Abstract. Keywords: Working capital; cash (conversion) cycle; target following; adjustment costs. JEL Classification: G30, G31, G32

Transcription

1 Working Capital Management and Target Following: Evidence from India Gaurav S Chauhan (gauravs@iimidr.ac.in) and Pradip Banerjee (pbanerjee@iimidr.ac.in) Indian Institute of Management Indore (India) Abstract The paper examines the variation in the intensity of target following behavior of Indian manufacturing firms while they manage their working capital needs. We show that the strength of mean reversion varies widely across firms depending on whether their deviation from the working capital target is positive or negative and whether they actually conform to target following behavior or not in a given period. In contrast to the previous studies, we find no evidence of systematic target behavior for the firms in our dataset. While a large fraction of firms exhibit intense non-target behavior in the period immediately following the deviation from their targets, we also find absence of systematic target following in the subsequent periods, even by firms following target intensely in the initial period. The results are robust to several different measures of target cash cycles, varying magnitude of deviation from target cash cycles, and varying financial constraints. We also find that a large part of the exhibited target behavior (or its absence) could not be explained by commonly used set of firm-specific and macroeconomic factors. Keywords: Working capital; cash (conversion) cycle; target following; adjustment costs. JEL Classification: G30, G31, G32 0

2 Working Capital Management and Target Following: Evidence from India 1. Introduction Working capital in a firm acts as a bridge between its long-term assets and liabilities and is an important source of the value for the firm. Collectively, the trade receivables, inventories, and trade credit constitute the net operating working capital of a firm 1. Adequate working capital equips the firms to deal with contingencies such as input price fluctuations, stock out costs of inventories, building relationships with customers by providing a higher trade credits which can act as guarantee for product quality and, encourages sales to overcome cyclicality of demand (see for e.g. Brennan et al., 1988;Deloof and Jegers, 1996; Petersen and Rajan, 1997; Ng et al., 1999;Wilner, 2000; Summers and Wilson, 2002; Corsten and Gruen, 2004). On the flip side, however, every incremental addition in working capital requires additional operating and financing costs thereby reducing the profitability or increasing the financial distress for the firms (Kim and Chung, 1990; Deloof, 2003; Hill et al., 2010). If excess working capital lock-in vital cash and capital, it could impede firms to take value-enhancing projects for their growth (Ek and Guerin, 2011).Accordingly, working capital may acts as a source of internal funds and substitute for cash (Eckbo and Kisser, 2013 and Bates et al., 2009) when used aggressively by tightening the credit sales and the level of inventory (Chiou et al., 2006). Considering such a trade-off, past empirical literature has suggested specific relationship between firm value or performance and their working capital levels. Specifically, Aktas et al. (2015) and Banos-Caballero et al. (2014 and 2012) suggest an optimal or target level of net working capital for a typical firm. These studies show a positive (negative) relationship between working capital and firm value or performance for firms underinvesting (overinvesting) in working capital. However, the literature has scarcely studied the target following behavior of firms to actively chase such an optimal level. In a rare attempt to do so, Baños-Caballero et al. (2010) studied the mean reversion of cash cycles 2 of small- and medium-sized Spanish firms. Baños-Caballero et al. (2013) further extended this work to study the reversion in a broader sample of Spanish non-financial firms facing varying 1 Net operating working capital in this paper is defined as trade receivables plus inventories minus trade payables 2 The cash cycle or cash conversion cycle is a measure of working capital deployed by the firms in the past literature. It denotes the number of days elapsed between the payments made to the suppliers of raw material and intermediate goods to the receipt of payment from the customers. 1

3 financial constraints and market power. In these studies the reversion is studied through the coefficient of lagged cash cycle, reflecting the speed of adjustment (SOA) towards their targets, in a partial-adjustment dynamic panel model. These studies find that, at an aggregate level, firms actively and rapidly revert to their targets in the following year of experiencing a deviation from these targets. Although, partial-adjustment models provide an elegant way to study mean reversion, the generalizability of target following is rather limited by using them for several reasons. First, while significant deviation from the target behavior assumed by partial-adjustment models is quite common, the SOAs identified in these models may only indicate the frequency of full rebalancing in the sample rather than economically meaningful target adjustments (Hovakimian and Li, 2012). Second, the estimation through dynamic panel models is sensitive to the panel length. The estimation is relatively accurate for larger panels with more number of observations per firm (Flannery and Hankins, 2013). However, what can be considered as a large enough panel length is debatable (Judson and Owen, 1999). Finally, following the past literature, the motivation to mean revert is different for firms that under- or over-invest in working capital. Similarly, since firms face varying degree of adjustment costs, the strength of mean-reversion could be different for firms following target from those that do not follow their targets in a given period. Accordingly, an aggregate measure of SOA prevent systematic inferences of target following behavior for these sub-sets of firms. The estimation through dynamic panel models is particularly worrisome for emerging market dataset as they consist of numerous entry and exit of large number of diverse firms entailing inherent short-panel bias in these datasets. Further, emerging market firms operate with varying financial and non-financial constraints. 3 Due to such constraints, on one hand, while it becomes imperative for these firms to judiciously manage their working capital, on the other hand, unpredictable nature of these emerging markets may prevent them to closely track their working capital targets. Accordingly, there could be large dispersion in the target following behavior of emerging market firms, warranting a detailed granular analysis. In this paper we assess the target following intensities for the firms in an emerging market setup using a large sample of Indian manufacturing firms. We propose a novel approach to 3 Among the emerging markets, Allen et al. (2012) show that Indian firms rely heavily on internal finance, which contributes 45 percent of their total annual financing. Alternative sources other than banks and capital markets meet 30 percent of the financing needs of these firms. A very small percentage of financing percent and 6.5 percent, respectively is provided by banks and financial markets 2

