Predictors of firm growth in India: An exploratory analysis using accounting information

Abstract

This paper aims at identifying relevant financial factors which critically affects firm revenue growth. We specifically focus on the dynamic nature of such factors across up or down-market cycles and also for different scales and size of business. The study uses annual data of 17 accounting and financial variables for a sample of 1,450 Indian firms which exist continuously between 2003 and 2014 and generate a framework for identification of critical factors which affect firm revenue growth. We employ a variable reduction technique via principal component analysis (PCA), and then use the “principal factors” identified thereon, in a logistic regression approach to develop such a framework. The study finds efficiency in management of current assets and capital (both short- and long-term) to be the most critical factors, determining the firm revenue growth in Indian context. The relative importance of capital deployment efficiency is more for small firms than for large firms whereas asset management efficiency is the most critical factor in larger firms. Long-term solvency supersedes all other factors during market downturns. These findings may have important implications for firms and its stakeholders as a priori knowledge on the importance of critical factors regarding firm’s revenue growth could enable the mangers to support them in their decision-making process.

Keywords:

• firm performance
• revenue growth
• financial information
• logistic regression

JEL classifications:

• G31
• G32
• M100
• M400

PUBLIC INTEREST STATEMENT

In the existing competitive and dynamic business-environment, it is essential for the firm and its stakeholders to identify the key factors influence firm growth. Firm growth is important for survival, contributes to creation of jobs and increase economic activity. This research attempts to identify the relevant financial factors which critically affect firm revenue growth for Indian firms. The findings suggest efficiency in management of current assets and capital (both short- and long-term) to be the most critical factors, determining the firm revenue growth in Indian context. For small firms, capital deployment efficiency and for the large firms, asset management efficiency are the most critical factor. During market downturns, the long-term solvency supersedes all other factors in the priority of importance.

1. Introduction

The crucial importance of firm growth in corporate finance has been studied widely in finance literature. Few studies highlight, that firms experiencing continuous growth have more likelihood of survival, contributes to creation of jobs and increase economic activity (Audretsch & Lehmann, 2005; Batjargal et al., 2013; Coad, Segarra, & Teruel, 2016; Thornhill & Gellatly, 2005). There are studies which reflect that at times, highly innovative firms may record low growth and vice versa. This may suggest that growth and survival cannot be studied in isolation (Mason & Brown, 2014; Nightingale & Coad, 2014). Moreover, Caves (1998) posit that reallocations of activity from the less efficient to the more efficient are extremely important for the optimal use of resources, and hence more evidence (obtained through research) is needed on how competitive conditions within an industry affect the speed with which the more efficient (high growth firms) displace the less efficient (low growth firms).

From the neoclassical to modern researchers, each school of thought have defined firm growth with different propositions however, the universal way is: a firm has to start, grow with various challenges, then mature and finally decline. In each stage several factors congregate in order to enable the firm’s progression allowing it to move to the next stage. For instance, while the neoclassical research confer that a firm will grow till an “optimal size” is achieved, the proponents of the “theory of growth of firm” suggests that there exists no limit to growth of a firm if its resources are effectively used and finally, the modern finance theorists, believes that due to excessive competition and significant technology changes the “theory of competitive advantage” is more appropriate for firms to grow. Despite the contradictions among different schools, the consensus among all is firm growth is beneficial for all the stakeholders like employees, managers, shareholders, creditors and even regulators and policy makers (Coad et al., 2016, Thornhill & Gellatly, 2005, Whetten, 1987).

In this paper, we attempt to identify the critical factors influencing firm revenue growth in India, using several financial statement-based measures. We use a balanced panel data of 17,400 firm years between 2003 and 2014 across 17 financial variables. We employ a variable reduction technique, namely principal component analysis (PCA) followed by application of qualitative response regression model. Our primary results reflect that the capital deployment efficiency and current asset management are the most critical factors in determining the firm revenue growth in Indian context. The relative importance of capital deployment efficiency is more for small firms than for large firms whereas asset management efficiency is the most critical factor in larger firms. Long-term solvency supersedes all other factors during market downturns. As highlighted above, we believe that these findings can have significant implications for multiple firm level stakeholders.

Our paper is different from some of the previous related works and contributes to the existing literature in the following ways. (i) Although much has been clarified on this issue for developed markets in US, Europe and Pacific Basin, not a lot of work has been done so far in emerging countries like India. (ii) We use a rich and extensive updated data of around 17,400 firm years (1,450 firms over 12 years). The results obtained should therefore be reasonably robust. (iii) It is possible, that the firm level parameters could be having variable predictive ability for sales growth, during periods of general exuberance in the market vis-a-vis periods of relative discomfort and uncertainty. To check for differential patterns, we divide our full sample period into market movement-based sub-samples (bull and bear) and do a sub-sample analysis separately for each. To the best of our knowledge, these have not been attempted before in related research. (iv) It is also possible that the association of firm level parameters and sales growth can be varying cross-sectionally (large firms and small firms). To capture that we repeat sub-sample analysis against a size dimension also. (v) A major criticism of return measures like Return on Asset (ROA henceforth) and return on equity (ROE) used in previous empirical studies, is the problem arising out of using asset or equity value at the end of accounting years (Baucus, Golec, & Cooper, 1993) or at the beginning (Brick, Palmon, & Venezia, 2015), in this study we modify our assessment to address that issue. Finally, we substantiate all our results obtained from the main analysis, through a series of robustness test as detailed in the methodology section.

The remaining part of the paper is organized as follows: the next section discusses the background theories and reviews relevant literature, Section 3 provides the data description and methodology adopted for the study. Section 4 presents the empirical results; Section 5 discusses the same while Section 6 provides the summary and conclusion.

2. Firm growth: background theory and related literature

A significant amount of work has been conducted to study the factors responsible for firm growth. However, there is no unified approach or theory to determine the critical factors promoting firm growth (Correa & Sharma, 2003). Therefore, we present a brief review of the theories of growth of firms in order to develop a framework to identify the factors influencing the firm growth.

The neoclassical economists like Viner (1931) believe that firms strive for growth till an optimal size is achieved, to exploit economies of scale to the fullest, beyond which they stop growing as the cost of managing such a large organization starts superseding the benefits. Following this hypothesis Coase (1937) in his seminal article came up with the “transaction cost theory of firms,” which states that the optimal boundary of growth of the firms is determined by the trade-off between advantages of coordination via authority in a hierarchy and advantages of coordination through price mechanism. They infer that, if the transaction costs are high, the firms are expected to expand and if low then the boundary of firm growth is expected to be small. In addition, Lucas (1978) linked firm growth with the managerial talent. He argued that large firms grow rapidly as their managers successfully use their skills which small firms are unable to achieve.

In contrast to the neoclassical school of thought, Penrose (1960) in his “theory of the growth of the firm” laid a resources-based perspective instead of limiting the “optimal size” of a firm. He emphasized that firm growth has no limit and results from optimum utilization of resources like managerial experience and managerial attention. The theory advocates that firm’s performance lies on the ability to create and use resources and continuously re-design the resource portfolio. In addition, Marris (1963, 1964) observed with his “managerial” theory that utility maximizing mangers expedite the firm’s growth rate subject to heavier incentives to them in form of compensation, bonuses and other perquisites. Even-though testing the “managerial” theory is difficult the author came up with an interesting conclusion that manger-driven firms’ growth rate is significantly higher than the owner-driven firms (empirical support in Hay & Kamshad, 1994). Marris (1963, 1964) also hinted on the fact that firm growth can be achieved by diversification. Muller (1969) extended this perspective and concludes that mergers are fastest way of growth compared to internal growth and the manger’s role become eminent for carrying out such expansions.

