart -the working capital choice. Both need to be considered together, since one is a consequence of the other. This paper uses a framework that combines these two concepts, allowing us to take a first step towards a more integrated and insightful treatment of working capital practices, whose relevance would be context dependent.

Using data from firms in 42 industries and 5 1 countries, we pursue an exploratory study of the main patterns in working capital and FNOs across industries and regions. We describe the main variations we observe in the data and examine whether they could be reasonably linked to differences in business decisions, industry characteristics, or financial market development. This paper is only a first step, in which we aim at assessing the relative importance of firm, industry, and region components in the decisions regarding FNOs and working capital. Identifying the complete set of determinants of working capital and improving its current management practices, however, will require a more comprehensive study. Particularly, we expect subsequent research to provide a suitable analytical framework that helps identifying not only the main determinants of the operating investment and its optimal financing choice, but also their corresponding influence in terms of profitability and overall performance of the firms.

The analysis we present in this paper provides preliminary support to our initial conjectures. Particularly, we find that the investment decision -the FNOs- is mainly driven by firm and industry characteristics, and that the financing choice -the working capital- is primarily influenced by the economic and financial market environment -country, region, institutions, etc.- surrounding the firms. Even though our empirical analysis shows that both working capital and FNO depend on the firm's specific business decisions (i.e. identifying company, industry, and country, it turns out that most of the annual variation is explained by firms' identification codes -based on Standard & Poor's Global Compustat company identifier - ID), we further observe that industry effects are stronger in explaining the differences of the operating investments (FNOs), than in explaining the differences of the financing choice (i.e., working capital). Moreover, we find this effect to be stronger for developed countries than for developing ones, for which the country variable shows a stronger effect. These results might suggest that financing decisions are more sensitive than investment choices to the presence of financing constraints.10

The rest of the paper proceeds as follows. In Section I, we describe the sample and the data selection process. Section II provides the analysis of the main cross sectional variations of FNOs and working capital at the industry level. In Section III we examine the regional patterns. In these two sections we use a simple inspection of basic summary statistics. Then, in Section IV, we present a deeper analysis based on variance decomposition. Finally, Section V concludes and presents interesting avenues for future research.

I. Data, Sample Selection, and Variable Definition

We use data from all listed companies in the North America and Global Compustat databases from 2000 to 2007, reorganizing the standard industrial classification (SIC) code described in Fama and French (1997). We eliminate repeated observations, as well as firms reporting missing or negative data in our key variables (i.e. total current assets, total assets or total revenues). We also remove potential outliers; specifically, we discard observations outside the interval given by the 1st and 99th percentile. We concentrate on data from all industries in four different regions (Asia, Europe, Latin America, and North America), excluding firms in the financial services and defense industries. Finally, in order to obtain a better assessment of the different component of variance - using Country, Industry, Countryxlndustry, Company and Annual observations - we exclude any subject with less than three nested observations. The final panel includes 122,892 observations from 20,5 15 companies in 51 different countries.

Our main variables are defined as follows: FNOs are computed as Current Assets minus all non-financial shortterm liabilities. Working Capital, on the other hand, is the Compustat variable, defined as current assets minus current liabilities (which is mathematically equivalent to the estimation based on long term capital minus fixed assets). To make our key variables comparable, we use the standard scaling factors. We scale FNO by total revenues, which help us to control for activity level and firm size. The working capital variable, on the other hand, is scaled by FNOs, leaving us with a ratio that represents the percentage of FNOs financed with working capital.

II. Cross Sectional Variation: The Industry Effect

In our first approach towards analyzing the patterns in working capital management we observe a set of summary statistics of our main variables, FNO to Revenues and Working Capital to FNO, by industry. The results are presented in Panels A and ? of Tabic I.

We observe that even though on average firms have FNO equivalent to 88% of their revenues, there is an ample variation of this figure across industries. Several industries have operational investments exceeding their annual revenues; such is the case of Pharmaceutical Products, Medical Equipment, and Mines and Precious Metals, among others. Other industries, on the contrary, have relatively low operating investment; retailers, for example, invest on average only 35% of their yearly revenues (similar cases are Candy and Soda, Health Care, and Business Supplies, among others). As we suggested before, the level of the operating investment seems to be inspired at the industry level.

The working capital ratio, on the other hand, also presents wide variations across industries." The average ratio denotes that around 28% of the operating investment is financed with long-term capital; however, while firms in the Measurement & Co, Medical Equipment and Pharmaceutical Products industries, for example, finance, on average, more than 50% of their operating investment with long-term capital, firms in the Communication, Healthcare, and Transportation industries support less than 5% of their FNO with long-term fimds. There are even some other industries exhibiting a negative working capital to FNO average ratio (e.g. Entertainment, Restaurants & Hotels, and Personal Service firms). Such a negative ratio implies that firms are, on average, financing part of their fixed assets with short-term debt.

According to this pattern, manufacturing firms appear more inclined towards financing a larger share of the operating investment with long-term funds. One potential reason would be a lower cyclical component around these industries (i.e., having a more stable investment requirement, it is reasonable to match it with a more stable financial source). In agreement with this, we also observe that the closer we get to the service sector, the more short-term funds are used to support operating investment needs. The use of short-term funds is even more radical in the most cyclical or sensitive service firms - such as those involved in the entertainment, restaurants and hotels and/or personal service business.

III. Cross Sectional Variation: The Regional Effect

After considering the influence of industry characteristics on FNOs and working capital policies, we examine to what extent these patterns differ across regions. We define four different regions; two corresponding to emerging markets, such as Asia and Latin America, and two consisting of developed markets, such as Western Europe and North America. Since the United Kingdom's (UK) capital and financial markets are more comparable to those operating in the United States (US) (as opposed to the other Western European countries') - being the former more capital market oriented and the latter more bank oriented - we present information on UK firms separate from other European countries' firms. Thus, we end up presenting five regions. The information is summarized in Table II.

Even though we observe ample variation across regional mean ratios, the difference is significantly narrowed when we consider median figures. This suggests that most of the variation is caused by extreme values (for example, due to the presence of some pharmaceutical and sophisticated medical equipment firms in the US - which lead to higher average FNO ratios in that region - and a number of financially constrained firms in emerging markets -which tilt average working capital ratios towards lower figures).