4 overcome the previous methodological challenges in studying the target following behavior. Specifically, to infer the target behavior, we observe the deviation in the cash cycle of a firm with respect to its target at time t=0 and compare it with the deviation in subsequent periods. If firms do revert to their respective targets, the magnitude of deviation should reduce in subsequent periods. The intensity of reversion can be seen from the extent of reduction in the deviation.while we expect significant number of firms to mean-revert, we also expect intense target behavior for firms that actually revert and only moderately deviating non-target behavior for those who could not revert, probably due to some adjustment cost concerns. Additionally, we also expect the effect of any such adjustment cost to subside overtime. Thus, apart from studying such reversion in the period immediately following the deviation, we also study it in several periods after that. Consistent with our objective to conduct a granular analysis of target behavior, we study the target following behavior separately for firms that exhibit positive and negative deviations and within these for firms that exhibit target and non-target behavior. 4 The findings in this paper suggest no perceptible aggregate target behavior. Approximately half of the firms do not exhibit target behavior at all at any point in time irrespective of their initial signs of deviation. However, target behavior varies greatly among the firms with positive and negative deviations with respect to their targets. Moreover, these results remain unaltered even when we observe the reversion for five consecutive years. While target following behavior does not persist for firms initially exhibiting target behavior, the target following intensity also does not improve with time for firms initially exhibiting non-target behavior. Thus, it would be incorrect to ascribe the absence of target following to some economic adjustment costs. Our findings of no systematic target following by the firms are robust to several different measures of target cash cycles, varying financial constraints including varying size of the firms and, varying magnitude of deviation from target cash cycles. We also validate our statistical estimation using simulated datasets for intentional and random target following 4 Target following firms are identified as those whose cash cycles actually converge towards their target in the following periods and vice-versa. 3

5 behavior. Moreover, the results are robust even when we consider that the firms may follow a range of cash cycles rather than specific targets. In the latter part of the paper we also investigate if there are characteristic differences between firms following target behavior and those that do not. Accordingly, we test if the target behavior is influenced by firm-specific and macroeconomic factors and to what extent. We find that most of the variables influence target behavior in different directions for the two subsets of firms facing negative and positive deviations. This justifies our contention that firms facing negative or positive deviations are characteristically different and therefore the reversion for them should be studied discretely. Further, it seems that firm-specific and macroeconomic variables, used in past studies as determinants of both cash cycles and target behavior, may not be the first order determinants of target behavior, at least for our sample of firms. While most of the variables are found to be statistically significant in influencing target behavior, their effect is not significant in economic terms. These findings further corroborate the absence of any systematic target following behavior among the firms. Thus, while Indian manufacturing firms do not mean revert like their counterparts in developed countries, the absence of such mean reversion could not be systematically attributed to firm-specific and macroeconomic factors. There could be several unknown managerial constraints driving the real motives of these firms to manage their working capital. Apart from analyzing the target following intensities at a granular level, our paper is the first, to the best of our knowledge, to investigate the target following behavior in a large dataset of manufacturing firms in an emerging economy. The rest of the paper is organized as follows. In section 2, we observe the movement in cash cycles of firms to primarily infer mean reversion, if any. Section 3 estimate the speed of reversion following previously used dynamic panel models and discusses the challenges in conducting a granular analysis of target behavior through them. Section 3 also discusses the proposed modification in our 4