Furthermore, the principle of “growth of the fitter” evolved in the modern economies characterized by technical changes and cut-throat competition etc. This principle founded by Schumpeter, followed literature in form of theory (Alchian, 1950; Downie, 1958; Nelson & Winter, 1982) and empirical works (Metcalfe, 1993, 1994, 1998; Dosi, Marsili, Orsenigo, & Salvatore, 1995; Marsili, 2001; Hardwick & Adams, 2002; Baily & Farrell, 2006). These researchers find that two factors that impact firm growth are profitability and productivity. Hence they attributed these parameters as indicators of “fitness.”

In summary, the background theories on firm growth are contentious at best. However most of them highlight that there exist some systematic factors that impacts the process of firm growth. In order to delve into identifying the factors, we conduct a survey of empirical works relating to some of these critical variables influencing firm growth.

2.1. Size

Firm size is considered as the important factor influencing firm growth in the neoclassical theory. In order to test whether firm size is independent of firm growth, Gibrat (1931) proposed the “law of proportionate effect” or Gibrat’s law. The empirical model runs as:

where is the size of the firm and is the lagged size of the firm. The hypothesis of independence between firm size and firm growth holds if the coefficient value is one. If it is less than one, then smaller firms grow faster than larger ones and if more than one, the larger firm’s growth is supposed to be more than the smaller firms. Empirical research on these suppositions provides conflicting results with some studies (like Hart, 1962; Prais, 1974; Samuels, 1965) suggest positive relationship between firm size and growth rate while others (like Bottazzi & Secchi, 2003; Dunne & Hughes, 1994; Evans, 1987a, 1987b; Hall, 1987; Kumar, 1985) reports a negative relationship. Other studies like (Hart & Oulton, 1996; Lotti, Santarelli, & Vivarelli, 2003; Mowery, 1983) reports a nonlinear relationship between firm size and firm growth.

2.2. Profitability

Several recent studies like (Coad, 2007, 2009; Goddard et al. 2004) believes that firm growth and profitability is linked to each other. Early theoretical studies relating firm growth and profitability justify that growth has positive impact on profitability (see Verdoorn, 1949 and Kaldor, 1966, empirically supported in the works of Chandler & Jansen, 1992; Coad, 2007, 2009; Cowling, 2004, among others). They claim that growth enhances productivity as the firms achieve the benefits of economies of scale leading to higher profits. Alchian’s (1950) principle of “growth of the fitter” also suggests that fittest firms grow and survive in the market and it is possible that profit rates reflect the fitness of the firm to survive other tend to exit. However, Muller (1977) argues through the persistence of profits (POP) theory that as firms has free entry and exit any profit opportunity fades quickly and the profitability returns to its long-run average. But, there exist empirical studies which claim to have a negative relationship between firm growth and profitability (Markman Gartner, 2002; Reid, 1995).

2.3. Age

The theoretical work relating to firm growth and firm age of Arrow (1962) argues that if the “learning-by-doing” theory works than the firms with long existence have advantage over the young firms in the market. However, Evans (1987a, 1987b) and Dunne, Roberts and Samuelson (1988, 1989) analyzing the role of age in firm growth reports that for a given firm size its growth decreases proportionally as the firm gets older and vice versa for young firms. Empirical evidence on the inter-linkage between firm age and growth is also mixed in nature. (Dunne & Hughes, 1994; Fizaine, 1968; Variyam & Kraybill, 1992) report a negative relationship between firm age and growth, while Das (1995) reports a positive relationship in the Indian context.

2.4. Productivity

Theoretically, growth should be closely associated with productivity of the firm. However, there exist several perspectives like Jovanovic (1982) in the “passive learning” model entails that as the firm is small, its growth is bound by the fixed productivity level but Ericson and Pakes (1995) with their “active learning” model claimed that firms can influence their productivity levels by investing in research and development. However, empirical results suggest that this supposition may not be always true. Griliches and Regev (1995), Bottazzi, Cefis, and Dosi (2002), Bottazzi & Secchi (2006)), Baily and Farrell (2006) and Foster, Haltiwanger, and Syverson (2008) posit that productivity cannot be treated as a predictor of growth particularly in the industry set-up lacking competition.

2.5. Liquidity

Liquidity reveals a firm’s ability to meet its short-term obligations and quickness in converting an asset into cash at its fair market value. Current ratio and quick ratio are the most commonly used liquidity measure in finance (Mateev & Anastasov, 2010). Good liquidity management can improve operating results and enhance firm growth, whereas poor liquidity management can lead to weak operating profits and hurt firm growth (Moyer, McGuigan, & Kretlow, 2001). The results of empirical studies on impact of liquidity on firm growth is also somewhat mixed in nature, while some suggest a positive relationship between liquidity and firm growth (Baskin, 1987; Opler, Pinkowitz, Stulz, & Williamson, 1999); others posit a negative correlation (Shin & Soenen, 1998).

2.6. Financial leverage

Financial leverage captures a firm’s capital structure (debt versus equity) and reflects a firm’s ability to meet its long-term obligations exposed to financial risk (Mao & Gu, 2008). Debt in the capital structure has clearly some advantages (tax-shield) but beyond a certain level, the fixed interest and principal commitments of debt can really hurt the ability of a firm to operate freely and effectively. Given the mixed arguments about debt, some theories (Modigliani & Miller, 1963) in capital structure suggest usage of an optimal amount of debt for best results. Grossman and Hart (1986), Harris and Raviv (1990), and Zantout (1997) empirically document a positive association between financial leverage and firm growth, whereas Capon, Farley, and Hoenig (1990) and John (1993) showed a negative impact of financial leverage on firm growth.

2.7. Asset utilization efficiency

Asset utilization efficiency measures management’s efficiency in using firm assets to create sales over a certain period of time. Activity reveals how rapidly noncash assets flow through a firm and how quickly these assets generate revenue (Moyer et al., 2001). A positive relationship between assets, efficiency, and firm performance has been proposed and is empirically supported (Kiymaz, 2006).

In summary, various firm-wise financial factors (i.e., liquidity, financial leverage, asset utilization, profitability, size) are proposed as predictors of firm growth with inconclusive results. One possible reason for such conflicting results may be use of different measures of firm growth. Various measure typically used as proxy for firm growth are ratio of R&D expenses over sales revenue (Titman & Wessels, 1988), percentage sales growth (Wald, 1999), growth in total assets (Norvaišienė & Stankevičienė, 2007), market to book ratio (Beevan and Danbolt, 2002, 2004; Booth, Aivazian, Demirguc‐Kunt, & Maksimovic, 2001; Rajan & Zingales, 1995). This important issue of assessing predictability of firm growth remains mostly unaddressed in emerging markets particularly in India, barring a few and far between (Bhaduri, 2002a, 2002b; Bhole & Mahakud, 2004; Kakani, 1999).

