Working Capital Management and Firm Performance in Emerging Economies: Evidence from India

Abstract

This article aims to investigate the relationship between working capital management and firm performance in an emerging market. The analysis is done over a long window spanning across 2000–2014 by using ordinary least square (OLS), fixed- and random-effects model and generalized method of moments (GMM) on 2,327 firm-year observations, a panel data of 179 companies listed on the S&P BSE 500 Index of Bombay Stock Exchange (BSE). For robustness, in addition to accounting performance, market-based performance measure has also been employed to measure firm performance. This study based on India finds a negative relationship between the working capital management and firm performance, necessitating the need to efficiently manage the working capital for enhanced profitability.

Keywords

Working capital management cash conversion cycle profitability emerging markets India panel data GMM

Introduction

Working capital essentially constitutes the funds required to finance the repetitive revenue-generating activities of a firm. Working capital has always been a subject of great corporate attention and the business cycles have put to test the adequateness of working capital in firms, all industries pervasive, across the globe. The way working capital is managed, significantly affects the overall financial performance of any firm and in turn its quintessential survival (Cielen et al., 2004). Moreover, the efficient utilization of working capital investment assumes further importance as it forms a significant portion of total assets.

Working capital management, however, is a very complex task, more so due to the presence of an inverse relationship between liquidity and profitability which therefore requires a trade-off between the two important goals. The primary goal of any firm is profit maximization, but to ensure smooth functioning of operations, the firms also need to ensure adequate liquidity. The competitive landscape induces the firms to focus more on increasing profitability which in the long run cannot be achieved unless the firm develops strategies for increasing efficiency, measured by the working capital. The working capital management consequently becomes a very strategic function in a firm (Baños-Caballero, García-Teruel & Martínez-Solano, 2011; Deloof, 2003; Lazaridis & Tryfonidis, 2006; Shin & Soenen, 1998).

Working capital management encompasses management of cash, receivables, inventory and payables. Along with affecting the liquidity, working capital policy of a firm has an implication on its profitability (Smith, 1980). A very stringent working capital management policy may lead to liquidity crisis, whereas a liberal one may lead to reduction in profitability. A liberal credit policy may improve sales but as a result, bad debts may also rise; conversely, a strict credit policy may negatively affect sales of a firm. There is also a trade-off in inventory holding, as a liberal inventory holding policy may increase inventory holding cost and maintaining lesser inventory levels may lead to stock-out situations and higher ordering cost. The same is true for cash management, more focus on liquidity will reduce profitability and less cash holding may lead to cash out situation. Another important component of working capital is accounts payable. Delaying payments to suppliers may be expensive as we may lose cash discount we would have got on cash payment and vice versa. So managers need to find an optimum level of working capital which leads to efficiency and profitability, thereby maximizing a firm’s value.

The working capital management issues are important for all firms irrespective of size, sector, industry, country and the type of economy. Working capital management and corporate performance has been a subject of empirical research for long in the developed countries (see, e.g., Deloof, 2003; Gill, Biger & Mathur, 2010; Lazaridis & Tryfonidis, 2006; Shin & Soenen, 1998). In the emerging countries, characterized by nascent financial markets and the different market settings efficient working capital becomes all the more relevant (Abuzayed, 2012; Padachi, 2006).

This study attempts to analyze the impact of working capital management on financial performance of the firms in the emerging market of India. For measuring the financial performance, both accounting and market metrics have been used. The independent variables are cash conversion cycle (CCC), accounts receivable period, inventory period and accounts payment period. The study finds a negative relationship between CCC and firm performance measured through gross operating profit (GOP) and Tobin’s Q.

Conceptual Framework and Literature Review

Voluminous research evidence exists on analysis of working capital, its determinants and relationship with firm performance. Sagan (1955) is one of the first proponents of importance of working capital in finance literature and emphasized on the need of managing working capital as it vitally affects the health of a firm. The baton was taken further by early works of Walker (1964) and Weston and Brigham (1972) who propounded and tested three propositions relating to effects of working capital on a firm’s risk. Van Horne (1969) developed a framework for taking working capital decisions by assigning probabilities. These approaches have been further refined and tested by Warren and Shelton (1971) using financial simulation, Cohn and Pringle (1973) and Copeland and Khoury (1980) using capital asset pricing model (CAPM), Hilton (1976) using accelerator models, etc. Richards and Laughlin (1980) devised the concept of working capital cycle which combines the current assets and current liabilities in one number.