6 evaluation strategy and study the variability in the target following intensity for single and multiple periods. Section 4 explores the determinants of target behavior in a multivariate setting. Section 5 presents our concluding remarks. We also undertake several tests for robustness to validate our key results. We present all of them in a separate Supplementary Appendix, submitted along with this manuscript for review which can be read as a standalone document. 2. Mean Reversion in Cash Cycles: Primary Analysis Following the past literature we use cash conversion cycles or cash cycles (CCC) to analyze the target behavior of the firms (see for e.g., Soenen, 1993; Shin and Soenen, 1998; Deloof, 2003; Padachi, 2006). In this section we primarily analyze the aggregate movements in cash cycles of firms with varying degrees of deviation from their targets over a period of time to get a first-hand sense of the underlying mean reversion in cash cycles in our data set. Following Baños Caballero et al. (2010), we specify the target CCC of a firm as a function of firm-specific attributes. A firm s target CCC for a given period t is estimated using the predicted values from the following fixed-effect model: 5 CCC i, t 1 j. X i, t i t i, t (1) where, CCCi,t+1 is the actual cash conversion cycle for firm i at time t+1, measured as follows: CCC ceivable Days Inventory Days PayableDays (2) i, t 1 Re i, t 1 i, t 1 i, t 1 where, these terms are defined as follows: 5 Although we demonstrate mean reversion in this section using the functional form of equation (1) to estimate target cash cycle, the findings remain qualitatively similar using alternative functional measures of cash cycles used later in this paper. 5

7 Xi,t represents lagged values of several firm-specific determinants of cash cycles used in the past literature, for firm i at time t. These variables are size, leverage, cash flows, profitability, asset tangibility, sales growth, age, financial distress and median industry cash cycle. These variables and their relation to working capital management are discussed in Supplementary Appendix (SA-VAR). Lagged values of the firm-specific variables are chosen for estimation of target cash cycles through equation (1) so as to accommodate endogenous determination. In equation (1), ηi represents time-invariant, firm-specific attributes; γt represents the yearfixed effects and εi,t is the usual stochastic error term. From here on, we drop the subscript i from all the variables of interest for the ease of exposition. Our data is derived from annual financial statements of all Indian manufacturing firms (including quoted and unquoted firms), available in the Prowess database of the Center for Monitoring of Indian Economy (CMIE) post economic liberalization from 1993 to We exclude firm-year observations with leverage (total debt to total assets) less than zero or greater than one, negative net fixed assets or missing values for any variable of interest. We also require a firm to have at least three continuous years of data on all the variables of interest to qualify for inclusion. Further, to remove the effect of outliers, we also exclude firm-year observations with subsequent period change in cash cycles exceeding 10 times in magnitude of the cash cycle in the immediately previous period and winsorize all firmspecific variables at the 1st and the 99th percentiles. After applying these filters, the final data set consists of an average of 2,714 firms per year and an unbalanced panel of total 52,785 firm-year observations. The average panel length for the constituent firms is 4.1 years. The 6

8 descriptive statistics for the variables of interest are shown in Appendix A. We also report pairwise correlations for all the variables of interest in Appendix B. We estimate (but do not report explicitly) variance inflation factors (VIF) for all the variables of interest in all the tests conducted in this paper and find that multi-collinearity is not a potential concern. 6 We define time t=0 as a transition point where a typical firm experiences a deviation of the actual cash cycle from its estimated target. We define the deviation from target as follows: CCC CCC TCC (3) t t t where, TCCt is the target cash cycle estimated as the predicted value of the next period CCC by using (1) and, CCCt is the actual CCC at time t. 7 Thus, the deviation represent the difference between actual and anticipated cash cycles at time t. Next, we primarily observe the mean reversion in cash cycles of firms with varying signs and severity of deviations from their targets. We begin by estimating the deviations from the target for each of the following five years after t=0 for each firm in our data set. We then segregate the firms in four groups depending on the signs and severity of the deviations at t=0. These groups are classified as experiencing most negative (G1), moderately negative (G2), moderately positive (G3), and most positive (G4) deviations. By design, groups G1 and G2 (G3 and G4) consist of firm-year observations with negative (positive) deviations and are of equal size. Subsequently, we calculate the medians of the ensuing deviations (ΔCCCt) for all the five years (i.e. from t=1 to t=5) for all four groups defined at t=0. Notice that the firms are classified into four groups only once at t=0 and consequently remain in the same group while we study the changes in their deviations over next five years. However, we do not put any constraint on the change in firms target CCC over these five years; the deviations in 6 The maximum value of VIF for any variable in any of these tests is Since we use data till 2015, we can estimate targets till 2014 using equation (1), where dependent variable would be the CCC of year

9 each year is calculated using the target of that particular year only. For the four groups, our objective is to broadly see the extent of reduction in the deviations of firms CCC from their respective targets overtime reflecting the mean-reversion in CCC. The analysis over a longer period reveals the impact of any possible adjustment costs and other frictions that might possibly prevent any immediate reversion to the target cash cycles. Figure 1 shows the plot for these medians of the deviations for all the four groups and suggest that the speed of mean reversion is quite different for them. Firms with moderately positive deviations (G3) seem to revert most rapidly so as to bridge half of their deviations in just about two years. On the other hand, firms with the most negative deviations (G1) show very slow or no reversion at all. The patterns in Figure 1 reveal couple of interesting facets of mean reversion in cash cycles for the firms in our dataset. First, firms with negative and positive deviations of their cash cycles with respect to their targets exhibit different target following behavior. Second, even among firms with similar signs of deviations, the firms may exhibit varying speed of reversion. Following these observations, we delve deeper into the pattern shown in Figure 1 by segregating firms that are closer to their target in the next period and those that do not within firms facing negative and positive deviations in each year. Specifically, we observe the movement in cash cycles in subsequent years as follows. Let, CCCt and TCCt are the actual and target cash cycles for a firm at time t respectively. We define a measure to capture the extent of mean reversion (EMR) as the following ratio: CCC TCC EMR CCC TCC t 1 t 1 t t (4) The denominator in equation (4) represents the total initial deviation of the cash cycle of a firm with respect to its target, and the numerator represents the consequent deviation in the 8