3. Data and methodology

The study employs cross sectional time series panel data of 1,450 NSE listed firms screened for financial companies and companies with missing values. The data is collected from Prowess database of Centre for Monitoring Indian Economy (CMIE). To understand the factors which help in predicting sales growth, the study identifies 17 financial ratios or accounting variables (explained in Table 1) for the period 2003–2014. The rationale behind the number of variables selected is the precedence of using around 15–20 variables in the previous studies (see e.g. Molinero and Larraz, 2005; Uyar and Okumus, 2010).

Table 5. (A) Rotated factor coefficients, (B) size wise classification, and (C) time wise classification

Panel A Rotated Factor Coefficients

Full Sample (All Firms)

Variable

STSOL

SIZE

LTSOL

AMEFF

CADEFF

CAEFF

WCEFF

TA

.932

NS

.926

ROE

ROC

.716

ROA

.759

GM

CR

.978

QR

.978

DER

-.786

ICT

.785

CTR

.688

FAR

.756

CAT

.682

WCT

.774

DTR

.535

FGT

Panel B Size Wise Classification

The large firm sample produces six factors STSOL: Short Term Solvency; LTSOL: Long Term Solvency; AMEFF: Asset Management Efficiency; CADEFF: Capital Deployment Efficiency; CAEFF: Current Asset Efficiency; WCEFF: Working Capital Efficiency under the rotated Structure.

The small firm sample produces five factors STSOL: Short Term Solvency; LTSOL: Long Term Solvency; AMEFF: Asset Management Efficiency; CADEFF: Capital Deployment Efficiency; CAEFF: Current Asset Efficiency;

Large Firms

Small Firms

Variable

STSOL

LTSOL

AMEFF

CADEFF

CAEFF

WCEFF

Variable

STSOL

LTSOL

AMEFF

CADEFF

CAEFF

ROE

ROE

.803

ROC

.487

.460

ROC

.782

ROA

.570

ROA

.876

GM

GM

CR

.714

CR

.822

QR

.714

QR

.822

DER

–.236

DER

-.804

ICT

.666

ICT

CTR

.761

CTR

.575

FAR

.679

FAR

.889

CAT

.412

CAT

.681

WCT

.964

WCT

DTR

.753

DTR

.636

FGT

FGT

Panel C Time Wise Classification

The Bull phase sample produces 7 factors STSOL: Short Term Solvency; SIZE; LTSOL: Long Term Solvency; AMEFF: Asset Management Efficiency; CADEFF: Capital Deployment Efficiency; CAEFF: Current Asset Efficiency; WCEFF: Working Capital Efficiency under the rotated Structure.

The Bear Phase sample produces five factors STSOL: Short Term Solvency; SIZE, LTSOL: Long Term Solvency; AMEFF: Asset Management Efficiency; CADEFF: Capital Deployment Efficiency;

Bull Phase

Bear Phase

Variable

STSOL

SIZE

LTSOL

AMEFF

CADEFF

CAEFF

WCEFF

Variable

STSOL

SIZE

LTSOL

AMEFF

CADEFF

TA

.929

TA

.932

NS

.909

NS

.931

ROA

.761

ROA

.815

ROC

.728

ROC

.700

ROE

.788

ROE

.816

GM

-.560

GM

CR

.979

CR

.981

QR

.979

QR

.980

DER

–.789

DER

–.814

ICT

ICT

CTR

.712

CTR

.486

FAR

.758

FAR

.860

CAT

.543

CAT

WCT

.938

WCT

DTR

.610

DTR

FGT

FGT

Based on Varimax criterion, the seven components derived in Unrotated Matrix are labeled as STSOL: Short-Term Solvency; Size; LTSOL: Long-Term Solvency; AMEFF: Asset Management Efficiency; CADEFF: Capital Deployment Efficiency; CAEFF: Current Asset Efficiency; WCEFF: Working Capital Efficiency under the rotated Structure. The highest factor loading in Factor STSOL are from variables CR (.978) & QR (.978); TA & NS contributes significantly to SIZE, DER contributes negatively to LTSOL implies higher the DER adverse the LTSOL of the firm, ROA and FAR contributes to AMEFF, ROC & CTR forms the capital deployment efficiency/CADEFF, CAT & DTR groups to form CAEFF, WCT significantly affects WCEFF. All the factors seem relevant as they are backed by variables which correctly reflect the practical significance.

Table 6. (A) Full sample, (B) size wise classification, and (C) time wise classification

Panel A Full Sample
Principal Components		B		S.E.			Wald	df		Sig.		Exp(B)
STSOL		–.052		.018			8.518	1		.004		.949
SIZE		.166		.037			20.351	1		.000		1.180
LTSOL		.139		.028			24.963	1		.000		1.149
AMEFF		.247		.048			26.954	1		.000		1.281
CADEFF		1.425		.081			312.778	1		.000		4.159
CAEFF		.936		.143			42.800	1		.000		2.549
WCEFF		–.094		.049			3.745	1		.053		.910
PANEL B Size –Wise Classification
Large Firms							Small Firms
Principal Components	B	S.E.	Wald	df	Sig.	Exp(B)	Principal Components	B	S.E.	Wald	df	Sig.	Exp(B)
STSOL	–.087	.037	5.550	1	.018	.916	STSOL	–.082	.029	7.875	1	.005	.921
LTSOL	.230	.094	5.951	1	.015	1.259	LTSOL	.113	.060	3.530	1	.060	1.119
AMEFF	.645	.106	37.004	1	.000	1.906	AMEFF	.108	.062	3.041	1	.081	1.114
CADEFF	.608	.073	68.420	1	.000	1.836	CADEFF	.600	.089	45.723	1	.000	1.821
CAEFF	.005	.061	.008	1	.930	1.005	CAEFF	.187	.060	9.622	1	.002	1.206
WCEFF	.102	.039	6.819	1	.009	1.107
Panel C Time–Wise Classification
Bull Period							Bear Period
Principal Components	B	S.E.	Wald	df	Sig.	Exp(B)	Principal Components	B	S.E.	Wald	df	Sig.	Exp(B)
STSOL	–.042	.021	3.968	1	.046	.959	STSOL	–.04	.033	1.484	1	.223	.961
SIZE	.164	.032	25.745	1	.000	1.179	SIZE	.176	.078	5.063	1	.024	1.193
LTSOL	.168	.034	24.450	1	.000	1.182	LTSOL	.320	.125	6.591	1	.010	1.377
AMEFF	.052	.029	3.274	1	.070	1.053	AMEFF	.159	.060	6.961	1	.008	1.173
CADEFF	1.455	.101	208.189	1	.000	4.285	CADEFF	.853	.083	104.673	1	.000	2.346
CAEFF	.268	.077	12.209	1	.000	1.307
WCEFF	–.013	.023	.332	1	.564	.987

Dependent variable: SGR-Sales Growth Rate.

Independent variables: STSOL, SIZE, LTSOL, AMEFF, CADEFF, CAEFF, WCEFF.