The contemporaneous literature has tested working capital management from different angles, such as the effects of working capital on firm liquidity (Chiou, Cheng & Wu, 2006; Kim, Mauer & Sherman, 1998) and the impact of working capital on firm value (Deloof, 1993; Smith, 1980). Furthermore, extensive research, though largely inconclusive, has been done to understand the relationship between working capital management and profitability (Abuzayed, 2012; Deloof, 2003; Gill et al., 2010; Padachi, 2006; Shin & Soenen, 1998, etc.). The empirical evidence on analysis of firm performance based on a firm’s working capital management can be broadly classified into two categories. The evidence in first category indicates that a longer CCC tends to reduce the firm profitability, implying that a firm’s working capital has a negative relationship with profitability and the second category theorizes the positive relationship between the two.

In the US, Shin and Soenen (1998) documented a negative relationship between marginal net operating cycle and profitability of the company, and similar evidence was found by Ebben and Johnson (2011), for 879 small firms. Deloof (2003) using CCC for 1,009 Belgian companies also found negative relationships between gross profit and working capital components. The evidence is corroborated in Europe by Lazaridis and Tryfonidis (2006) for Greece, Baveld (2012) for the Netherlands, García-Teruel and Martínez-Solano (2007) for Spain and Enqvist, Graham and Nikkinen (2014), Rehn (2012) and Yazdanfar and Ohman (2014) for Nordic countries of Finland and Sweden (see Table 1). In Asia, the negative relationship is abundantly evident for developed markets of Japan (Wang, 2002) and emerging markets of India (Ramachandran & Janakiraman, 2009), Indonesia (Wuryani, 2015), Iran (Alipour, 2011; Vahid, Mohsen & Mohammadreza, 2012), Mauritius (Padachi, 2006), Pakistan (Raheman & Nasr, 2007) and Vietnam (Dong, 2010). Likewise, Ukaegbu (2014) posits the negative association between working capital and profitability for four African countries (Egypt, Kenya, Nigeria and South Africa). Other studies in African countries also document the same relationship, such as Mathuva (2010) for Kenya, Ogundipe, Ogundipe and Ajao (2012) for Nigeria and Abuzayed (2012) for Jordan. The findings of these studies imply that maintaining low working capital through less receivables, lower levels of inventory and high payables would lead to high profitability.

Table 1.

Empirical Evidence on Relationship of Working Capital with Firm Performance

Country	Author(s)	Sample Size (Number of Companies)	Horizon	Relationship of Working Capital with Firm Performance
Belgium	Deloof (2003)	1,009	1992–1996	–
Egypt, Kenya, Nigeria, South Africa	Ukaegbu (2014)	102	2005–2009	–
Finland	Enqvist et al. (2014)	1,136 firm years	18 years (1990–2008)	–
Finland and Sweden	Rehn (2012)	13 industries or (1,789 companies)	10 years (2000–2009)	–
Greece	Lyroudi and Lazaridis (2000)	82	1997	+
	Lazaridis and Tryfonidis (2006)	131	2001–2004	–
India	Ramachandran and Janakiraman (2009)	85	1997–2006	–
	Sharma and Kumar (2011)	263	2000–2008	+
Indonesia	Wuryani (2015)	70	2008–2012	+
Iran	Alipour (2011)	1,063	2001–2006	–
	Vahid et al. (2012)	28	2006–2009	–
Japan and Taiwan	Wang (2002)	1,555 Japanese and 379 Taiwanese firms	1985–1996	–
Jordan	Abuzayed (2012)	93	2000–2008	+
Kenya	Mathuva (2010)	30	1993–2008	+
Mauritius	Padachi (2006)	58	1998–2003	–
The Netherlands	Baveld (2012)	50	Crisis period: 2008–2009, non–crisis: 2004–2006	–
Nigeria	Ogundipe et al. (2012)	54	1995–2009	–
Pakistan	Raheman and Nasr (2007)	94	1999–2004	–
Spain	García–Teruel and Martínez–Solano (2007)	8,872 SMEs	1996–2002	–
Sweden	Yazdanfar and Ohman (2014)	137	2008–2011	–
United Kingdom	Thuvarakan (2013)	97	2006–2011	No significant relationship
	Banos–Caballero et al. (2014)	258	2001–2007	Inverted–U–shape
United States of America	Shin and Soenen (1998)	58,985	1975–1994	–
	Gill et al. (2010)	88	2005–2007	+
	Ebben and Johnson (2011)	879 small firms	2002–2003	–
Vietnam	Dong (2010)	130	2006–2008	–

Source: Compiled by the authors.