10 subsequent year with respect to the revised target in that year. EMR then denotes the extent of reduction in the deviation, consequent to any target following. In case the firm comes closer to its target in the next period, we expect this ratio to be less than unity. A ratio greater than one would signify non-target behavior with respect to the cash cycles. The magnitude of EMR further tells us the amount of reversion in the next period. Using EMR as the measure capturing intensity of target following, we segregate the firms with EMR greater than and less than unity separately for each year of our data for firms with negative and positive deviations from their cash cycles. Thus, a target following group consists of firm-year observations with individual EMR less than +1 and a non-target group consists of the remaining firms. Thus, in all we have the following four categories of firms: Category 1 (C1): firms with negative deviation and following target; Category 2 (C2): firms with negative deviation and not following target; Category 3 (C3): firms with positive deviation and following target; Category 4 (C4): firms with positive deviation and not following target. Subsequently, we calculate the median of such individual EMRs for each year and for each of the four categories. Since the first set of targets are estimated for the year 1993, we could estimate individual EMRs from the year 1994 onwards. Thus, the firms with their initial cash cycles and estimated targets for 1993 (say t=0) are classified into C1 to C4 with respect to the resulting deviation from the target after one year i.e (t=1) and so on. Figure 2 shows the plot of such medians for these four groups. Although, by design the median EMRs for each of the year for the target following groups is less than unity, these medians tend to vary a lot across time and are quite different in a year for firms with negative and positive deviations. Moreover, apart from the variability overtime, the median EMRs for the non-target following groups is far greater than unity in a given year. Although, due to certain adjustment costs the 9

11 firms may not be able to converge to their targets in the period immediately following the deviation, such adjustment costs may not force firms to diverge to a large extent from their targets in the next period. Thus, we infer that with such large magnitude of EMRs, the absence of mean reversion for the firms in non-target group may not be primarily due to such adjustment costs. We investigate the matter of adjustment costs further by observing the target behavior of firms in one more subsequent period post experiencing the deviations from their targets. Thus, we re-calculate the median EMRs of the same firm-year observations classified into four groups previously (at t=0) for the very next year (at t=1). For example, for the firm-year observations classified into category C1 for the year 1996 (t=0), we re-calculate the median EMRs of these observations in 1997 (t=1). Importantly, while the EMRs for the year 1996 are calculated using deviations in 1996 (t=0) and 1997 (t=1), the EMRs for 1997 are calculated using deviations in 1997 (t=1) and 1998 (t=2), while using the same set of firm-year observations classified into C1-C4 in 1996 (t=0) only. Thus, if adjustment costs truly prevent firms from converging in the immediately next period after experiencing deviations, we can expect more conformance from non-target following group in the next period such that they may exhibit improved target behavior, while at the same time we expect target following groups to continue exhibiting target behavior in the next period. Figure 3 shows the plot of these median EMRs for the subsequent year of deviation (at t=1). As can be seen the variability in these subsequent median is far greater than their initial values at t=0 (Figure 2). The intensity of target following in the subsequent period seem to be very unpredictable irrespective of whether firms followed target or not in the immediately preceding period. While median intensity of target following is less than unity in some years, it is more than unity in other years. Further, the choice of these years seem to be random suggesting that adjustment costs associated with any systematic factor may not be driving the 10

12 cash cycle movement of the firms. The unpredictable variability of target following intensity overtime, in fact, suggests that firms in our dataset may not be systematically following their targets. These preliminary observations, accordingly, motivate the following important testable implications for the cross section of firms in our data set: Is there any aggregate target following behavior? What is the intensity of target behavior, if any, exhibited by the firms? Are the intensities different for firms with negative and positive deviations? What is the extent of variability in these intensities overtime for firms that choose to exhibit target following in the initial periods? Are there any firm-specific differences that explain differences in the target behavior of these firms? We try to answer these questions in this paper. 3. Target Following Behavior: Empirical Analysis 3.1 Challenges in Estimating Speed of Adjustment through Partial-Adjustment Models We begin our analysis by highlighting the methodological challenge in estimating the speed of reversion using partial-adjustment dynamic panel models. Accordingly, we estimate the speed of reversion for the firms in our data set using the methodology suggested by Baños Caballero et al. (2010). Specifically, the standard partial adjustment model is given by: (CCCi,t+1 CCCi,t)=λ.(TCCi,t CCCi,t) (5) Where, TCCi,t is the firm i s desired (or target) CCC defined earlier. Every year, firm closes a proportion λ of the gap between its actual and its desired CCC level. Substituting equation (1) for target CCC into equation (5), we get the following model: CCC 1 ( 1 ) CCCi, t j. X i, t.( i t i, ) (6) i, t t Which can be re-written as: 11