Table 7. Robustness test results

Robustness Test Results Panel A Winsorized Full Sample
Principal Components	B	S.E.	Wald	df	Sig.	Exp(B)
STSOL	–.022	.032	6.456	1	.025	.979
SIZE	.086	.103	2.689	1	.040	1.090
LTSOL	–.039	.032	5.428	1	.023	.962
AMEFF	.164	.065	6.342	1	.012	1.178
CADEFF	.258	.029	108.36	1	.000	2.159
CAEFF	.048	.070	13.457	1	.002	1.186
WCEFF	–.109	.049	15.74	1	.033	.924
Panel B Size -Wise Classification
Large Firms							Small Firms
Principal Components	B	S.E.	Wald	df	Sig.	Exp(B)	Principal Components	B	S.E.	Wald	df	Sig.	Exp(B)
STSOL	.180	.293	.378	1	.539	.805	STSOL	-.092	.036	6.491	1	.011	.912
LTSOL	.230	.094	5.951	1	.015	1.259	LTSOL	.005	.052	.008	1	.928	1.505
AMEFF	.557	.237	5.551	1	.018	1.746	AMEFF	1.437	.029	2.391	1	.012	1.207
CADEFF	.284	.181	2.453	1	.017	1.329	CADEFF	.352	.099	12.693	1	.000	1.423
CAEFF	.115	.016	5.268	1	.038	1.875	CAEFF	.161	.052	9.431	1	.002	1.175
WCEFF	.102	.039	3.819	1	.009	1.007

Table

Panel C. Time wise classification
Bull Period							Bear Period
Principal components	B	S.E.	Wald	df	Sig.	Exp(B)	Principal components	B	S.E.	Wald	df	Sig.	Exp(B)
STSOL	.070	.105	.449	1	.503	.871	STSOL	.039	.136	.080	1	.777	1.039
SIZE	−.061	.027	5.084	1	.024	.941	SIZE	−.110	.045	5.970	1	.015	.896
LTSOL	.929	.272	11.682	1	.001	2.533	LTSOL	.267	.118	2.106	1	.084	1.306
AMEFF	.126	.034	13.969	1	.000	1.135	AMEFF	.625	.339	3.407	1	.065	1.868
CADEFF	−.144	.057	6.423	1	.011	.866	CADEFF	.516	.176	8.603	1	.003	1.675
CAEFF	.214	.102	4.420	1	.036	1.238
WCEFF	−.063	.029	4.622	1	.032	.939

Dependent Variable: SGR-Sales Growth Rate.

Independent Variables: STSOL, SIZE, LTSOL, AMEFF, CADEFF, CAEFF, WCEFF.

Table 1. Variables description

Variables	Descriptions
SGR	Sales Growth Rate (Dependent Variable)
TA	Total Assets
NS	Net Sales
ROE	Return on Equity (ROE = PAT/Total Equity
ROC	Return on Capital Employed (ROCE = PAT/Avg. Capital Employed)
ROA	Return on Assets (ROA = PAT/Total Assets
GM	Gross Margin (GM = Gross Profits/Sales)
CR	Current Ratio (CR = Current Assets/Current Liabilities)
QR	Quick Ratio (QR = (Receivables + Cash Balance + Marketable Securities)/Current Liabilities)
DER	Debt to Equity Ratio
ICT	Interest Cover Ratio (PBIT/Interest Expenses)
CTR	Capital Turnover Ratio (CTR = Sales/Capital Employed)
FAR	Fixed Asset ratio (FAR = Sales/Fixed Assets)
CAT	Current Assets Turnover (CAT = Sales/Current Assets)
WCT	Working Capital Turnover (WCT = Sales/Working Capital Employed)
DTR	Debtors Turnover Ratio (DTR = Sales/Avg. Debtors)
FGT	Finished Goods Turnover (FGT = Sales/Finished Goods)

This table display all the 17 variables used in the study with their explanation. The dependent variable (SGR) is classified as 1 and 0. 1 implies that sales have grown compared to previous period and 0 implies sales have fallen compared to previous period.

3.1. Sub-samples

To explore differential patterns, in the association between firm level financial variables and firm performance, if any, between sub-groups within our sample, we conduct all the analyses across sub-samples created along time and size partitions. The sub-sample creation modality is discussed hereunder:

3.1.1. Sub-samples based on firm size

As already mentioned in the previous section, firm size can have both positive and negative effects of firm performance (Berman, Wicks, Kotha, & Jones, 1999; Fama & French, 1993; Keating, 1997; Rogers, Helmers, & Koch, 2010; Westphal, 1998; Wu, 2006). To explore that possibility, we divide our full sample panel which comprises of 17,400 observations (i.e. Panel for 12 years of 1,450 companies) into two broad categories; large firms and small firms. The basis for such classification is based on the average total assets of the firms during the study period. For it, the full sample panel of 17,400 observations is divided into five quintiles each of 3,480 observations, the upper two quintiles (6,960 observations) form the sample for Large companies and the lower two quintiles (6,960 observations) forms the sample for the Small Companies panel.

3.1.2. Sub-samples based on time

It is possible, that the firm level parameters could be having variable predictive ability for sales growth, during periods of general exuberance in the market vis-a vis periods of relative discomfort and uncertainty. To explore such a possibility, we also classify our full panel across a time dimension into two broad categories: bull market periods and bear market periods. This is with the assumption that the general sentiment and expectation level of most stakeholders in the economy is adequately captured by the stock market movements, and that in turn should be affecting firm level prospects. The period for which market return has been less (more) than the risk free rate is considered to be bear (bull) years. We consider CNX Nifty Total Return index as the proxy for market and annualized 91-day T bill yields as the proxy for risk free rate. Thus our study considers 2003–2007, 2009, 2012 and 2014 as bull years and forms the panel consisting of 13,050 observations and 2008, 2011 and 2013 as bear years having 4,350 observations.

3.2. Correlation across variables

Table 2 shows that 47 out of 136¹ (i.e. 35%) of the correlations across variables selected by us are highly significant signaling the presence of considerable multi-collinearity problem amongst the explanatory variables. This may result in large variances and co-variances resulting in high standard error of the regression coefficients making precise estimation difficult. Standard regression models will thus fail to make accurate estimations in this scenario. In this context the standard procedures for estimation are standard variable reduction techniques like, partial least squares (PLS) and principal component analysis (PCA). As mentioned before, in the context of this particular question, PCA is more widely used in recent literature (Delen, Kuzey, & Uyar, 2013). We thus use PCA as our variable reduction technique in this study.