To gain a better understanding of working capital patterns, it is interesting to examine firms' data classified by industry and region.12 Within that setting, we observe several interesting features. For example, we observe that firms in the Pharmaceutical Products industry, which were reported as the ones with larger FNO on revenues in Table I, show a large variation across regions, ranging from 0.99 in Asia to 4.95 in North America.13 Something similar happens with firms in the Precious Metals industry, in which the FNOs on revenues ratio ranges between 1.30 in North America to 5.31 in Europe. In the next section we undertake this analysis using a more accurate method in order to learn about the relative impact of different drivers on FNOs and working capital patterns.

IV. Cross Sectional Patterns: A Variance Decomposition Approach

To better understand what drives the cross-sectional variation of these ratios, we follow a variance decomposition analysis. Variance decomposition analyses have been approached using either Components of Variance techniques (ANOVA) procedures. In this paper, we analyze the components of variance using a cross-classified nested model.14 The basic model for assessing firm, industry and country effects is the following:

x^sub tiks^ = μ + α^sub k^ + [straight phi]^sub s^+ δ^sub ks^ + β^sub i^ + ε^sub tiks^. (1)

Where xtih denotes the dependent variable (FNO / Revenues or WC / FNO ratios) for year t, at the Ith firm, in the kh region, and 5rt industry. This model describes xtiks as an overall mean (average ratio of all firms over the entire period), using a country or regional-specific effect, ak, an industry effect, f5, an interaction between regional and industry effects, δ^sub ks^, a firm-specific effects β^sub i^, and an error term ε^sub lkst^ The inclusion of the interaction geographical-industry effect follow from recent findings that report the presence of an important industry cluster effect in different countries - e.g., Brito and Vasconcelos (2006); Makino, Isobe, and Chan, (2004); and McGahan and Victer, (2008).

The usual assumption is that the error term, sikst, corresponds to random disturbances, drawn independently from a distribution with zero mean and constant but unknown variance^2. The model also assumes that all the other effects, are realizations of random processes with zero mean and constant, but unknown, variances. Finally, the model assumes that all the covariances equal zero.15

We estimate Equation (1) using a cross-classified nested model. It is nested, since the annual observations are nested at the firm level; the firm-specific effect is nested in the interaction region-industry which in turn is nested simultaneously in each of the main effects - region and industry. It is cross-classified, because it simultaneously estimates these two main effects.

A. The Total Sample

We start by examining the components of variance of FNO and working capital for the entire sample in order to explore the relative magnitude of the different effects. We consider firm, industry and regional effects. The results are reported in Table III.

The first thing we observe in Table III is that most of the variation both in FNO and working capital ratios is explained by firm effects and residuals. Interestingly, while the residuals seem to have major relevance in the variation of working capital, the company effect is more important at the FNO level. Given that we interpret working capital policy as a financing choice, the intuition is straightforward: working capital decisions are more sensitive to unexpected variations in financial markets conditions (the unexplained component accounts for more than 62% of the working capital variance). On the contrary, the variance of FNO is more closely related to firm-specific effects, such as corporate strategy (company effect accounts for 52.7% of the FNO to sales ratio variance).

We find industry to have a more relevant impact on FNO than on working capital ratios (6% vs. 2.7%). Notice also the role of the region ? industry interaction. It seems that while FNOs' industry patterns are relatively shared globally, working capital patterns are more influenced by country or region specific features.

In order to test the impact of inter annual variations within the model, we run an alternate version that includes a full set of year dummies interacted with region. This specification, not reported in the paper, does not show any significant difference with respect to reported results. As expected, we find that year dummies are significant, showing that year effects account for variations in working capital and FNOs. Yet, the main random effects under analysis are similar to those found in the paper.16

Even though using this framework we can observe some interesting patterns, more information can be expected from splitting the sample between developed and emerging markets.

B. Developed and Emerging Countries

Following the United Nations classification, we grouped countries into two broad categories: developed and emerging countries. We repeat the variance decomposition in each of these samples. Results are summarized in Table IV.

The differences between the average ratios of emerging and developed economies are somewhat suggestive. Particularly, we find differences in working capital ratios to be larger than differences in FNOs. This is interesting (again, given that working capital has a financing interpretation), since these deviations could be associated to some sort of financing constraints of less developed markets.

We also find some interesting patterns regarding FNOs' variance decomposition. The data suggests the impact of industry characteristics on FNO to be more relevant for developed than for emerging countries (7.5% vs. 1.9%), as expected. In addition, the country effect seems to be more significant in explaining the variation of FNO in emerging economies (5.6% vs. 0.3%). Both findings suggest that these FNO ratios, within emerging economies, are likely to be affected by certain constraints rather than being unconstrained goals. Emerging economies are more volatile (i.e. which can also be observed in their higher unexplained - inter annual-coefficients), and are likely to need permanent adjustment to unexpected changes, rather than being concentrated on keeping up with optimal industry figures; there is less possibility of long-term planning. This might also be supported by a larger proportion of unexplained changes in emerging countries (46.1%vs. 36.9% in developed ones).

Regarding working capital ratios, the variance decomposition does not suggest clear differences between emerging and developed economies. One possible explanation for this puzzling result can be found in our sample composition. The fact that firms in the developed markets represent a larger coverage of the country corporate landscape -that is, in more developed countries a larger number of firms quote in the capital markets and report data that is captured in the dataseis - can bias our results. In emerging markets, only a few large firms float their stock in the market and therefore, wc are likely to be capturing a smaller fraction of the corporate sector, namely the largest and more efficient firms.17 This sample bias is likely to hide a significant portion of the differences in working capital management across different regions. Alternatively, this could be explained by the fact that there is a wide variety of countries within each category -in the next section, we propose a finer regional decomposition in order to evaluate this case.

C. Regional Perspective

In order to enhance our analysis, we follow a finer regional decomposition. We grouped firms into our four main regions and applied the variance decomposition analysis to each subsample. A major concern with this approach is that when generating the subsamples we are imposing a restriction in only one dimension of analysis - the region effect - biasing the overall results against the geographical effect. Forthat reason, we will not focus on the results per-se but in the comparison between regions. Results are summarized in Table V.18

A few additional patterns emerge from Table V. On the one hand, the emerging market grouping seems to be too coarse. Particularly, Asian average ratios do not seem to differ so widely from both European and North American ones. Latin American firms are the ones that present clear differentiation. For example, while the average FNO to revenues ratio is around 75% in the first three regions, it is barely 48% in Latin American firms. Similarly, the average working capital to FNO ratio in Latin American firms is between lA or lA of what it is in the other three regions. It is possible that Latin American firms present higher constraints to arrive at optimal figures - both in terms of investment and financing - given that the Asian tigers have suffered less volatility and have received larger financing inflows during the period under analysis.