However, the contradicting evidence on a positive relationship between working capital and firm performance has also been found with the logic that higher levels of accounts receivable and inventory generate higher sales and subsequently should contribute positively to the bottom line. Sharma and Kumar (2011) documented a positive relationship between accounts receivable and profitability and posited that generous trade credit is required by firms in emerging markets like India to survive and sustain against competitors. On similar lines, Mathuva (2010) documented a positive relationship between inventory levels and profitability for Kenyan firms. Analogous results were documented by Lyroudi and Lazaridis (2000) for Greece, Gill et al. (2010) for US, Abuzayed (2012) for Jordan and Wuryani (2015) for Indonesia. Baños-Caballero et al. (2014) in their study of 258 UK firms found an inverted-U-shaped relationship between investment in working capital and profitability indicating that an optimal level of investment in working capital exists which can balance the costs and benefits, thereby maximizing a firm’s value.

This article aims to analyze the relationship of working capital measures with firm performance for an emerging market and seeks to test whether it confirms the negative relationship as evidenced by most of the studies or otherwise. In the Indian market setting, Ramachandran and Janakiraman (2009) and Sharma and Kumar (2011) found contrasting evidences; thus, it becomes imperative to further analyze the relationship between working capital management and firm performance in India.

Data and Methodology

Sample

The sample for the study includes non-financial firms listed on the Bombay Stock Exchange (BSE) of India for the period from 2002 to 2014. From the list of constituent companies of S&P BSE 500¹ index, all financial firms including banks and financial services firms have been excluded. In addition, companies with missing data and the ones with different financial year have been excluded. Moreover, firms with illogical data, such as negative assets, liabilities and capital, have also been removed. The final sample included 2,327 firm-year observations comprising 179 firms for 13 years from 2002 to 2014. The data for the variables required in the study were extracted from Ace Equity Database.² The sample so obtained represents 18 different industries,³ with the share of information technology being the highest and that of media and publishing being the lowest

Variables

To assess the relationship between working capital management and profitability of Indian firms, the empirical framework of Shin and Soenen (1998) and Deloof (2003) has been followed. All constituents of working capital have been taken individually and collectively, in line, two measures of profitability have been taken representing the accounting profits and the market measure of profits. Furthermore, to get robust results various control variables have been used. Table 2 summarizes the variables used in the study.

Table 2.

Variables Definition

Variable	Label	Name	Definitions	Hypothesized Sign
Dependent Variable
Firm performance	GOP	Gross operating profit	EBITDA/operating assets
	TQ	Tobin’s Q	(MV of equity + MV of debt)/(BV equity + BV of debt)⁴
Independent Variables
Working capital	CCC	Cash conversion cycle	Receivable days + inventory days – payable days	–
	ARD	Account receivable days	Accounts receivable/(sales/365)	–
	ID	Inventory days	Inventory/(cost of goods sold/365)	–
	APD	Accounts payable days	Accounts payable/(cost of goods sold/365)	–
Control variables	SIZE	Firm size	Natural log of sales	+
	GROWTH	Sales growth	(Current year sales/previous year sales) – 1	+
	LEV	Leverage	Debt/total assets	–
	FFA	Fixed financial assets	Fixed financial assets/total assets	+/–
	VNOI	Variability in net operating income	Standard deviation of operating income/total operating assets	–
	GDP	Growth in gross domestic product	(Current year GDP/previous year GDP) – 1	+

Source: Compiled by the authors.

Dependent Variables for Measuring Firm Performance

In order to carefully gauge the impact of working capital on firm performance, two measures of performance have been considered representing the accounting and market-based measure of performance. Accounting profitability is measured by GOP which is determined by dividing gross operating income, that is, earnings before interest, taxes, depreciation and amortization (EBITDA), by operating assets. While gross operating income measures the firm’s operating profit and does not consider the financial assets which may be a significant part of total assets, it becomes imperative to check the market-based performance as well. Tobin’s Q is applied to measure the market-based performance. It compares the market value of the firm with its book value.

Independent Variables

Numerous independent variables have been included; the main independent variables are the working capital measures and to ward off the firm differences, additional control variables have been added based on the inputs from Deloof (2003).

Working Capital Measures: Working capital is the main independent variable and its constituents have been comprehensively represented through CCC and individual components of working capital, namely, accounts receivable days (ARD), inventory days (ID) and accounts payable days (APD), have been separately examined. The calculation formulas used are represented in Table 2.

Control Variables: In order to control for the firm-specific attributes, size, growth, leverage, fixed financial assets to total assets, variability of net operating income and growth in GDP have been considered. Based on the literature, the hypothesized signs of the control variables are shown in Table 2.

Methodology

In order to examine the effect of working capital management on the firm performance, correlation and three forms of regression have been employed. Correlation analysis is used to understand the relationship between the sampled variables. Correlation analysis has a big limitation that it does not examine the causal relationship. More care is required while using Pearson correlation coefficient because it may not give a clear indication of any directional association between the variables. To illustrate, if through application of correlation it is found that GOP is negatively related with CCC, then it is not possible to earmark whether the shorter CCC leads to high profitability or vice versa. Therefore, in order to understand the impact of working capital management on firm performance, ordinary least square method (OLS), panel data analysis with fixed- and random-effect model and dynamic panel model/generalized method of moments (GMM) to explain the lag effect have been employed.