Table 2. Descriptive statistics

	Full sample		Size wise sample				Time wise sample
	All firms		Large firms		Small firms		Bull phase		Bear phase
Variables	N	μ	N	μ	N	μ	N	μ	N	μ
SGR	17,400	260.533**	6,960	27.911**	6,960	110.296*	13,050	187.568**	4,350	27.642**
TA	17,400	47,605.324**					13,050	13,079.09**	4,350	22,363.019**
NS	17,400	13,633.954**					13,050	42,356.595**	4,350	68,405.743**
ROE	17,400	5.016**	6,960	15.165**	6,960	2.76**	13,050	16.606**	4,350	4.197**
ROC	17,400	5.325**	6,960	9.699**	6,960	−3.141**	13,050	17.602*	4,350	2.542**
ROA	17,400	196.661**	6,960	34.009**	6,960	37.47**	13,050	25.132**	4,350	6.37**
GM	17,400	1,016.7**	6,960	36.196**	6,960	605.644**	13,050	76.507**	4,350	0.584**
CR	17,400	1.916**	6,960	1.801**	6,960	3.396**	13,050	3.046**	4,350	2.4*
QR	17,400	1.366**	6,960	0.901**	6,960	2.128**	13,050	1.904**	4,350	2.119**
DER	17,400	1.771**	6,960	1.941**	6,960	1.389**	13,050	1.339**	4,350	1.57**
ICT	17,400	83.968**	6,960	155.839**	6,960	13.381**	13,050	71.729**	4,350	122.048**
CTR	17,400	1.058**	6,960	1.417**	6,960	1.134**	13,050	4.269**	4,350	1.763**
FAR	17,400	9.862**	6,960	8.469**	6,960	14.229**	13,050	11.054**	4,350	1.155**
CAT	17,400	2.284**	6,960	2.643**	6,960	2.47**	13,050	8.085**	4,350	16.738**
WCT	17,400	5.875**	6,960	2.674**	6,960	5.069**	13,050	2.335**	4,350	2.306**
DTR	17,400	18.013**	6,960	14.531**	6,960	29.823**	13,050	18.588**	4,350	−2.446**
FGT	17,400	242.379**	6,960	625.077**	6,960	81.2**	13,050	416.29**	4,350	225.68**

This table presents the mean statistics of the 17 variables under study classified into three segments namely: full sample, size wise classification and time wise classification. Under size wise classification the mean values of TA & NS are absent as we intended to study the effect of rest of the variable on SGR when the full sample is classified on size basis. More than 90% of the variable’s mean are statistically significant which implies absence of outliers and missing data because the t-stat depends upon sample mean and sample variance which are both sensitive to outliers. In order to strive for model parsimony, we have checked for the specification error (i.e. inclusion of critical predictor variable and deletion of irrelevant variables). The practical significance of the variables is validated as most of the variable’s mean are in acceptable range (for instance FGT in full sample is 242 times which is more clear in size wise classification with a FGT of 625 times for large firm and 81 times for the smaller firms, when the sample is classified on bull and bear phase wise again FGT reflects that FGT increases to 415 times in bull and 225 times in bear time periods).

* Significance at 10% level.

** Significance at 5% level.

3.3. Research method

3.3.1. Principal component analysis

Principal component analysis (PCA – a data reduction technique) is used to transform a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components. In PCA, assuming that X_{m × n} is a data matrix, it is converted into a correlation matrix as F = (1/N)XX^T, where X^T is the transpose of X. We then diagonalize the N × N correlation matrix F in the form F = VMV^T, where M is the diagonal matrix of eigenvalues λ_i = (λ₁, λ₂,…,λ_N) in descending order and V is an orthogonal matrix of the corresponding eigenvectors. Each eigenvalue and the eigen vector is shown as , where, is the Kth principal component. The eigenvalues denotes to the portion of total variance in contributing to the principal component . Hence the total variance can be shown as . Thus, the proportion of total variance in F explained by the Kth principal component is .

The first principal component has the highest degree of variance and the subsequent components describe less variance in the data set. From earlier research it is evident, that a 90% trace criterion is sufficient to describe the maximum of the variation in the data set. Kaiser (1960) recommends retaining only those principal factors whose eigenvalues are greater than 1. Cattell (1966) suggest a graphical method through Scree Test which captures the relevant principal factors as the ones whose eigenvalues feature in the sharpest decent area of the curve before flattening out. In the present study we retain only those factors whose eigenvalues are greater than 1 and this criterion captures 60–80% of the total variance.

3.3.2. Logistic regressions (LR)

Once the correlated explanatory variables are reduced to specific factors or components, understanding the relationship of these factors with the dependent variable, is the next objective of this study. For that we convert the dependent variable, representing revenue or sales growth as a binary response variable, for which the possible estimation techniques available are Ordinary Least squares (OLS), discriminant analysis (DA) and Qualitative response regression models, like logistic regression model. Pampel (2000) confirms the logistic regression results to be more robust over OLS and discriminant analysis which are bound by the following assumption:

1. in OLS and DA independent variables should have a multivariate normal distribution;

2. The variance co-variance matrix of all the independent variables should be homogenic.

Moreover, LR uses maximum likelihood estimation instead of ordinary least square to derive parameters which becomes more relevant in case of large sample. Hence, we studied the impact of financial ratios on revenue growth of Indian firms in the logistic regression framework.

3.4. Robustness test

3.4.1. Repeating analysis over a winsorized sample

It is possible that our overall patterns visible may not reflect the generic trend due to presence of outliers. To control for that, we have winsorized the sample to limit the extreme values in the sample and to reduce the effect of possible spurious outliers. To do that, we sort the dependent variable (SGR) of full and sub-sample samples based on time and size dimension in ascending order. We then drop top 10% and bottom 10% from sample data of each sample, i.e. full as well as sub-samples. On the remaining 80% of the sample data, we repeat the data reduction technique, i.e. PCA and logistic regression for all the samples under study. The results of the robustness test are discussed in the following section.

4. Empirical results

Table 3 presents the results of the PCA. The table cells indicate eigenvalues and percentage of variance of the unrotated principal components. PCA leads us to extract the factor with common characteristics in the sample data (i.e. variables with high correlations are grouped in a factor, which we name as components and are presented in the table as Com-1,2,3, etc. In this study the criterion used to group variables into a factor are: latent root criterion (in which factors with eigenvalues more than 1 will be retained); percentage of variance criterion (in which factors will be retained till they explain a requisite amount of variance, based on past literature cumulative % of variance ranging in between 55% and 65% and more is accepted) and scree test criterion (in which factors are retained till the point the latent roots decline and flatten). As presented in Table 3, the full sample retains 7 components satisfying all the criterions. As far as the sub-sample analysis is concerned, with respect to size-based classification, PCA for the large and small firms identifies, respectively, 7 and 6 components, whereas for time-based classification, bull and bear phase identifies 7 and 5 components, respectively. The corresponding scree plots are shown in Figure 1.