In terms of variance decomposition, Table V seems to confirm the previous findings. On the one hand, industry characteristics influence FNO in developed economies more than what they do in emerging countries. Yet, the country effect looks relevant only in determining FNOs of Asian countries (as opposed to Latin American ones). Once more, even though the working capital variance decomposition does not show clear patterns across regions, we do find the country variable to be somewhat important among Latin American economies; certainly, in terms of financing, it is not the same to be in Brazil, Chile, Argentina, Bolivia, etc. To expand these results, it would be necessary to run a deeper economctrical analysis, using country specific data.

D. Note on Robustness

Theoretically the investment intuition we are trying to capture with the concept of FNOs is the operating investment of the firm; therefore, any non-operating factor included in this figure would have a distortive effect. It is well known that corporate cash holdings go much beyond a transaction component; in fact, there is a prolific research area analyzing many other motives for holding cash.19 Therefore, the cash variable included in our estimation of FNO -which corresponds to the variable cash and marketable securities in Compustat- is likely to also include non-operating balances. Unfortunately, it is not possible to separate operating balances from precautionary, strategic, or any other sort of cash. As a result, the definition of FNOs used throughout Tables I through V might be overstating the operating investment (moreover, this could have a differential influence across regions, for example, based on differential inflation rates, or future cash flows volatility).20 In order to circumvent this problem, and as a robustness check, we run the whole model with a different specification which now is tilt towards the opposite extreme. We remove cash holdings from the computation of the FNO, which are now estimated as net of cash. The results are very similar to those obtained in the base case model and are available upon request.

Finally, to avoid the inclusion of some other non-operating current assets into the operating investment of the firm (for example, some intangibles or tax credits) we estimate FNOs by simply adding the basic individual components, arriving to an alternate (narrow) specification. Therefore we define FNO = Trade Receivables + Inventories - Trade Payables. This specification captures only the truly operational investment (nevertheless, we run the model on this new specification both including and excluding cash). Once again, results are very similar to the basic model, and are available upon request. This finding suggests that the definition of FNO we present is not introducing any bias, at least, within this sample.

V. Conclusion

In this paper, after presenting what we believe to be a more useful interpretation of working capital, we have pursued a preliminary exploration for a global sample. By looking at the differences across various groups, we have aimed to motivate further analysis that would lead to more relevant answers.

First, we restate the intuition of working capital, based on the consideration of two complementary concepts: financial needs for operations (FNO) and working capital, which we directly connect to the investment and financing component, respectively. Under this view, working capital is interpreted as the long-term capital financing operating investment.

After analyzing the summary statistics of our variables by industry and region, we use a cross-classified nested model with mixed effects, to explore the main patterns that explain the variance of FNO and working capital ratios in a variety of empirical settings.

Our main findings can be summarized as follow:

At the aggregate level, we find most of the variation in FNO and working capital ratios to be captured by firm effects and unexplained (inter annual) residuals. Yet, while the latter component has more relevance in capturing the variation of working capital, the company effect is more prevalent at the FNO level. Additionally we find the industry effect to have a more relevant impact at the FNO level. That is, whereas the investment decision is more influenced by the industry and firm strategy, the financing choices are more sensitive to financial market conditions.

When we compare average ratios between emerging and developed economies, we find differences in working capital to be larger than differences in FNOs; this could be associated to a larger influence of financing constraints in the emerging market context. Next, even though financing constraints appear to have a larger impact on financing decisions, we also find some influence on FNOs. More specifically, we find that while the impact of industry characteristics on FNO prevail in the developed world, the country effect is more significant in explaining the variation of FNO in emerging economies.

Finally, a more refined study on regional subsamples suggests the impact of financing constraints -which prevent firms from achieving optimal figures- to be more prevalent among Latin American countries. Moreover, following a working capital variance decomposition, we find the country effect to be more relevant among this group. These findings match with the perception that Asian economies have enjoyed more foreign investment inflows and that, within Latin American countries, the situation has been dissimilar.

This paper leaves a number of questions open for further research. It would be interesting - now that we have a more accurate interpretation of working capital - to explore a more complete set of determinants of working capital, to measure the impact of working capital policies on profitability and on the probability of bankruptcy, and to evaluate to which extent these effects relate to the efficiency of the financial and capital markets in which the firms operate.

An answer to these questions is absolutely critical if we want to emphasize the importance of the working capital choice from a managerial perspective. Even though, there are some studies that analyze the link between working capital management and firms' profitability, they all interpret working capital as an investment component, and therefore, tend to only concentrate on the correlation between the cash conversion cycle and firms' profitability.21 Our understanding of working capital from the financing perspective leads to a completely different framework for this analysis. The level of working capital alone is not an indication of good or bad working capital management policies. Being a financing choice, working capital has to be determined as a function of the size of the operating investment and its corresponding volatility. Given that the variation of the activity level could be caused by either seasonality or growth -with obviously different capital structure implications-, it is critical to understand its source. Also, the relevance of the working capital choice and its influence on profitability is likely to be affected by the liquidity and risk characteristics of the specific market in which firms operate. Furthermore, we believe that the relation between working capital and profitability is unlikely to be captured by a linear model. Rather, we consider that setting wrong levels of working capital is likely to produce more noticeable effects when going beyond certain thresholds and in certain market conditions; effects that will certainly break any sort of linearity. We believe these observations leave an enormous room for future research, which would need to incorporate these factors as part of an integrated framework.

Similarly, it would be interesting to consider the relation between working capital policies and financial distress. There is an obvious trade-off between financing costs and rollover risk, which is expected to depend on market conditions and development. Therefore, there is another avenue of research that could analyze the correlation of inefficient working capital policies and events such as financial distress or bankruptcy, as well as its dispersion across different markets.

Finally, it would be interesting to analyze whether there is an optimal working capital ratio in general, and whether it should change according to industry, region and year specifications. Moreover, it would be interesting to explore the relevance of FNO and working capital individual components across countries and regions; in particular, whether deviations are more relevant depending on the specific component.

Sidebar

Moreover, framing working capital practices within the financing of operating investment helps to understand its key drivers, and to differentiate them -and their relative importance- from the key factors that shape the operating investment of a firm.