In the regression analysis, GOP and Tobin’s Q are the two endogenous variables depicting firm profitability with eight exogenous variables at a time. Total eight models are created with each variable, of which four models take GOP as dependent variable and remaining four models take Tobin’s Q as a dependent variable. The study models firm profitability as a function of four working capital measures, that is, CCC, receivable days, ID and payable days, which are taken one at a time. Furthermore, firm size, sales growth, debt ratio, financial asset as a percentage of total assets, variability of earnings and GDP growth rate are the other independent variables included to control for firm-specific differences.

Regression: Gross Operating Profit as a Dependent Variable

The first set of regression models examines the effect on accounting profitability for firm i at time t individually for a set of working capital measures and the control variables. The following hypothesis is framed to be tested using the set of four models:

H1: Efficient working capital management improves Indian firms operating profitability.

The working capital measures in the four models are different. The first model employs CCC, models (2), (3) and (4) use ARD, ID and APD, respectively.

\begin{matrix} G O P_{i t} = β_{1 i} + β_{2} C C C + β_{3} S i z e + β_{4} S a l e s G r o w t h + β_{5} L e v e r a g e + β_{6} F F A \\ + β_{7} V N O I + β_{8} G D P + e_{i t} \end{matrix}

(1)

\begin{matrix} G O P_{i t} = β_{1 i} + β_{2} A R D + β_{3} S i z e + β_{4} S a l e s G r o w t h + β_{5} L e v e r a g e + β_{6} F F A \\ + β_{7} V N O I + β_{8} G D P + e_{i t} \end{matrix}

(2)

\begin{matrix} G O P_{i t} = β_{1 i} + β_{2} I D + β_{3} S i z e + β_{4} S a l e s G r o w t h + β_{5} L e v e r a g e + β_{6} F F A \\ + β_{7} V N O I + β_{8} G D P + e_{i t} \end{matrix}

(3)

\begin{matrix} G O P_{i t} = β_{1 i} + β_{2} A P D + β_{3} S i z e + β_{4} S a l e s G r o w t h + β_{5} L e v e r a g e + β_{6} F F A \\ + β_{7} V N O I + β_{8} G D P + e_{i t} \end{matrix}

(4)

Regression: Tobin’s Q as a Dependent Variable

The second set of models includes similar independent variables as in the first set; however, the dependent variable is a market-based metric of performance measured through Tobin’s Q. The following hypothesis is framed to be tested using the set of four models:

H2: Efficient working capital management improves Indian firms’ market value.

The following set of four models is used to examine the above-mentioned hypothesis:

\begin{matrix} T o b i n s Q_{i t} = β_{1 i} + β_{2} C C C + β_{3} S i z e + β_{4} S a l e s G r o w t h + β_{5} L e v e r a g e + β_{6} F F A \\ + β_{7} V N O I + β_{8} G D P + e_{i t} \end{matrix}

(5)

\begin{matrix} T o b i n s Q_{i t} = β_{1 i} + β_{2} A R D + β_{3} S i z e + β_{4} S a l e s G r o w t h + β_{5} L e v e r a g e + β_{6} F F A \\ + β_{7} V N O I + β_{8} G D P + e_{i t} \end{matrix}

(6)

\begin{matrix} T o b i n s Q_{i t} = β_{1 i} + β 2 I D + β_{3} S i z e + β_{4} S a l e s G r o w t h + β_{5} L e v e r a g e + β_{6} F F A \\ + β_{7} V N O I + β_{8} G D P + e_{i t} \end{matrix}

(7)

\begin{matrix} T o b i n s Q_{i t i t} = β_{1 i} + β_{2} A P D + β_{3} S i z e + β_{4} S a l e s G r o w t h + β_{5} L e v e r a g e + β_{6} F F A \\ + β_{7} V N O I + β_{8} G D P + e_{i t} \end{matrix}

(8)

The time series and cross-sectional data are not controlled for heterogeneity and the results may be biased if it is not controlled. To control that, the study used the same data and analyzed using panel data models with fixed- and random-effects models. Furthermore, to ensure more robustness and to include the lag impact GMM has also been employed. The detailed results on the basis of indicated models follow in next section.