13 CCC (7) ' ' ' ' ' i, t 1 1. CCCi, t j. X i, t i t i, t where, the terms have their usual meanings defined earlier. The speed of reversion is estimated from the coefficient of the lagged dependent variable as (λ=1-β1). In order to overcome the problem of endogeneity and serial correlation, Baños Caballero et al. (2010) made use of two-step system generalized method of moments (GMM) proposed by Blundell and Bond (1998). Accordingly, we estimate the coefficients of equation (7), using the system GMM approach of Blundell and Bond (1998) for the full sample of firms. The results reported in column 1 of Table 1 suggest a speed of reversion of approximately 21% ( ) per year using system GMM approach. This would mean that mean reversion is rather very slow for firms in our data set; an average firm would take about five years to fully mean revert. This is consistent with our primary findings of no systematic target following in the previous section. We also test for these coefficients using the OLS and fixed-effect models. The results are reported in columns (2) and (3) in Table 1. The speed of reversion differs markedly between the OLS and fixed-effect model. In the case of the OLS, the coefficient of the laggeddependent variable suggests absolutely no reversion, however, the fixed-effect model shows the reversion of 29% in a year. These inconsistencies in our results are in line with the methodological drawbacks pointed out by Hsiao (2003) and Huang and Ritter (2009) in using the pooled OLS and fixed-effect models. While the OLS estimator overestimate the coefficient of the lagged dependent variable, the fixed-effect estimator underestimates it. Although, the estimation through system GMM approach is expected to overcome the biasedness in OLS and fixed-effect models, the accuracy of estimation is a function of serial correlations in the error terms and the validity of the instruments used. Baltagi (2008) show that instruments used for estimation in system GMM approach are often invalidated by the 12

14 second-order autocorrelation of the error terms. Further, weak instruments may result in even worse estimates (Nelson and Startz, 1990; Bound et al., 1995 and Bun and Windmeijer, 2010). Flannery and Hankins (2013) also suggest that the performance of the system GMM approach is sensitive to the number of observations per firm in the dataset; the estimation is relatively accurate for larger panel lengths. We investigate if our results using system GMM approach are affected due to these econometric challenges. First, we perform the Arellano-Bond test for zero autocorrelation in first-differenced errors. Since the first difference of independently and identically distributed error terms will be autocorrelated, the model misspecification cannot be established by no serial correlation at order one. Thus, we test for the null hypothesis at second-order under the null that there is no autocorrelation and report the p-values for same. It can be seen that the moment conditions are valid and serial correlation is not a problem for the results in column 1 using system GMM approach. Next, to test the validity of instruments used in system GMM approach, we perform the Sargan-Hansen test for overidentifying restrictions and report whether the instruments are valid at 5% significance levels. We find that instruments are invalid for the results in columns 1 suggesting that the model is mis-specified and the estimation may not be reliable. Apart from issues pertaining to the stability of coefficients in dynamic panel models, these models may not be particularly suited to study target behavior for our dataset with smaller panel length, as suggested by Flannery and Hankins (2013). Hovakimian and Li (2012) further question the utility of the coefficients in dynamic panel models as economically meaningful measures of target behavior. They suggest that instead of measuring the adjustment speed these coefficients tend to measure the frequency of full rebalancing in the sample. Thus, inferring target behavior through dynamic panel models may not be 13

15 suggestive, especially using a dataset for an emerging market. These findings, therefore, motivate us to study the target behavior using the modified testing strategy in the following sub-section. 3.2 Evaluating the Variability in Target Following Intensity: Single Period Analysis Our analysis in section 2 (Figure 1 to 3) suggests that the intensity of target following varies greatly depending on whether firms face negative and positive deviations from their targets or whether they actually exhibit target and non-target behavior. Thus, although an aggregate speed of reversion can be captured by the results in Table 1, these result mask the true variability in the intensity of target following in granular subsets of data. Since we wish to empirically analyze such variability for these subsets of firms, we may have to work with small sub-samples of the data. However, further splitting of data into sub-samples may further reduce the panel length per firm for these sub-samples where system GMM approach may not yield reliable results.. We, therefore, modify our evaluation process as follows. Considering that our aim is to examine the variability in target following behavior across firms, we estimate the statistics EMR for the firms in our data set using the following regression expression: CCC i, t 1 TCCi, t 1 EMR.( CCCi, t TCCi, t ) i, t (8) Where, terms have their usual meanings defined in section 2. To avoid the influence of the intercept term to impair the relationship in equation (4), while estimating equation (8), we normalize the dependent and independent variables by subtracting their respective means. Consequent to observing relative median target following intensities in the previous section, we now quantify these intensities at the firm level for the four categories (C1-C4) of the firms using equation (8). 14