Sidebar

...working capital decisions are more sensitive to unexpected variations in financial markets conditions (the unexplained component accounts for more than 62% of the working capital variance). On the contrary, the variance of FNO is more closely related to firm-specific effects, such as corporate strategy (company effect accounts for 52.7% of the FNO to sales ratio variance).

Sidebar

It is possible that Latin American firms present higher constraints to arrive at optimal figures - both in terms of investment and financing - given that the Asian tigers have suffered less volatility and have received larger financing inflows during the period under analysis.

Footnote

1 Faus (1997), Genoni and Zurita (2003), Hill, Kelly and Highfield (2010), and Preve and Sarria- Allende (2010) provide a more comprehensive description of working capital management and its importance for corporate finance.

2 See Ferris (1981), Emery (1984), Smith (1987), Brennan, Maksimovic, and Zechner (1988), Mian and Smith (1992), Lee and Stowe (1993), Long, Malitz, and Ravid (1993), Biais and Gollier (1997), Petersen and Rajan ( 1 997), Frank and Maksimovic ( 1 998), Cunat (2000), Burkart and Ellingsen (2002), Himmelberg, Love, and Sarria-Allende (2008), among others.

3 See Petersen and Rajan (1997), Wilner (2000), Molina and Preve (2009 a, 2009 b), and Love, Preve, and Sarria-Allende (2007) among omers.

Footnote

4 See Baumöl (1952), Opler, Pinkowitz, Stulz, and Williamson (1999), Bates, Kahle, and Stulz (2006), among others.

5 See Singh (2008), Michalski (2007), Carpenter, Fazzari, Petersen, Kashyap, and Friedman (1994), Carpenter and Levy (1998), Titman and Wessels (1988), Faulkender and Petersen (2006), among others.

6 Papers analyzing specific topics deal with issues such as debt structure (Bolton and Scharfstein, 1996), debt maturity (Danisevska, 2002; DemirgucKunt and Maksimovic, 1996; Barclay and Smith, 1995; Aivazian, Ge, and Qiu, 2005), etc. Other papers cover specific contexts, such as emerging economies (Demirguc-Kunt and Maksimovic, 1999; Schmukler and Vesperoni, 2000; and Broner, Lorenzoni ,and Schmukler, 2004).

7 See Hill et al. (2010) and Baños-Caballero, García-Teruel, and MartínezSolano (2010).

8 We define long-term capital as the sum of long-term debt and equity. See Preve and Sarria- Allende (2010) for a more comprehensive treatment.

9 Even though operating liabilities include a variety of concepts (such as account payables, accrued taxes, wages, etc.), in what follows, and without loss of generality, we will reduce this concept to the most significant component, namely account payables.

Footnote

10 This link is consistent with Fazzari and Petersen (1993) and Kieschnick, Laplante, and Moussawi, (2009). An additional reference to this topic could be found in Hill, Kelly and Highfield (2010).

Footnote

11 Which is natural, given the financing interpretation of this concept (see Frank and Goyal, 2009).

Footnote

12 Which are obtained by merging Tables I and ?; not reported in the paper, but available upon request.

13 This figure could be capturing some non-operating current assets such as idle cash.

14 Both techniques, "Analysis of Variance" (ANOVA) and "Components of Variance" (COV), estimate how much of the variance of the dependent variable is explained by the categories included as independent variables; but while ANOVA computes the variance of the estimates considering each categorical variable as having a fixed set of possible realizations, the COV technique computes the corresponding variance of the estimates, allowing the individual realizations wimin each categorical independent variable, to be randomly selected from an infinite population. See Brush, Bromiley, and Hendrickx (1999).

Footnote

15 It should be noticed that the model is of mixed effects, where the grand mean is the only fixed effect and all the others are random effects.

Footnote

16 We decided to keep the former model, for simplicity and brevity.

Footnote

17 Previous research has found that size is one of the determinants of some of the components of the FNOs (for example, Petersen and Rajan (1997) and Molina and Preve (2009a), among many others), as well as a factor that might influence capital structure decisions (see, for example, Titman and Wessels (1988) and Rajan and Zingales (1995), among others). In this setting, however, we are not exploring the specific determinants of FNOs and working capital, but rather helping characterize die investment and financing components through a variance decomposition analysis. Widiin this setting, the impact of size, as a specific factor, should be captured by the ID effect. Nevertheless, a smaller cross-sectional variation across the emerging market sample might influence the results of our analysis

18 Table V does not include UK figures, since we cannot explore the impact of country effects in a single country setting.

Footnote

19 See for example Dittmar, Mahrt-Smith and Serveas (2002), Opler, Pinkowitz, Stultz, and Williamson (1999) and Almeida, Campello, and Weisbach (2003), among others.

20 We thank an anonymous referee for pointing out mis issue.

Footnote

21 See Deloof (2003), García Teruel and Martinez Solano (2006), Lazaríais and Tryfonidis (2006), Nobanee and Hajjar (2009a,b), Nobanee (2009), Nobanee, AlShattarat, and Haddad, (2009), among others.

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AuthorAffiliation

te would like to thank Gabriel Noussan, Guillermo Fraile, Javier Garcia Sanchez, Florencia Paolini, and Pablo Slutzky, for their useful comments and discussions.

Hernán Etiennot is an Assistant Professor of Accounting and Control at the IAE Business School at Universidad Austral in Buenos Aires, Argentina. Lorenzo A. Preve is an Associate Professor of Finance at the IAE Business School at Universidad Austral in Buenos Aires, Argentina. Virginia Sarria Allende is an Associate Professor of Finance at the IAE Business School at Universidad Austral in Buenos Aires, Argentina.

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Copyright Financial Management Association International 2012

The Corporate Soap-Opera "As the Cash Turns": Management of Working Capital and Potential External Financing Needs

Lifland, Steven A.Review of Business; New York Vol. 32, Iss. 1,  (Winter 2011/2012): 35-46.

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Many analysts have shifted their focus from a corporation's uncertain bottom line to the firm's potential cash flow as a means of ascertaining company value. This paper posits that firms have been emphasizing the days of the working capital cycle. A pool of 'found funds' exist as the company efficiently manages its current assets and liabilities. This paper also measures the potential external financing needed to meet the working capital requirements. If the costs associated with working capital accounts exceed the benefits of such items as the holding of specific inventory levels and/or the issuing of greater trade credit, the firm's future dependence on debt financing will be impacted. [PUBLICATION ABSTRACT]

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Executive Summary

Many analysts have shifted their focus from a corporation's uncertain bottom line to the firm's potential cash flow as a means of ascertaining company value. This paper posits that firms have been emphasizing the days of the working capital cycle. A pool of 'found funds' exist as the company efficiently manages its current assets and liabilities. This paper also measures the potential external financing needed to meet the working capital requirements. If the costs associated with working capital accounts exceed the benefits of such items as the holding of specific inventory levels and/or the issuing of greater trade credit, the firm's future dependence on debt financing will be impacted.