Analysis and Discussion

Descriptive statistics are documented in Table 3. The value of operating profit margin varies from –57.79 per cent to 102.73 per cent with an average value of 13.53 per cent. It seems to be very volatile and beyond the limits reported in other studies (Deloof, 2003). The average CCC of sampled companies is 48.94 days. An Indian firm on an average receives payment after 54.44 days after credit sales and is able to clear inventory in 55 days. Firms pay on an average in 60.8 days to pay-off their credit purchases. Mean sales growth rate of sampled Indian firms is 23.75 per cent with a spread of –74.60 per cent to 23.06 per cent showing high variation in growth rate of Indian firms. It is also worth noting that average financial assets are 9 per cent of total assets of the firms. Variability of earnings of sampled firms ranges from 1.3 per cent to 136.3 per cent with an average value of 31.41 per cent showing high variability in earnings in sampled Indian firms.

Table 3.

Descriptive Statistics

	Mean	Maximum	Minimum	Std Dev.	Observations
GOP	0.1353	1.0273	–0.5779	0.1062	1,391
TQ	2.4057	25.4140	0.0000	2.5203	1,391
CCC	48.9436	1,144.4310	–870.9411	98.3434	1,391
ARD	54.4354	429.8715	1.2164	38.3223	1,391
ID	55.3099	881.6692	0.0000	65.6000	1,391
APD	60.8017	1,097.1770	–864.8376	64.9935	1,391
SIZE	7.7380	13.0997	1.2903	1.7479	1,391
GROWTH	0.2375	23.0647	–0.7460	0.6847	1,391
LEV	0.2800	0.8931	0.0000	0.1765	1,391
FFA	0.0901	0.8632	0.0000	0.1276	1,391
VNOI	0.3141	13.6300	0.0130	0.7769	1,391
GDP	0.07081	0.1061	0.04372	0.0935	1,391

Source: Authors’ calculations.

Correlation

Table 4 depicts the correlation coefficients of sampled variables of Indian firms. The dependent variable, GOP, has a significant and negative relationship with most of the variables. The results clearly indicate a significant statistical relationship between profitability of firms and working capital management. Cash conversion cycle, ARD, ID and payable days are negatively related with GOP. It indicates that Indian firms with high profitability are less bothered about their working capital management. The results are similar for Tobin’s Q. Debt ratio is negatively related with both the measures of profitability of the sampled Indian firms, depicting that the profitable firms are not taking the advantage of financial debt.

Table 4.

Correlation Analysis

	GOP	TQ	CCC	ARD	ID	APD	SIZE	GROWTH	LEV	FFA	VNOI	GDP
GOP	1.00
TQ	0.32*	1.00
CCC	–0.05**	–0.11*	1.00
ARD	–0.09*	–0.09*	0.41*	1.00
ID	–0.10*	–0.10*	0.70*	0.19*	1.0000
APD	0.07*	0.0082	0.56*	0.16*	0.06**	1.0000
SIZE	–0.01	0.06**	–0.16*	–0.32*	–0.12*	–0.08*	1.0000
GROWTH	0.02	0.03	–0.04	–0.07*	–0.04	–0.02	0.05***	1.0000
LEV	–0.47*	–0.34*	0.07*	0.01	0.11*	0.02	0.12*	0.04	1.0000
FFA	0.22*	0.03	–0.11*	–0.20	–0.06*	–0.01	–0.01	0.01	–0.19*	1.0000
VNOI	0.16*	–0.03	–0.03	0.03	–0.07*	–0.01	–0.30*	0.02	–0.12*	0.02	1.0000
GDP	–0.04	0.10*	0.04	–0.05***	0.07*	–0.01	0.43*	0.02	–0.01	0.05***	–0.33*	1.0000

Source: Authors’ calculations.

Note: *** Significant at 1 per cent, ** Significant at 5 per cent, * significant at 10 per cent.

Ordinary Least Square Method

Ordinary Least Square Method: Gross Operating Profit as a Dependent Variable

The results in model (1) suggest that size of the firm, growth rate, financial assets, variability of earnings and GDP growth rate are positively and statistically significantly related with profitability of the firms. However, debt ratio is negatively and significantly related with profitability of the firm. Cash conversion cycle is negatively and significantly related with profitability of samples firms. This shows firms can improve their profitability by lowering their CCC. The results are presented in Table 5(A).

The results in models (2) and (3) are similar to the results of model (1). The coefficients of ARD and ID are negatively and significantly related with profitability of sample firms implying that the firms can improve their profitability by lowering collection period from debtors and by lowering the inventories holding period. In model (4), the coefficient of APD is positively and significantly related with profitability of sample firms. This shows firms can improve their profitability by increasing the APD and by extending the credit periods from their vendors and suppliers.