Table 3. Correlations matrix for the full sample (2003–2014)

Variables	SGR	TA	NS	ROE	ROC	ROA	GM	CR	QR	DER	ICT	CTR	FAR	CAT	WCT	DTR	FGT	Correlation significant at 1/5%
SGR	1
TA	.001	1																0
NS	.003	.799**	1															1
ROE	.026**	.013**	.016**	1														3
ROC	.033**	.019*	.021*	.437**	1													4
ROA	.056**	.015	.013	.362**	.466**	1												3
GM	.000	−.001	−.003	.003	.009	.006	1											0
CR	−.002	−.019*	−.015	−.009	−.006	.026**	.002	1										2
QR	.000	−.015	−.016	−.007	.001	.048**	.005	.896**	1									2
DER	−.001	−.006	−.004	−.130**	−.044**	−.040**	−.001	−.011	−.014	1								3
ICT	.000	.110**	.031**	.046**	.083**	.114**	.000	.000	.006	−.008	1							5
CTR	.001	−.001	.016**	.028**	.063**	.018**	−.003	−.007	−.009	−.001	.002	1						4
FAR	.001	.008	.071**	.023*	.020*	.242**	−.006	−.001	.007	−.005	.005	.057**	1					5
CAT	.007	.022*	.078**	.069**	.075**	.001	−.019*	−.085**	−.088**	.003	.002	.047**	.088**	1				9
WCT	.000	−.001	.001	.000	−.001	.001	.000	.000	.000	.000	−.001	.000	.001	.001	1			0
DTR	.000	.000	.005	.028**	.041**	−.002	−.002	−.009	−.112**	−.002	.001	.011	−.001	.135**	.001	1		4
FGT	.000	.117**	.009	.113**	.006	.006	−.001	−.004	−.002	−.002	−.002	.001	−.001	−.004	−.001	−.002	1	2

This table shows that 47 out of 136 (i.e. 35%) of the correlations are highly significant signaling the presence of collinearity amongst the explanatory variables. This may result in large variances and co variances also high standard error of the regression coefficients making precise estimation difficult. It may also widen confidence intervals making true population coefficient as zero. Thus a reduced number of factors will be appropriate for further analysis hence Principal Component Analysis (PCA) is used as a data reduction technique in the current study.

*Correlation is significant at the .05 level.

**Correlation is significant at the .01 level.

Figure 1. Scree plots.

The unrotated principal components reduce the number of relevant variables originally considered from 17 to 7 in full sample case. However, it does not provide meaningful information regarding the factors or components derived. Hence the factors are rotated till practical significance results from it, in order to interpret the variables and factors meaningfully.

Tables 4 and 5 panels (A–C) presents the rotated factor matrix based on Varimax Criterion² containing the factor loadings for each variable on each factor for the full sample, size-based sub-samples and time-based sub-samples, respectively. Based on Varimax criterion, the seven components derived in Unrotated Matrix are labeled as STSOL: Short-Term Solvency; Size; LTSOL: Long-Term Solvency; AMEFF: Asset Management Efficiency; CADEFF: Capital Deployment Efficiency; CAEFF: Current Asset Efficiency; under the rotated Structure. Following are our principal observations:

1. The highest factor loading seems to be in the Factor short-term solvency (STSOL) which is contributed primarily from variables current ratio (CR) having a factor loading of 0.978 and quick ratio (QR) having a factor loading of 0.978.

2. Total assets (TA) and net sales (NS) contribute significantly to the broad factor Size,

3. Debt-equity-ratio (DER) contributes negatively to long-term solvency (LTSOL) which implies higher the DER adverse the LTSOL of the firm,

4. Return on asset (ROA) and fixed asset ratio (FAR) contribute significantly to asset management efficiency (AMEFF),

5. Return on capital (ROC) and capital turnover ratio (CTR) contributes to capital deployment efficiency (CADEFF),

6. Current asset turnover (CAT) and debtors’ turnover ratio (DTR) contributes to form current asset efficiency (CAEFF).

Table 4. Unrotated principal components: eigenvalues and % of variance

Principal component	Com-1	Com-2	Com-3	Com-4	Com-5	Com-6	Com-7
Full sample	All Firms
Eigenvalue	1.929	1.749	1.245	1.175	1.055	1.005	1.001
% of total variance	12.861	11.661	8.301	7.835	7.032	6.703	6.675
Cum. Eigenvalues	1.929	3.678	4.924	6.099	7.153	8.159	9.160
Cum % of variance	12.861	24.522	32.823	40.658	47.690	54.393	61.068
SIZE WISE FIRMS
	Large Firms
Eigenvalue	1.507	1.458	1.104	1.028	1.006	1.003
% of total variance	10.762	10.415	7.888	7.345	7.182	7.164
Cum. Eigenvalues	1.507	2.965	4.069	5.097	6.103	7.106
Cum % of variance	10.762	21.177	29.065	36.410	43.592	50.756
Small Firms
Eigenvalue	1.685	1.382	1.297	1.178	1.037
% of total variance	12.037	9.875	9.265	8.413	7.410
Cum. Eigenvalues	1.685	3.068	4.365	5.543	6.580
Cum % of variance	12.037	21.912	31.177	39.590	47.000
TIME WISE FIRMS
	Bull Phase
Eigenvalue	1.918	1.767	1.250	1.160	1.065	1.016	1.001
% of total variance	12.787	11.782	8.332	7.730	7.099	6.772	6.674
Cum. Eigenvalues	1.918	3.685	4.935	6.095	7.160	8.175	9.177
Cum % of variance	12.787	24.569	32.901	40.632	47.731	54.503	61.177
	Bear Phase
Eigenvalue	1.979	1.745	1.637	1.346	1.154	–	–
% of total variance	15.195	13.636	12.916	10.977	9.693	–	–
Cum. Eigenvalues	1.979	3.725	5.362	6.709	7.863	–	–
Cum % of variance	15.195	28.831	41.747	52.724	62.417	–	–

Observing high correlation amongst the explanatory variables presented in Table 2, the necessity of conducting component analysis becomes inevitable. It leads us to extract the factor with common characteristics in the sample data (i.e. variables with high correlations are grouped in a factor, commonly known as components as presented in the table as Com-1, 2, 3, etc.). In this study, the criterion used to group variables into a factor are: latent root criterion (in which factors with eigenvalues more than 1 will be retained); percentage of variance criterion (in which factors will be retained till they explain a requisite amount of variance, based on past literature cumulative % of variance ranging in between 55% and 65% and more is accepted) and scree test criterion (in which factors are retained till the point the latent roots decline and flatten). As presented in the table, the full sample retains 7 components satisfying all the criterions. Scree plot is given in Figure 1. Large and Small firms under size wise classification consider 6 and 5 components, respectively. Bull and Bear Phase under Time wise classification considers 7 and 5 components, respectively. Scree plots for each are given in Figure 1.

All the factors seem relevant as they are backed by variables which correctly reflect the practical significance.

Table

Panel B
Large Firms^a							Small Firms^b
Variable	STSOL	LTSOL	AMEFF	CADEFF	CAEFF	WCEFF	Variable	STSOL	LTSOL	AMEFF	CADEFF	CAEFF
ROE							ROE		.803
ROC		.487		.460			ROC				.782
ROA			.570				ROA			.876
GM							GM
CR	.714						CR	.822
QR	.714						QR	.822
DER		−.236					DER		−.804
ICT		.666					ICT
CTR				.761			CTR				.575
FAR			.679				FAR			.889
CAT					.412		CAT					.681
WCT						.964	WCT
DTR					.753		DTR					.636
FGT							FGT

^a The large firm sample produces six factors STSOL: Short-Term Solvency; LTSOL: Long-Term Solvency; AMEFF: Asset Management Efficiency; CADEFF: Capital Deployment Efficiency; CAEFF: Current Asset Efficiency; WCEFF: Working Capital Efficiency under the rotated Structure.

^b The small firm sample produces five factors STSOL: Short-Term Solvency; LTSOL: Long-Term Solvency; AMEFF: Asset Management Efficiency; CADEFF: Capital Deployment Efficiency; CAEFF: Current Asset Efficiency.