Introduction

There may be a slow trend where firms are switching their focus from the uncertainty of the profit and loss statement to the balance sheet. Corporations are placing an emphasis on the strength of their balance sheet, specifically looking at cash and the corresponding liquidity. When firms hold too much cash, equity investors would rather see the firms earning interest from short-term investments. A relatively large cash balance puts pressure on firms to buy back shares or pay dividends. This paper looks at the trend in the corporation's management of its working capital needs. This is accomplished by measuring the liquidity and managerial efficiency of the company's current assets and liabilities. The tools used to analyze this are the individual working capital ratios, and the overall working capital conversion cycle or the days of working capital. The discussion about a firm's current position typically involves the relationship between current assets and current liabilities, and working capital represents the excess of one over the other.

This paper advocates that the aggregate working capital dollar figure by itself does not address the question of a firm's liquidity. Regardless of its magnitude, this figure does not assess the quality of the corporation's dayto-day operations. Two of the better known measures of liquidity are the current and quick ratios. However, these ratios do not give the investor the full evaluative information that is required to appraise the position of liquidity. These two ratios share the same weakness in that they are conceptually based on the notion that the firm will liquidate all its current assets to cover all its current liabilities. Yet, investors look at firms as going concerns. The emphasis of this work is on the actual time that it takes for a company to convert its capital assets into cash in order to pay its current obligations. What is the extent to which firms have strengthened their cash flow position by reducing the number of days that they must tie-up cash in their working capital? These monies are waiting to be spent on future growth or liability reduction. Funds are often tied up in unpaid customer bills or old inventory. This 'found' cash in the balance sheet and the observation of the working capital habits of firms may tell us how firms are approaching this subject, and how efficiently they are managing the process.

Literature Review

The existence and maintenance of working capital is the lifeblood of a corporation. It is the cash flow that revitalizes operations or slows it down to inoperable levels. Regardless of the size of the company, the management of working capital accounts should influence its financial health. Kargar and Blumenthal (1994) found that small businesses were significantly impacted by management's ability to successfully plan the cash requirements of the firm. Managers need to monitor the ratio of total working capital to total company assets, as a relatively high figure can signal future strains on the operational financial health of the firm. Filbeck and Krueger (2002) report the ordinal rankings of industries across working capital management variables for the period of 1996-1999 as reported by a CFO Magazine survey. The working capital measures were not static but the specific ratios for different industries were stable over time.

The majority of the empirical studies on the management of working capital has centered on the possible link to profitability. Jose et al. (1996) found evidence that U.S. firms following an aggressive working capital policy saw their profits enhanced. There was a significant negative relationship between the cash conversion cycle of a firm and its profitability. Looking at U.S. firms during the period of 1974-1994, Shin and Soenen (1998) found evidence that the reduction of net trade credit increases profitability. When they focused on individual industries, that connection was not that strong. Deloof (2003) studied a sample of large Belgian public firms between 1992-1996 and found their profits improved as they reduced their days of receivables and inventories. In a sample of 58 small manufacturing firms in Mauritius, over the period of 1998-2003, Padachi (2006) found that the companies with aggressive working capital policies were met with lower profitability. Ganesan (2007) studied a sample of firms from the telecommunication equipment industry and while he found a negative relationship between working capital efficiency and profit margins, the results were not significant for that industry. In a more general study, Raheman and Nasr (2007) analyzed 94 Pakistani public firms from 1999-2004 and found a significant negative relationship between a high investment in liquid assets and profitability. Ramachandran and Janakiraman (2007) found that the operating profit of the firm had a negative relationship with the days of accounts payable. They felt it implied that the less profitable firms waited longer to pay their bills. In a current work, Mohamad and Saad (2010) obtained a sample of 172 firms listed on the Bursa Malaysia exchange over the time period of 2003-2007 and found significant negative associations between working capital variables and a firm's return on assets and return on invested capital.

In a study of the aggregate cash conversion cycle, Moss and Stine (1993) found that a negative relationship existed between the size of the firm and the length of the cycle. Larger firms tend to have shorter conversion cycles. Taking a survey of 78 domestic firms and 58 foreign firms, Maxwell, Gitman, and Smith (1998) found that the majority of the sample took advantage of float to control their disbursements and collections but only the foreign firms had significant usage. Some firms took no advantage of float in handling their working capital needs. Looking at a sample of merchandising and manufacturing firms, Uyar (2009) found that the latter group had longer conversion cycles and that there was a negative relationship between the size of the firm and the length of the cycle.

The determinants of working capital management were explored by Chiou and Cheng (2006) where factors such as an industry effect, firm performance, and firm size did not provide consistent conclusions. Two factors that did prove to be consistent were operating cash flow and leverage. Padachi (2006) found that there was an increasing trend in the shortterm component of working capital financing. In another test of the components of working capital management, Nazir and Afza (2008) looked at the operating cycle, operating cash flow, size, ROA, and leverage and found that the operating cycle, ROA, and leverage were significant.

The theoretical support for the management of working capital centers on how that supervision meets the short-term financial requirements of the firm. This paper extends the literature by recognizing that many firms resort to external financing to support their working capital needs and looks for the presence of any significant trends. Specifically, this paper measures the ability of a firm to manage its working capital components over the years 2004 through 2009. It argues that an important relationship exists between a firm's working capital components and their subsequent financing. While a firm can reflect a profitable bottom-line, its inability to generate sufficient positive net cash flows from operations will likely put pressure on management to seek out additional financing to support its working capital needs. If the costs associated with working capital needs exceed the benefits of such items as the holding of specific inventory levels and/or the issuing of greater trade credit, the firm's future dependence on debt financing will be impacted. This in turn can influence profits.

Sample Data

The firm-industry data was obtained from CFO Magazine's annual Working Capital Management Survey for 2010, which was done in conjunction with REL Consultancy Group. It included 1,000 of the largest public corporations headquartered in the United States, broken out into 58 industries. The financial sector is not included. In an effort to report comparable financial analysis, the data was adjusted to reflect the impact of any off-balance sheet arrangements, financing revenues and receivables, and LIFO inventory reporting.