Ordinary Least Square Method: Tobin’s Q as a Dependent Variable

The same exercise is repeated by taking Tobin’s Q as a dependent variable keeping independent variables unchanged. The results are presented in Table 5(B). The results in model (1) suggest that CCC is negatively and significantly related with Tobin’s Q of sample firms implying that the market performance can be improved by reducing the CCC. Likewise, in models (2) and (3) similar results are found. The coefficients of ARD and ID are negatively and significantly related with Tobin’s Q of sampled firms. The firms can improve their market performance by lowering collection period from debtors and by lowering the inventories holding period (or improving inventory turnover). In model (4), the coefficient of APD is positively and significantly related with Tobin’s Q of sampled firms. Therefore, the firms can improve their market performance by increasing the APD and by extending the credit periods from their vendors and suppliers.

The results in both Table 5(A) and 5(B) clearly indicate that irrespective of taking accounting measure of firm performance or market measure of firm performance, the results are similar and clearly indicate that firm performance improves if a firm improves its working capital management.

Table 5(A).

OLS: GOP as a Dependent Variable

OLS
Regression Model	1		2		3		4
Regression Model	Coefficient	p–value	Coefficient	p–value	Coefficient	p–value	Coefficient	p–value
Constant	0.1521	0.0000	0.1640	0.0000	0.1564	0.0000	0.1623	0.0000
CCC	–0.0003	0.0378
ARD			–0.0001	0.0266
ID					–0.0003	0.0740
APD							0.0001	0.0209
SIZE	0.0076	0.0000	0.0066	0.0001	0.0071	0.0000	0.0070	0.0000
Growth	0.0051	0.1589	0.0047	0.1887	0.0049	0.1730	0.0048	0.1810
LEV (DRR)	–0.2667	0.0000	–0.2657	0.0000	–0.2650	0.0000	–0.2651	0.0000
FFA	0.1191	0.0000	0.1124	0.0000	0.1168	0.0000	0.1168	0.0000
VNOI	0.0165	0.0000	0.0162	0.0000	0.0163	0.0000	0.0162	0.0000
GDP	0.0000	0.0005	0.0000	0.0013	0.0000	0.0010	0.0000	0.0008
Adjusted R²	0.2585		0.2587		0.2582		0.2609

Source: Authors’ calculations.

Table 5(B).

OLS: Tobin’s Q as a Dependent Variable

OLS
Regression Model	1		2		3		4
Regression Model	Coefficient	p–value	Coefficient	p–value	Coefficient	p–value	Coefficient	p–value
Constant	3.1957	0.0000	3.5779	0.0000	3.1347	0.0000	2.8737	0.0000
CCC	–0.0020	0.0021
ARD			–0.0055	0.0024
ID					–0.0021	0.0353
APD							0.0008	0.4137
SIZE	0.0879	0.0353	0.0699	0.1078	0.0979	0.0190	0.1179	0.0041
Growth	0.1488	0.1075	0.1423	0.1241	0.1509	0.1032	0.1598	0.0848
LEV	–5.0540	0.0000	–5.1285	0.0000	–5.0378	0.0000	–5.1411	0.0000
FFA	–0.9224	0.0688	–1.0938	0.0342	–0.8047	0.1114	–0.7521	0.1369
VNOI	–0.1440	0.0997	–0.1436	0.1007	–0.1442	0.1004	–0.1288	0.1418
GDP	0.0000	0.3275	0.0000	0.3574	0.0000	0.3892	0.0000	0.5635
Adjusted R ²	0.1300		0.1298		0.1268		0.1244

Source: Authors’ calculations.

Panel Data Analysis: Fixed or Random Effect

The data are further analyzed for the relationship between working capital and firm performance using panel data analysis. The test results of Hausman test support the fixed-effect model, as the hypothesis of random-effect model was rejected.

Fixed-effect Model: Gross Operating Profit as a Dependent Variable

The results in model (1) suggest that size of the firm, growth rate, financial assets, variability of earnings and GDP growth rate are positively and statistically significantly related with profitability of the firms. Debt ratio is negatively and significantly related with profitability of the firm. Cash conversion cycle is negatively and significantly related with profitability of sample firms. This shows firms can improve their profitability by lowering their CCC. The results are presented in Table 6(A).

The results in models (2) and (3) are similar to the results of model (1). The coefficients of ARD and ID are negatively and significantly related with profitability of sample firms. The firms can thus improve their profitability by lowering collection period from debtors and by lowering the inventories holding period. In model (4), the coefficient of APD is positively and significantly related with profitability of sample firms, implying that profitability can be increased by getting extended credit periods from the vendors and suppliers.