Table

Panel C
Bull Phase^c								Bear Phase^d
Variable	STSOL	SIZE	LTSOL	AMEFF	CADEFF	CAEFF	WCEFF	Variable	STSOL	SIZE	LTSOL	AMEFF	CADEFF
TA		.929						TA		.932
NS		.909						NS		.931
ROA				.761				ROA				.815
ROC					.728			ROC					.700
ROE			.788					ROE			.816
GM						−.560		GM
CR	.979							CR	.981
QR	.979							QR	.980
DER			−.789					DER			−.814
ICT								ICT
CTR					.712			CTR					.486
FAR				.758				FAR				.860
CAT						.543		CAT
WCT							.938	WCT
DTR						.610		DTR
FGT								FGT

^c The Bull phase sample produces 7 factors STSOL: Short-Term Solvency; SIZE; LTSOL: Long-Term Solvency; AMEFF: Asset Management Efficiency; CADEFF: Capital Deployment Efficiency; CAEFF: Current Asset Efficiency; WCEFF: Working Capital Efficiency under the rotated Structure.

^d The Bear Phase sample produces five factors STSOL: Short-Term Solvency; SIZE, LTSOL: Long-Term Solvency; AMEFF: Asset Management Efficiency; CADEFF: Capital Deployment Efficiency.

Factor loadings are the correlations of each variable and the factor. It indicates the degree of correspondence between the variable and the factor and variables with high factor loadings represents the factor significantly. Theoretically with a sample size of more than 400 observation factor loading of even of 30% is considered fit, however to make our results more robust we consider factor loading of 50% or more as fit in the study. The same methodology is followed for classifying the variables into the factors in case of full sample, size-based sub-samples (large and small firms) and time-based sub-samples (bull and bear phases).

Table 6 panels (A–C) present the results of LR estimation model for the full sample and the sub-samples based on size and bull-bear phase. Panel A presents the results for the full sample while panels B and C shows the results for the sub-samples based on size and bull-bear phase. The table cells show Wald statistics and odds ratio or exp(b) values for each factor, from which one can derive the increase in odds for increment in the dependent variable per unit of increment of the independent factor, one at a time, ceteris paribas. We present the principal observations hereunder:

1. We find that all the factors (7 in all) are statistically significant at 10% or less.

2. Based on odds ratio values, CADEFF seems to be the most critical factor while STSOL seems to be the least critical in determining revenue growth.

3. STSOL seem to be negatively related to sales growth as well as having a relatively small odds ratio in terms of magnitude. This implies that, with increment in these factors the sales growth reduces but the impact is considerably small in magnitude. So working capital management, in Indian context, at least based on the data and time period used in this study, seems to be less one of the less critical factors in determining sales growth per-se, although it could positively affect the cash flow scenario in the short run.

4. CADEFF seems to be the most critical factor with an odds ratio equal to 4.150 which implies that for one-unit increment in this particular factor raises the odds of increase in sales by more than 300%.

5. Next in the pecking order of criticality is CAEFF standing at an odds ratio of 2.549 which implies than an increment in one unit of this factor raises the odds of increase in sales by more than 150%.

6. Combining observations (3) and (5) above leads to an interesting conclusion. Short-term solvency which might be significantly contributed by cash holding seems to be not a critical factor while current asset efficiency which might include all items of current assets including cash holdings is a critical factor. This might imply that cash holding may be having a negative impact on sales growth.

7. SIZE, LTSOL and AMEFF are all having positive odds ratio of 1.18, 1.15 and 1.28 implying that the odds of increase in sales for one-unit increase in each of these factors are 18%, 15% and 28%, respectively.

Overall, capital deployment efficiency, current asset management efficiency, firm size, long-term solvency and asset management efficiency seem to be positively affecting revenue growth in decreasing order of importance. Short-term solvency is having an almost insignificant impact, which again points a possible negative impact of excess cash holding on the sales prospects.

Panels B and C of Table 6 shows the results of the subsamples. The overall results in both these samples are more or less in line with the full sample. We however present here the important incremental observations:

1. CADEFF and AMEFF are both critical factors for small as well as large firms, but the relative importance of CADEFF is more for small firms than for large firms, visible from the relatively higher odds ratio. It may be mentioned here that AMEFF refers to efficiency in managing primarily fixed assets while CADEFF refers to efficiency in managing the total capital deployed. This probably implies that, current assets form a less significant proportion for large firms while it is a significant in case of smaller firms. As such current asset management efficiency has a relatively lower criticality in case of large firms substantiated by lower odds ratio of 1.005 against CAEFF vis-a-vis an odds ratio of 1.206 for small firms (Table 6, Panel (B)).

2. Between bull and bear periods, again CADEFF is the most critical factor in both phases. One important observation, however, seems to be the relative importance of LTSOL during bull and bear periods. LTSOL seems to be an extremely critical factor during bear phases, representing periods of uncertainty (second only to CADEFF), while it is not so during bull periods, representing periods of exuberance. This is something in line with logical expectation, as during periods of overall uncertainty and fear, long-term solvency can certainly prove to be a differentiating factor for generating additional sales.

5. Discussion of results

Overall, our results reveal, that factors related to CADEFF, CAEFF, SIZE, LTSOL and AMEFF are positively affecting sales growth in decreasing order of importance. STSOL, is negatively related to sales growth although its impact is almost insignificant. This implies that STSOL, which is significantly contributed by cash holding, is not a critical factor, while CAEFF which includes all items of current assets including cash holdings is a critical factor. Combining these two observations the obvious conclusion is that excess cash holding may be having a negative impact on sales growth. This seems to be logical and one can easily identify three significant negative effects of excess cash holdings: lowering the return on assets, increasing the cost of capital and increasing the business risk by creating an overly confident management (Opler et al., 1999).

In the time-based sub-sample analysis, we find that during bull periods, i.e. periods of exuberance the results are almost similar to the findings obtained in full sample analysis. However, during bear cycles, i.e. period of discomfort and uncertainty, the importance of long-term solvency supersedes the current asset efficiency. This implies that in Indian context at least, during periods of overall uncertainty and fear, manifested long-term solvency in a firm generates positive expectations from the firm’s stakeholders and the market in general that translates into producing additional firm growth.

In the size-based sub-sample analysis, we find that, capital deployment efficiency is the most critical factor determining revenue growth for small firms while asset management efficiency is the most critical factor in case of large firms. This, we believe, must be resulting from relatively higher criticality of fixed asset management efficiency for large firms compared to small firms. For smaller firms, deployment of precious funds in fixed assets is probably less. This might be causing a marginal loss of economies of scale for them, which goes to the larger firms with greater deployment of funds in fixed assets. This improves the criticality of the AMEFF factor in larger firms. This, in conjunction with the observation that SIZE (proxied by total assets of firms) is also one of the critical factors impacting revenue growth, confirms this conjecture.