16 Equation (8) is similar to the regression expression used to test the pecking order theory of capital structure in Shyam-Sunder and Myers (1999), except that it measures the fraction of residual deviation in cash cycles instead of the fraction of debt issued in the total financing deficit. However, Chirinko and Singha (2000) raise serious concerns regarding the stability of slope coefficient in the basic Shyam-Sunder and Myers (1999) tests. Specifically, the coefficients may not correctly capture the proportion of financing through debt versus equity for a sample that includes significant proportions of both types of securities. In our case, the target and non-target following firms in the data set are analogous to the firms issuing debt and equity in the Shyam-Sunder and Myers (1999) tests. Studying target following behavior separately for the target and non-target following firms is, therefore, consistent with the concerns raised in Chirinko and Singha (2000). In our empirical analysis, we use three criteria to identify and confirm the target behavior: (i) the proportion of firm-year observations in the target group should outweigh the proportion in the non-target group, i.e. most of the firms should be conforming to the target behavior; (ii) the slope coefficients (or the EMR statistics), reflecting the intensity of reversion, associated with the target group should be closer to zero as this would signify bridging most of the initial deviation in the subsequent period; and (iii) the slope coefficients associated with the non-target group should be closer to one. The last criterion considers that although the firms in the non-target group do not conform to target behavior, this could be due to several adjustment costs that prevent firms from adjusting quickly. If this is the case, then these firms would not deviate much from their initial levels in the subsequent period and would revert in further periods to follow. Consequently, we expect a slope coefficient closer to one for these firms; values farther away from unity indicates intense non-target behavior probably unrelated to any adjustment costs. Since we have intentionally isolated firms into target and 15

17 non-target groups, the slope coefficients would be less than and greater than one, respectively. 8 To begin with and for the comparison sake, we estimate the slope EMR in equation (8), first, for the entire data set without isolating firms with positive and negative deviations. Results are reported in column 1 of Table 2. A slope coefficient of suggests that, on an aggregate, firms do not revert in the subsequent period, or the speed of reversion ( ) is extremely slow to conclude any perceptible target following. This is consistent with our primary observations in section 2. However, since the target behavior seem to vary with the sign of deviation, we next estimate the slope coefficients by isolating these firms into positive or negative deviations with respect to their targets, but not isolating them into target and nontarget following firms. Results shown in columns 2 and 3 respectively for negative and positive deviations suggest that while 64% of the firms have their cash cycles less than their targets (negative deviations), the firms facing either type of deviations do not seem to revert in the subsequent period. While firms with negative deviations show very slow reversion (slope very close to but less than one), the firms with positive deviations show a slow divergent non-target behavior (slope close to but greater than one). These results are counter-intuitive to our primary findings in section 2, where firms with positive deviation show relatively more aggressive target following (Figure 1). This could be probably due to the concerns raised by Chirinko and Singha (2000) while using model such as equation (8) in case where target and non-target following firms are significant in numbers and are pooled together for analysis. 8 However, this is strictly applicable only for the line passing through origin, i.e. when the intercept in equation (8) is zero. Normalizing the variables by subtracting their means, ensures that the intercept is zero in our estimation. 16

18 We verify the concerns raised by Chirinko and Singha (2000) and finally examine the target behavior for the four categories (C1-C4) of the firms with positive and negative deviations further isolating them into target and non-target following groups separately. The results are reported in Table 3, columns 1 to 4. The results also report the fraction of the firms following target behavior among firms with negative and positive deviations separately. A series of interesting observations seems to follow the results. First, approximately only half of the firms follow target behavior in the subsequent period. Incidentally, this is also possible even when the subsequent cash cycle movements of the firms are randomly determined by the managers with the toss of a coin. Second, firms in their respective groups exhibit intense target and non-target following behavior; for example, firms following target behavior among those with negative deviations initially (C1), tend to revert by approximately 35% ( ) in a year. However, at the same time, the firms not following target behavior, among those with negative deviations (C2), tend to exhibit intense non-target behavior suggested by a coefficient quite far away from unity (1.194). The same is true for firms with positive deviations. Finally, part of the results in Table 3 are consistent with the preliminary findings in section 2 (Figure 1); firms with positive deviations tend to exhibit more intense target behavior as compared to firms with negative deviations. Thus, consistent with the insights in Chirinko and Singha (2000), our modified test results in Table 3 suggest that the slope coefficients in Tables 1 and 2 are indeed influenced by the large fraction of firms exhibiting intense non-target behavior (almost 50%) and therefore, should be studied separately. Overall, the results in Table 3 confirm our preliminary findings of no systematic target following by the firms in our dataset. 3.3 Adjustment Cost Concerns and Multi-period Transitions In all our previous tests for target behavior, we find no systematic evidence of target following based on the cash cycle movements of the firms in a single adjustment period. 17