Fixed-effect Model: Tobin’s Q as a Dependent Variable

The same exercise is repeated and GOP was replaced by Tobin’s Q as a dependent variable keeping independent variables unchanged. The results are presented in Table 6(B). The results in model (1) suggest that CCC is negatively and significantly related with Tobin’s Q of sample firms. The results in models (2), (3) and (4) are similar to results obtained when the dependent variable was gross operating performance, with the coefficients of ARD and ID having a significantly negative effect and coefficient of APD having a significantly positive effect on Tobin’s Q.

The results in both Tables 6(A) and 6(B) clearly indicate that similar to OLS outputs panel data through fixed-effect model, firm performance improves if a firm improves its working capital management. But the explanatory power of fixed model is better than OLS models and the same is reflected in adjusted R-square values. In case of OLS, the adjusted R-square values were around 0.26 when GOP was a dependent variable and around 0.14 when Tobin’s Q was dependent variable. But in fixed-effect model, the value of adjusted R-square was around 0.57 and 0.45, respectively.

Table 6(A).

Fixed Effect/Random Effect: GOP as a Dependent Variable

Fixed or Random Effect
Regression Model	1		2		3		4
Regression Model	Coefficient	p–value	Coefficient	p–value	Coefficient	p–value	Coefficient	p–value
Constant	0.0502	0.1550	0.0920	0.0125	0.0647	0.0656	0.0460	0.1883
CCC	–0.0001	0.1137
ARD			–0.0005	0.0000
ID					–0.0003	0.0000
APD							0.0001	0.0534
SIZE	0.0290	0.0000	0.0261	0.0000	0.0284	0.0000	0.0301	0.0000
Growth	–0.0088	0.0030	–0.0104	0.0005	–0.0094	0.0014	–0.0090	0.0024
LEV	–0.3197	0.0000	–0.3126	0.0000	–0.3159	0.0000	–0.3231	0.0000
FFA	0.1758	0.0000	0.1685	0.0000	0.1797	0.0000	0.1825	0.0000
VNOI	–0.0052	0.1891	–0.0065	0.0995	–0.0053	0.1792	–0.0054	0.1760
GDP	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
Adjusted R ²	0.5723		0.5771		0.5774		0.5727
Hausman	63.2883	0.0000	65.9695	0.0000	70.7076	0.0000	61.8642	0.0000
Estimation	FE		FE		FE		FE

Source: Authors’ calculations.

Table 6(B).

Fixed Effect/Random Effect: Tobin’s Q as a Dependent Variable

Fixed or Random Effect
Regression Model	1		2		3		4
Regression Model	Coefficient	p–value	Coefficient	p–value	Coefficient	p–value	Coefficient	p–value
Constant	1.7236	0.1108	0.1291	0.8953	1.7547	0.1315	–1.5415	0.1007
CCC	–0.0013	0.3883
ARD			–0.0160	0.0000
ID					–0.0024	0.1691
APD							–0.0017	0.0979
SIZE	0.2643	0.0000	0.6069	0.0000	0.2677	0.0000	0.7471	0.0000
Growth	0.0825	0.6619	–0.0233	0.7682	0.0793	0.7060	0.0294	0.7104
LEV	–3.9027	0.0000	–3.1146	0.0000	–3.8885	0.0000	–3.4782	0.0000
FFA	0.2901	0.2554	0.4772	0.5009	0.3650	0.2218	0.9354	0.1901
VNOI	–0.2526	0.3107	–0.1549	0.1421	–0.2563	0.3081	–0.1117	0.2931
GDP	0.0000	0.0002	0.0000	0.0011	0.0000	0.0002	0.0000	0.0001
Adjusted R ²	0.4519		0.4640		0.4523		0.4527
LM
Hausman	16.5950	0.0109	27.2630	0.0001	16.6888	0.0105	18.0675	0.0061
Estimation	FE		FE		FE		FE

Source: Authors’ calculations.

Generalized Method of Moment System Estimation

In addition to the OLS and panel data models, GMM system estimation is also applied to analyze the dynamic panel data. This method allows for dynamic specification and is applied as the explanatory variables (CCC, ARD, ID and APD) are likely to have an unobserved lagged effect on the dependent variable. Each of these models is estimated using the GMM proposed by Arellano and Bond (1991). Sargan test applied on these models confirms that the diagnostics were satisfactory and accepted. The results of dynamic panel estimations/GMM are presented in Tables 7(A) and 7(B) taking GOP and Tobin’s Q as the dependent variables, respectively. The results in both the cases show that coefficients of lagged value of dependent and independent variables are statistically significant. In Table 7(A), when GOP is the dependent variable, the output shows that lagged value of performance affects the present performance and this relationship holds good in all the four models. The results show that past performance is positively related with present performance, implying that if a firm performed well in the past, then it is likely to perform better in future as well. Similarly, past working capital performance is also significantly negatively related with present performance. If a firm has shortened the CCC or managed other measure(s) of working capital efficiently in the past, then it is likely to perform well in future also.