5.1. Robustness test results

Tables 7(A)–(C) present the results of the robustness test conducted through winsorizing the data. Panel A shows the results of analysis on the winsorized full sample. We find that these results are exactly similar to the results from the main analysis with CADEFF, CAEFF and AMEFF getting manifested as critical factors responsible for revenue growth. Panels B and C show robustness test results on winsorized sub-samples based on size and time dimension. Here also we find that, the findings are in line with our principal results. For small firms the results are exactly similar while for large firms the results are almost similar with CADEFF, CAEFF and AMEFF being the critical factors in determining the firm revenue growth. Only the sequence of criticality changes a little bit. Again, along time dimension for the winsorized sub-sample the results are exactly similar to our principal results. However, for the bull period winsorized sub-sample, once again the results are more or less similar with CADEFF, CAEFF and AMEFF getting manifested as principal factors in determining firm revenue growth with marginal change in criticality sequence of these factors. On the whole, we can say that the findings from the robustness test adequately substantiate findings from our main analysis.

Table

Panel B
Large Firms							Small Firms
Principal Components	B	S.E.	Wald	df	Sig.	Exp(B)	Principal Components	B	S.E.	Wald	df	Sig.	Exp(B)
STSOL	−.087	.037	5.550	1	.018	.916	STSOL	−.082	.029	7.875	1	.005	.921
LTSOL	.230	.094	5.951	1	.015	1.259	LTSOL	.113	.060	3.530	1	.060	1.119
AMEFF	.645	.106	37.004	1	.000	1.906	AMEFF	.108	.062	3.041	1	.081	1.114
CADEFF	.608	.073	68.420	1	.000	1.836	CADEFF	.600	.089	45.723	1	.000	1.821
CAEFF	.005	.061	.008	1	.930	1.005	CAEFF	.187	.060	9.622	1	.002	1.206
WCEFF	.102	.039	6.819	1	.009	1.107

Table

Panel C
Bull Period								Bear Period
Principal components	B	S.E.	Wald	df	Sig.	Exp(B)	Principal components		B	S.E.	Wald	df	Sig.	Exp(B)
STSOL	−.042	.021	3.968	1	.046	.959	STSOL		−.04	.033	1.484	1	.223	.961
SIZE	.164	.032	25.745	1	.000	1.179	SIZE		.176	.078	5.063	1	.024	1.193
LTSOL	.168	.034	24.450	1	.000	1.182	LTSOL		.320	.125	6.591	1	.010	1.377
AMEFF	.052	.029	3.274	1	.070	1.053	AMEFF		.159	.060	6.961	1	.008	1.173
CADEFF	1.455	.101	208.189	1	.000	4.285	CADEFF		.853	.083	104.673	1	.000	2.346
CAEFF	.268	.077	12.209	1	.000	1.307
WCEFF	−.013	.023	.332	1	.564	.987

Table

Panel B. Size classification
Large Firms							Small Firms
Principal components	B	S.E.	Wald	df	Sig.	Exp(B)	Principal components	B	S.E.	Wald	df	Sig.	Exp(B)
STSOL	.180	.293	.378	1	.539	.805	STSOL	−.092	.036	6.491	1	.011	.912
LTSOL	.230	.094	5.951	1	.015	1.259	LTSOL	.005	.052	.008	1	.928	1.505
AMEFF	.557	.237	5.551	1	.018	1.746	AMEFF	1.437	.029	2.391	1	.012	1.207
CADEFF	.284	.181	2.453	1	.017	1.329	CADEFF	.352	.099	12.693	1	.000	1.423
CAEFF	.115	.016	5.268	1	.038	1.875	CAEFF	.161	.052	9.431	1	.002	1.175
WCEFF	.102	.039	3.819	1	.009	1.007

Table

Panel C. Time wise classification
Bull Period							Bear Period
Principal components	B	S.E.	Wald	df	Sig.	Exp(B)	Principal components	B	S.E.	Wald	df	Sig.	Exp(B)
STSOL	.070	.105	.449	1	.503	.871	STSOL	.039	.136	.080	1	.777	1.039
SIZE	−.061	.027	5.084	1	.024	.941	SIZE	−.110	.045	5.970	1	.015	.896
LTSOL	.929	.272	11.682	1	.001	2.533	LTSOL	.267	.118	2.106	1	.084	1.306
AMEFF	.126	.034	13.969	1	.000	1.135	AMEFF	.625	.339	3.407	1	.065	1.868
CADEFF	−.144	.057	6.423	1	.011	.866	CADEFF	.516	.176	8.603	1	.003	1.675
CAEFF	.214	.102	4.420	1	.036	1.238
WCEFF	−.063	.029	4.622	1	.032	.939

6. Conclusion and implication

The findings reveal that efficiency in capital deployment (captured by ROC and CTR) and managing current assets (captured by CAR and DTR) are the factors positively affecting firm revenue growth. Long-term solvency (captured by DER and ICT) and asset management efficiency (captured by ROA and FAR) exhibits weak positive impact on firm revenue growth. Short-term solvency (captured primarily by cash holding) has weak negative impact on revenue growth. This along with the previous observation that efficiency in current asset which includes all items of current assets including cash holdings is a critical factor; thus implying that excess cash holding has a negative impact on firm revenue growth.

The implication of the study is manifold. For instance, organizations view higher growth rate as a critical in achieving competitive advantage over rivals through lower production costs with economies of scale and scope and diversification of business risk. The employees and managers, see higher revenue growth as increase in their opportunities for promotion, higher compensation and enhanced reputation. Shareholders’ preview higher firm growth rates to translate effectively into higher returns on investment. Policy makers think higher firm growth has significant implications, as that has potential to impact employment and economic growth which are important tools of macroeconomics. Thus, a prior knowledge of the importance of these factors will allow firms to be conscious and, in the position, to improve their performance.

The limitation of the current study lies in the fact that individual firms have idiosyncratic reason for the growth due to large heterogeneity between them; hence, possible further research can be directed toward industry centric firms. The present study also may be impacted by country specific syndrome which allows for a cross-country study as the critical factors regarding firm’s operation and its revenue growth can be identified for better management decisions.

Additional information

Funding

The authors received no direct funding for this research.

Notes on contributors

Sibanjan Mishra

Dr. Sibanjan Mishra is an assistant professor at Xavier University, Bhubaneswar. He holds a PhD from Dept. of Business Administration, Utkal University, India. His research interests are in area of corporate finance, asset pricing and energy finance. He has published several research papers in journals of international repute.

Soumya G. Deb

Dr. Soumya G. Deb is an associate professor at Indian Institute of Management, Sambalpur. He holds a PhD (FPM) from IIM-Calcutta. Prior to that he did PGDBM, also from IIM-Calcutta and his bachelor’s in engineering from Jadavpur University, Calcutta. His research interests are in the areas of Mutual funds, Corporate Finance, Investments and Portfolio Management, Asset Pricing, Causality and Volatility in Financial markets, etc. He is also reviewer of research papers in several reputed journals published by SAGE, Springer, Emerald, Elsevier, etc.

Notes

1. Total number of correlation coefficients estimated = (17 × 17 − 17)/2 = 136.

2. Varimax Criterion is considered over Quartimax and Equimax criterions as it simplifies the interpretation of variable-factor correlation by indicating clear positive or negative correlation or complete lack of association. Varimax rotation is an orthogonal rotation of the factor axes to maximize the variance of the squared loadings of a factor (column) on all the variables (rows) in a factor matrix, which has the effect of differentiating the original variables by extracted factor. Each factor will tend to have either large or small loadings of any particular variable. A varimax solution yields results which make it as easy as possible to identify each variable with a single factor. This is the most common rotation option.