The survey uses the Global Industry Classification Standard (GICS) to categorize companies into appropriate industries. In an effort to streamline the number of relevant industries for study, this paper follows the work of Deloof (2003) who, due to the nature of activities, excluded firms engaged in banking and finance and insurance industries from his sample. Shin and Soenen (1998) looked at a pooled sample of firms comprising eight industries that were formed by the Standardized Industrial Code (SIC). Performing an analysis of working capital results across industries, Krueger (2000) analyzed the 2000 CFO Survey data from the period of 1996 - 1999 and found the relative rankings of the industries across the chosen working capital metrics.

This paper, building on those rankings, tested for the significant trends in the specific working capital measures of the days of receivables, inventories, payables and the aggregate working capital cycle over an extended period of time. Five industries are analyzed over the six-year time period of 2004 through 2009. The industries and their corresponding GICS are: Chemicals (GIC = 151010), Durables (GIC = 252010), Food (GIC = 301010), Health (GIC = 351010), and Oil-Gas (GIC = 101020). Using the GIC codes, the working capital data for the sample firms within these industries was collected from the Compustat annual industrial and full coverage files. The analysis uses stacked data for the period 20042009 and resulted in 118 total observations.

Measures of Performance

The typical definition of working capital is cash plus receivables and inventory-less items such as payroll, money owed to suppliers and shortterm debt obligations. The determination of the investment quality of a company's working capital is done through the measurement of liquidity and efficiency related to a firm's current position. One of the strongest ways to accomplish this is to convert specific turnover ratios into a figure that measures the time it takes to convert a firm's unique working capital assets into cash in order to meet its current obligations. In other words, how long is cash tied up in support of working capital needs?

The traditional cash conversion cycle is comprised of receivables, inventory, and payables. It's an additive concept, yet the denominators are not the same for these variables. This paper differs from the prior works of Lazaridis and Tryfonidis (2005), Padachi (2006), and Garcia-Truel and MartinezSolano (2007) and creates the metric, Days of the Working Capital Cycle (DWCC) variable, expressed as a percentage of sales. This brings about a balanced comparison across each element of the model, provides true comparisons between industries, and indicates the number of "days' sales" the firm has to finance its working capital (Shin and Soenen, 1998).

Another extension of this paper is to review the external debt funding associated with the firm's working capital requirements. A relatively fast cycle creates liquidity and acts as a positive indication of the quality of the current assets and the impact on the financing needs of the firm. Just as important is the efficiency of managing the associated receivables, inventory, and payables. This paper tracks the historical record, 2004-2009, of a firm's DWCC and compares it to rival companies in the same industry, providing some insight into the investment quality of the balance sheet.

The efficient management of these assets includes maintaining adequate product levels, monitoring of appropriate credit/payment terms, and mitigating any situation where the servicing of the working capital may significantly constrain the firm's cash position. As referenced above, the model being used to measure working capital asset efficiency is as follows:

Days in the Working Capital Cycle (DWCC) = the Days Accounts Receivable (DAR) + the Days of Inventory (DINV) less the Days Payables (DAP).

The number of days accounts receivable (DAR) is calculated as year end accounts receivables net of allowance for doubtful accounts divided by average daily sales, where DAR = AR/(net sales/365). It represents the average number of days that it takes for a firm to collect payments on their credit sales. There is a negative relationship between this figure and the enhancement in cash flows for the firm. The expectation is to see a trend where this ratio decreases over time, implying management's efficient use of its receivables and related credit policies. An increase implies a worsening situation. The number of days of inventory is determined by dividing the yearend inventories by net daily sales, where DINV = INV/(net sales/365). It reflects the average number of days that inventories are held by the company.

There is a negative relationship with a firm's cash flow position, as a relatively high DINV implies that monies tied up in inventory are not being recycled adequately during the operating period. The turnover is not adequate. A decrease in the DINV is an improvement in the time that inventory is held and helps to curb stale or obsolete levels of inventory. It also represents an improvement in cash inflow from the sale of inventory. An increase is a deterioration of the situation. The expectation over the time period is that firms make an effort to control their monetary commitment for this critical current asset.

In the case of the number of Days of Accounts Payable, this ratio reflects the average time that it takes for companies to pay their suppliers and vendors. It is calculated as DAP = AP/(net sales/365). A positive relationship exists between DAP and a firm's cash flow. As the ratio increases, it reflects the longer time period that a firm takes to settle its payment commitments. An increase in DAP can be interpreted as an improvement, as the firm is using the money of others and hence is able to devote its own cash for other commitments. The expectation is that firms will increase this ratio up to a level that reflects that the incentives of purchase discounts are beneficial. While cash flows are enhanced by a delay of paying for goods and services, this must be tempered with the fact that firms who continually pay "late" run the risk of paying penalties and being dropped from a supplier's customer list.

This paper extends the literature by recognizing that implicit in the working capital management cycle is the need of financing for the working capital. In order to meet operational cash flow needs, firms may rely on financing their working capital through the use of loans. This paper measures the potential financing need that arises given the expected growth in sales and the unique working capital conversion cycle (DWCC) of the firm. While the days of payables (DAP) reflect internal funding, the continual need to buy and sell inventory, along with the extension of credit and the subsequent cash collection, creates a reliance on external financing.

Methodology

The primary focus of this paper is to measure the degree of emphasis placed upon the handling of working capital accounts. This is done by following an empirical framework similar to the one used by Garcia-Truel and Martinez-Solano (2007). Their study divided their sample of firms, over the period 19962002, into four quartiles where the first quartile represents the least profitable firms and the fourth quartile is the most profitable. This study extends the literature by analyzing the trends in working capital needs over the more current time period of 2004-2009 and investigating a firm's subsequent reliance on debt to finance their working capital needs.

In order to determine if there have been any significant changes in the amount of cash tied-up in individual and aggregate working capital accounts over the years of 2004 through 2009, this paper performs a t-test on two paired or matched samples. The objective is to determine if the mean of the differences between the two populations is equal to a specified value, zero.

Leí DWCR represent the difference between the specific working capital ratio observations. The hypothesis states:

Ho: DWCR = 0 (the difference between the two observations is 0)

On average, there is no difference between the median working capital ratios for firms.

Ha: DWCR F 0 (the difference is not zero) (There is a difference between the two samples)

On average, there is a difference between the median working capital ratios for firms.