Table 7(A).

GMM Estimation: GOP as a Dependent Variable

GMM
Regression Model	1		2		3		4
Regression Model	Coefficient	p–value	Coefficient	p–value	Coefficient	p–value	Coefficient	p–value
GOP(–1)	0.2680	0.0000	0.2442	0.0000	0.2566	0.0000	0.2619	0.0000
CCC	–0.0005	0.0000
CCC(–1)	–0.0001	0.0500
ARD			–0.0020	0.0000
ARD(–1)			–0.0003	0.3022
ID					–0.0018	0.0000
ID(–1)					–0.00166	0.0856
APD							–0.0003	0.0890
APD(–1)							–0.0001	0.0436
SIZE	0.0984	0.0000	0.0984	0.0000	0.1115	0.0000	0.1382	0.0000
Growth	–0.0015	0.3287	–0.0051	0.0027	–0.0051	0.0002	–0.0051	0.0310
LEV	–0.2459	0.0000	–0.3287	0.0000	–0.2232	0.0000	–0.2759	0.0000
FFA	0.2188	0.0000	0.1706	0.0000	0.2511	0.0000	0.2582	0.0000
VNOI	0.0654	0.0000	0.0536	0.0000	0.0713	0.0000	0.0661	0.0000
GDP	0.2821	0.0000	–0.3117	0.0000	–0.2951	0.0000	–0.4121	0.0000
Sargan (X2)	67.7001		72.3489		71.6781		64.7150

Source: Authors’ calculations.

Table 7(B).

GMM Estimation: Tobin’s Q as a Dependent Variable

GMM
Regression Model	1		2		3		4
Regression Model	Coefficient	p–value	Coefficient	p–value	Coefficient	p–value	Coefficient	p–value
GOP(–1)	0.3249	0.0000	0.3078	0.0000	0.3276	0.0000	0.3296	0.0000
CCC	–0.0082	0.0000
CCC(–1)	0.0169	0.0000
ARD			–0.0330	0.0000
ARD(–1)			–0.0269	0.0000
ID					0.0033	0.1630
ID(–1)					–0.0104	0.1070
APD							0.0036	0.0128
APD(–1)							–0.0024	0.2123
SIZE	0.1158	0.4608	0.2361	0.1403	0.5882	0.0001	0.3610	0.0259
Growth	0.2469	0.0024	0.0895	0.4568	0.2997	0.0006	0.3022	0.0009
LEV	–11.2482	0.0000	–11.8934	0.0000	–12.9983	0.0000	–12.4077	0.0000
FFA	2.8399	0.0000	2.7736	0.0000	4.1000	0.0000	3.6919	0.0000
VNOI	–2.1758	0.0000	–2.1011	0.0000	–2.5298	0.0000	–2.4769	0.0000
GDP	–2.3696	0.0000	–2.8950	0.0000	–4.4750	0.0000	–3.5951	0.0000
Sargan (X2)	81.0859		80.7747		81.0265		80.4681

Source: Authors’ calculations.

Conclusion and Managerial Implications

Working capital management improves the overall efficiency of any firm and thus influences the profitability and competitiveness of any firm as well. In this study, four measures are used as a proxy for working capital efficiency, namely, CCC, ARD, ID and APD. The findings of the study clearly indicate that CCC has a significant impact on financial performance (profitability and market performance) of Indian firms. The relationship between CCC and financial performance is negative. This shows that by shortening the CCC, a firm can improve its financial performance. A possible explanation of this negative relation between CCC and firm financial performance is that shorter CCC implies shorter average collection period, improved inventory turnover and extended credit period from suppliers which results in lesser amounts of funds being blocked in working capital pipelines and thereby reduces the requirement of working capital finance. The less use of working capital finance results in less cash outflow in terms of financing cost. Due to this, working capital maintenance cost is also reduced resulting in better margins; the improved margins and profitability in the process enhance the firm value. So firms’ managers can enhance the value of their firms by shortening CCCe, reducing ARD, reducing ID and extending payable days.

The control variable firm size is also significantly related with firm performance and the findings suggest that larger firms with efficient working capital management are likely to have better financial performance. The findings of this study are consistent with the findings of Deloof (2003), Lazaridis and Tryfonidis (2006), García-Teruel and Martinez-Solano (2007), Mathuva (2010) and Ebben and Johnson (2011). The findings of this study are very useful for finance managers, entrepreneurs, financial institutions, business consultants and academic researchers. They can improve the financial performance of firms by improving their working capital efficiency highlighted in this article.

Footnotes

Notes

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