19 However, it is possible that the intensity of target behavior is influenced by adjustment costs and frictions in the market, and firms may move toward their target cash cycles in a rather gradual manner during several subsequent periods. Therefore, we conduct multi-period tests to incorporate adjustment costs into our methodology. Specifically, we observe a firm s target behavior for the next five years after they deviate from their target cash cycle at t=0. To accomplish this, first we segregate target and non-target following firms at t=0. Subsequently, we re-categorize these firms at t=1, 2, 3, 4, and 5, into C1-C4 depending on whether a firm is facing negative or positive deviation and pursue a target or non-target behavior at these times. 9 We then test for the target behavior as before for each year separately. Considering gradual adjustments in the cash cycles towards their targets, we expect (i) improvement in target behavior overtime for firms exhibiting non-target behavior at t=0 and, (ii) firms exhibiting target behavior at t=0 to continue target following in future periods also. The results presented in panels A E of Table 4 are broadly similar to our earlier findings. The proportion of firms exhibiting target and non-target behavior remain approximately similar for each year; approximately half of the firms always exhibit non-target behavior regardless of whether the firms exhibited target or non-target behavior in the past. Further, the intensities of target following do not show any systematic improvement (continuance) of target behavior for firms exhibiting nontarget (target) behavior at t=0. Thus, consistent with our earlier findings, specifically those in section 2 (Figure 3), the cash cycle movement of the firms remains incoherent with a possible systematic target chase even in a relatively long span of five consecutive years. Another aspect of adjustment costs is highlighted in the surveys of Graham and Harvey (2001) and De Jong and Verwijmeren (2010) regarding capital structure decisions. Their findings suggest that for managers that claim to have a target, they are more likely to say that 9 Accordingly, the core results in Table 3 are for the categorization of firms into C1-C4 at t=0. 18

20 they have a target range rather than a specific target to work with. This implies that, probably due to several different forms of adjustment costs and little marginal benefit of adjustments, managers are averse to frequent adjustments unless there is significant deviation to call for a reversion. We, therefore, test for target behavior of only those firms where deviation from the target cash cycle exceeds certain minimum threshold. Since mean absolute deviation for our sample is 6 days, we use a minimum threshold of 10, 20 and 30 days beyond which a firm may revert with some certainty. However, for the sake of brevity, we report results using 20 days only as possible target range. 10 Results shown in Table 5 are similar to the results in Table 3, which suggest that while the cash cycle movements of the firms are incoherent with a target chase, they may also not be significantly affected by the manager s perspective of managing within a range of cash cycles Discussion and Robustness Given that approximately half of the firms do not exhibit target behavior and also that the target behavior varies intensely among target and non-target following groups in any given period, a statistics measured by the slope coefficient of the lagged dependent variable in dynamic panel models, as an indicator of the speed of reversion, would be insufficient in inferring the variability in target following among different sub-sets of the firms. Our analysis, therefore, provide deeper insights to conclude that there seems to be no perceptible systematic target behavior across firms, although there seem to be intense target and nontarget behavior followed by significant numbers of the constituent firms in a given period. We undertake several robustness checks to test the validity of our results. We discuss and report these tests in a separate Supplementary Appendix (SA 1 to 5). Specifically, (i) we validate our testing strategy using simulated datasets for intentional target behavior and random cash cycle movements; (ii) since targets are unobservable, we test the validity of our 10 The results are qualitatively similar using 10 and 30 days threshold and are available upon request. 19

21 results for couple of other target specifications; (iii) although the effect of several firmspecific attributes on the deviation of cash cycles is captured in equation (8) through the targets estimated using them, we cross-check our results by explicitly controlling for the effect of these firm-specific variables; (iv) extending the analysis in section 2 for varying magnitude of deviations, we test the mean reversion for firms categorized according to the magnitude of deviations (G1-G4) and, (v) since past literature suggest a non-trivial role of financial constraints in influencing working capital management of the firms, we test if our results are influenced by firm size which is an indicator of financial constraints. However, more formally, we use the SA index developed by Hadlock and Pierce (2010) to classify firms into those facing lower or higher constraints and test for target behavior as before. We find that our key findings in Table 3 are quite robust to all of these variations and therefore seem to be reliable to infer systematic target following by the firms. Our results are in sharp contrast to those of Baños Caballero et al. (2010 and 2013) for Spanish firms. The reasons for this difference in findings, to an extent, could be attributed to several factors related to the uniqueness of data pertaining to the emerging economy of India. First, firms in India face greater financial constraints with inadequate availability of bank and capital market financing. This makes external financing more costly than internal financing due to higher information asymmetry, agency problems between managers and investors, and transaction costs. Under this scenario firms may prioritize to smooth fixed investments with the help of working capital funds in the spirit of Fazzari and Petersen (1993) and forgo pursuing economically optimal working capital targets. Second, firms in the fast growing economies continuously come across new investment opportunities that are fraught with high degree of uncertainty. This calls for greater managerial flexibility in terms of decisionmaking, which could contrast with the notion of pursuing a predefined set of target cash cycles. Even though an optimal level of working capital exists, firms may not actively pursue 20