Working capital measures for firms within an industry change across time.

Let DPfF represent the difference between the specific potential external financing needed due to working capital requirements for each observation. The hypothesis states:

Ho: DPEF = 0 (the difference between the two observations is 0)

On average, there is no difference between the median potential external financing models.

Ha: DPEF F 0 (the difference is not zero) (There is a difference between the two samples)

On average, there is a difference between the median potential external financing in the observations. Potential external financing amounts for firms within an industry change across time.

The test statistic is t with n-1 degrees of freedom. The difference between the means of the samples is not likely to be equal to zero, usually due to sampling variation, and the hypothesis test attempts to answer the question of whether the observed differences are sufficiently large enough to support the notion that the alternative hypothesis (Ha) is true.

The answer is in the form of a probability, the p-value.

* If the p-value associated with t is low (<= 0.05), there is evidence to reject the null hypothesis at the 95% confidence level.

* Two other common rates of acceptance are when the p-value associated with t is low and the null hypothesis can be rejected at the 99% (< = 0.01) and 90% (< = .10) confidence levels. It implies that there is a high probability that there is a difference in means across the paired observations.

The differences in the means of the working capital ratios are statistically significant over a specified time period.

Empirical Results

In order to determine statistically significant changes in the management of working capital for firms within the industries of Chemicals, Durables, Food, Health, and Oil-Gas, the t-test to paired data was performed over the time period of 2004 through 2009 with the results presented in Table 1 below.

Within each of the five industries, the median days of receivables, inventories, payables, and the working capital cycle are determined. The median days are calculated, not the arithmetic average days. The arithmetic average or mean is the total of the numbers divided by the amount of numbers. The median, which is still a form of an average, is the middle number in a set of numbers, when they are listed in numerical order. The use of the median protects from outliers influencing the results. The median, having less bias, tends to look more realistic than the arithmetic mean.

Next, the percentage changes in these median days are calculated to determine whether any significant trends in these working capital variables took place during the specified sixyear time frame. Negative median percentage changes imply that the firms within these industries saw their working capital turnover ratios increase and their corresponding days of working capital decrease. This means that the industry's firms experienced a decrease in the amount of days that cash needed to be 'dedicated' to receivables, inventories, payables, and the over-all working capital cycle. The corporations can use these 'found' funds to do things such as cut debt or increase net income, or improve their return on capital. Positive median percentage changes imply that firms within these specific industries saw their working capital turnovers decrease and the number of days that cash was tied up in working capital increase. This will constrain the cash flows of the firm and could negatively impact profitability.

The t-test to paired data was run for each of the four working capital ratios designated as DAR, DINV, DAP, and DWCC. The tests concentrated on the median percentage change in the individual working capital ratios and the aggregate working capital cycle over the time period of 2004 through 2009. The test looked at 118 firms from five industries. All five industries reflected significant median percentage changes in their overall working capital cycle. The Chemicals industry was significant at the 5% level while the others were significant at the 10% level. Chemical, Food, and Health saw significant decreases in the time that cash was needed to support their working capital accounts. Durables and Oil-Gas industries saw their number of days that cash had to be devoted to working capital needs rise over this same time period. In the case of the Chemicals industry, the median percentage change in the days of receivables (DAR) was 1.33% and significant at the 99% confidence level having a p-value of (.000).

Their Days of Payables had a significant median percentage change of 8.85% with a p-value of (.100). While the Days of Inventory had a negative percentage change of 1.80%, it was not statistically significant. The entire working capital cycle showed a significant negative median percentage change of 3.61%. In the case of the Durables industry, the median percentage change in the Days of Payables is a positive .13% and is significant at the 95% confidence level having a p-value of (.045). While the Days of Receivables had a negative change of 1.95%, it was not statistically significant. The working capital cycle had a median percentage change of 1.03% and was statistically significant at the 90% confidence interval. Overall, the number of days that funds had to be spent on working capital increased over this time period.

Within the Food industry, the median number of days that inventory was on hand decreased thereby improving cash flows but was not statistically significant. Both the Days of Receivables and Days of Payables increased over the same period of time. However, the increase in the inventory turnover appears to have been strong enough to influence the Days of Working Capital Cycle, as it had a negative median percentage change of 5.02% with a significance at the 90% confidence interval.

The Health industry saw each of its ratios having negative median percentage changes between 2004 and 2009. The Days of Receivables and Days of Payables were not significant but the median percentage change in Days of Inventory within the industry was 7.84% and significant with a p-value of .100. The Days in the Working Capital Cycle had a negative percentage change of .41% with a significant p-value of (.090). The implication is that this industry saw a significant reduction in the aggregate days that cash was tied-up in working capital requirements.

The Oil-Gas industry reported statistically significant results for each of the working capital ratios. The Days of Receivable and Days of Payables both generated positive median percentage changes where the p-values were (.000) and (.071) respectively. The industry significantly increased its inventory turnover and reduced the number of days that inventory was on hand as it had a median percentage change in days of a negative 9.21% and was significant at a 99% confidence level. The cycle of working capital days had a positive percentage change of 2.90% and was statistically significant with a p-value of (.070).

A critical variable of this study was the Days Working Capital Cycle. Based on the analysis, three out of the five industries reflected significant negative median percentage changes over the 2004 through 2009 time period. On average, there is an increase in their respective asset and liability turnovers and a decrease in the aggregate days that cash must be earmarked for working capital needs. This would be desired. A bi-product of these results is that firms have 'found' funds to direct to other areas such as debt reduction, dividend payments, or stock buybacks.

Days Sales Available and Potential External Financing Requirements

In this paper, the Days of the Working Capital Cycle (DWCC) is an additive concept as the components, Days of Receivables (DAR), Days of Inventory (DINV), and Days of Payables (DAP) are all expressed with sales as the common numerator for the respective turnover ratios used to calculate the days. Shin and Soenen (1998), in an anecdotal example, utilize a variable, the Net Trade Cycle (NTC), which expresses the three components of the cash conversion cycle as a percentage of sales. The DWCC is equivalent to their NTC. The result is that the DWCC represents the number of 'days sales' available to the firm to underwrite its working capital needs, all things considered equal in the situation. The potential financing needs, with respect to the firm's working capital requirements, is measured by incorporating the company's annual sales, projected sales growth percentage, and the ratio of days of the working capital cycle to the days in the operating cycle. A firm within the Oil-Gas Industry sample, Consolidated Energy Inc. (CNX), saw a sales growth of .364% between 2009 and 2008. Its sales were $4537.734 billion in 2009. Its DWCC was 29.881 days, with the days' sales percent being 8.187% (29.881/365=.08187). The estimated financing needed to support the working capital requirement is $1.352 million ($4537.734 x .00364 ? (29.881/365) = $1.352). This calculation was done for each firm in each industry over the time period of 2004 through 2009. There is a positive relationship between the Days of the Working Capital Cycle and the Potential External Financing (PEF). As the DWCC decreases, there is a lower need for external financing. This would imply efficiency with respect to working capital management and better performance, as the literature suggests.