22 it on account of several such operational or strategic considerations. Our results do not rule out the possibility that there could be enough managerial discretion or uncorrelated motives for cash cycle movements in subsequent periods. 4. Determinants of Target Behavior Results in the previous sections suggest that working capital management decisions of the firms are not consistent with a systematic target following behavior. However, it is still not evident if the target behavior (or its absence) is influenced by firm-specific and macroeconomic factors and to what extent. Such an exploration would help us identify the extent to which the cash cycle movements with respect to their targets are driven by these systematic factors versus several idiosyncratic managerial and other considerations. We explore if there are characteristic differences between firms following target behavior from those that do not by using the following probit model: TAR i, t j. X i, t i, t (9) where, TARi,t represents the dummy for target behavior for firm i in period t set equal to 1 if the EMR calculated through equation (4), for year t, is less than one (implying target behavior) and zero otherwise. Xi,t represents the set of firm-specific and macroeconomic variables used in the past literature that could influence the movement in cash cycles or working capital needs (see for e.g., Hill et al., 2010 and Baños Caballero et al., 2010). Some of these variables are mentioned earlier in section 2. These are size, profitability, cash flows, asset tangibility, growth opportunities, financial distress, market share, leverage, age and median industry cash cycle. Importantly, a subset of these variables (i.e. size, profitability, cash flows, growth opportunities and financial distress) also represent financing constraint faced by the firms. However, we also include SA index of Hadlock and Pierce (2010) to explicitly see the effect of financial constraints. Further, since firms may smooth their capital 21

23 expenditures with the working capital investments, as suggested by Fazzari and Petersen (1993), we also include capital expenditures as a proportion of total assets to study this effect. Since EMR in equation (4) is calculated with respect to deviation in year t+1, using values of all the factors at time t avoid any simultaneous determination. In addition to these firm-specific variables, following the past literature, we also use certain macroeconomic variables that could influence working capital investments. Smith (1987) argues that the level of accounts receivables are influenced by macroeconomic conditions. Blinder and Maccini (1991) and Kashyap et al. (1994) show the relationship between economic cyclicality and levels of inventory. Carpenter et al. (1994) argue that the short term financing cost has larger impact on profitability for smaller firms than the larger firms. Further, the role of inflation could be non-trivial given that emerging markets are endowed with high inflation (Demirgüç-Kunt and Levine, 1996). Accordingly, we use the following macroeconomic factors in our analysis: (i) inflation rate, measured as an annual percentage change in the consumer price index; (ii) growth in nominal GDP in a year; and (iii) average short-term interest rates (call/notice money rates) in a given year. We collect data pertaining to these macroeconomic variables from the Reserve Bank of India database. The results are shown in panels A and B of Table 6 for firms with negative and positive deviations in their cash cycles with respect to their targets respectively. The signs of the coefficients of firm-specific variables in the two panels suggest that firm-specific characteristics and macroeconomic factors indeed influence the target following tendency of the firms with positive and negative deviations differently. Many of these variables differ in their signs for the two subsets of firms facing either negative or positive deviations. For example, while higher growth opportunities lead to increased target following for firms with negative deviations, they lead to lesser target following for firms with positive deviations. This suggest that there could be inherent differences in the motivation of the firms in these 22

24 two groups to induce any change in their cash cycles. These findings corroborates our idea, developed in section 2, of studying firms with negative and positive deviations separately. Inferences based on a pooled sample of firms with both types of deviation could be truly misleading. With an exception of firms age all other variables seem to be significantly influencing either or both types of firms with negative and positive deviations. Among most important of the variables significantly influencing target behavior irrespective of the nature of deviations are growth opportunities, leverage, asset tangibility, profitability, size, capital expenditures, financial distress and inflation. While cash flows and short term interest rates seem to significantly influence only firms with positive deviations, firms market share, median cash cycle, SA index and GDP growth seem to significantly influence only firms with negative deviations. Since we find that capital expenditure influences target following behavior positively, this implies that increased capital expenditures lead to reduction in working capital investments for firms with positive deviations, consistent with the smoothening of investment hypothesis of Fezzari and Petersen (1993). However, such is not the case for firms with negative deviations. Increasing capital expenditures lead to increase in working capital investments for them. Along with the statistical significance of the variables of interest, we also estimate the economic significance of the variables that suggest the change in the probability of target behavior by one standard deviation change in the variables. While most of the variables are found to be statistically significant, they are not quite significant in economic terms. For example, for firms with negative deviations, one standard deviation change in the GDP growth rate increases the probability of target behavior by 6.78%. Given that only 56% of the 23