Within Table 2, the median Potential External Financing (PEF) and the median percentage change in PEF is presented for each of the five industries for the periods covering 2004 through 2009 and 2005 through 2009. The percentage change in the potential external financing is calculated for each of the years, and the comparison of means t-test is performed to determine if there were any significant trends over the two time spans.

The results within Table 2 show the median dollar amount of the estimated financing needs within each of the five industries, along with the overall percentage change in these figures from the beginning of the period until the end of the period. The p-values are shown in parentheses. If the median percentage change is positive, it implies that there was an increase in financing needs and that the DWCC had not been reduced enough, or it increased, over the time period. Where the median percentage change reflects a negative direction, it implies that there was a decrease in the external financing needs of the firms within that industry and that the days of the working capital cycle had been reduced.

During the time period 2004 - 2009, the industries of Chemicals, Health, and OilGas showed statistically significant positive percentage changes in the potential external financing needed. These positive median changes reflect that the working capital turnover ratios had increased, which constrained the cash flows to the firm and made it necessary for firms to seek external financing to support their working capital needs. The Chemical and Health industries showed positive percentage changes significant at the 99% confidence level. The oil-gas industry had positive results significant at the 95% confidence level. While neither Durables nor Food industries showed significant results for the time period, there was a positive median percentage change as it appears the days of working capital cycle increased thereby increasing external financing needs over this time period. However, there are improvements in the next five-year time span of 2005 through 2009. Each of the industries had significant results between these years. The Chemicals and Health industries reported improvements in their management of working capital.

The remaining three industries of Durables, Food, and Oil-Gas reported significant positive median percentage changes in their potential external financing with regards to their working capital needs. In the case of Durables and Food, their results had not been significant in the prior time period of 2004-2009 but gained significance in the time period 20052009. The implication for these two industries is that the firms were not able to reduce the days of the working capital cycle and saw their need for external financing rise significantly. The Oil-Gas industry had a positive 37.7% (p-value of .028) and 68.4% (p-value of .000) median percentage change in their potential external financing for the periods 20042009 and 2005-2009 respectively. Chemicals showed a median negative percentage change of -17.5% (p-value= .000), while the Health industry sample had a negative percentage change of -20.1% (p-value=.051). The decrease implies that the turnover ratios increased while the days of the working capital cycle decreased thereby increasing company cash flows. This leads to a fall in the external financing required to meet working capital needs.

In the prior period of 2004-2009, these same two industries had significant percentage increases in the external funding needed to support their working capital accounts. Chemicals reported a positive 50.8% (p-value =.000) and Health had a positive 4.7% (p-value=. 003). These two industries significantly improved their efficiency surrounding their working capital cycle. This in turn decreased their dependence on external financing required to support their working capital position.

Conclusion

Firms that efficiently manage their working capital are characterized as having increasing asset turnover ratios and decreasing days of receivables and inventories over the years, thereby 'freeing up' capital. Corporations use these 'found' funds to improve their supply chains, corporate logistics, and payment systems. The Days of the Working Capital Cycle represents the average number of days that cash must be committed to the management of a company's working capital needs. A decline in the ratio translates into the firm's ability to improve its inflows and management of cash.

During the six-year period of 2004 - 2009, three out of the five industry samples reported statistically significant declines in their DWCC. Specifically, the industries of Chemicals, Food, and Health were able to reduce the number of days that their cash had to be tied-up in maintaining their working capital. The sample industries of Durables and Oil-Gas were not as efficient. Both showed significant positive increases in their DWCC whereby the net effect of the asset turnover ratios and subsequent days of working capital increased, and lengthened the time that cash had to be earmarked for the support of the firm's working capital cycle.

In an extension of the literature, this paper measures the Potential External Financing brought about by the need to meet the working capital requirements. Within the time period of 2004 - 2009, each of the five sample industries reflected a positive percentage change in their estimated external financing related to working capital. This implies that the net effect of their asset turnover ratios decreased, and the days of working capital increased, placing a burden on the need for external financing of the related working capital. During the five-year period of 2005 - 2009, the results show that the industries of Durables, Food, and Oil-Gas still had significant positive increases in their financing needs. However, the sample industries of Chemicals and Health reported significant negative results. The implication is that these two latter industries were able to efficiently increase the net effect of their asset turnover ratios and reduce the number of days needed to underwrite their working capital cycle. Both industries improved their financial strength from the preceding period that had reported an increase in external financing due to how the working capital had been managed. The attention to working capital efficiency by these firms rewards both the investor evaluating the company and the managers running the firm. The rewards can be in the form of taking price discounts on purchases, extending credit terms to speed cash receipts, improved credit ratings, and reducing dependence on short-term borrowings.

Firms that can efficiently reduce the number of days that cash must be committed to their working capital accounts, over the years create a "cash culture" (Liebs, 2010) as opposed to only a focus on the "bottom-line". In the ongoing corporate soap opera, the quest for cash never ceases.

Sidebar

Winner of the Best Paper Award at the 5th Annual Symposium of the Financial Services Institute, The Fallout of the Financial Crisis: Impact on Accounting, Management and Marketing. Held in New York City on September 9-11, 2010.

The Chemicals and Health industries significantly improved their efficiency surrounding their working capital cycle. This... decreased their dependence on external financing. . .

Sidebar

The. ..management of working capital centers on how... supervision meets the short-term financial requirements of the firm.

Sidebar

Firms that efficiently manage their working capital are characterized as having increasing asset turnover ratios and decreasing days of receivables and inventories over the years, thereby 'freeing up9 capital.

Sidebar

Corporations use these 'found9 funds to improve their supply chains, corporate logistics, and payment systems.

References

References

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AuthorAffiliation

Steven A. Lifland, High Point University, North Carolina

slifland@highpoint edu

Word count: 5973

Copyright St. John's University Winter 2011/2012

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