Nexus Between Debt Financing and Market Value Moderated by Firm Size: Panel Data Evidence from India

Abstract

This study examines the effect of debt financing on market value of firm and evaluates the moderating effect of firm size on this relationship. Tobin’s Q and market-to-book value ratio are used as proxy for market value whereas long-term as well as short-term debt ratios are considered to indicate debt financing. Using data of 164 capital goods sector companies for 10 years (from 2010 to 2019), panel least square (PLS) regression with fixed and random effects (RE) model has been applied for data analysis. Based on findings, the study reports significant negative impact of borrowings (both long-term and short-term) on market value of selected companies. Further, the outcome of study confirms that firm size moderates the relationship between debt financing and firm value. The magnitude and significance of the effect of debt are stronger for small firms as compared to medium and large firms. Present verdicts will assist managers in designing capital structure policies by considering its impact on market value according to firm-size.

Keywords

Capital Structure Tobin’s Q Ratio Market-to-Book Value Firm Size Moderation Analysis

Introduction

Capital structure (CS) is essential combination of various securities (Abor, 2005) issued by a company for financing its business activities. Despite of extensive research work, the CS puzzle (Myers, 1984) has remained unsolved and lacks conclusive evidence on the relationship between CS and firm performance (Fosu, 2013). Following the ground-breaking work of Modigliani and Miller (1958), plentiful of theories and empirical research have been conducted in this area. Modigliani and Miller (1958) have pioneered research on CS by their argument of ‘irrelevance’ of leverage and firm value considering the assumptions such as perfect capital market, absence of taxes and transaction cost as well as homogenous expectations of investors. The notion of irrelevance was supported by Durand (1959) with ‘Net Operating Income’ approach. Later on, Modigliani and Miller (1963) have relaxed the assumption of taxes and incorporated the effect of corporate taxation on CS. Debt financing acts as tax-shield due to allowable deduction of interest expenses and thereby enhances the value of firm (Modigliani & Miller, 1963). But the proposition of tax-shield has been contradicted by Fama and French (1998) with empirical results that tax benefits associated with borrowings are not as substantial as promised theatrically. Besides, three major theories, that is, pecking order (Myers & Majluf, 1984), agency cost (Jensen & Meckling, 1976), trade-off (Kraus & Litzenberger, 1973; Myers, 1984) and market timing (Baker & Wurgler, 2002), are considered to be predominant in literature (Apanisile & Olayiwola, 2019). Pecking order theory emphasises on preferential hierarchy of financing and prescribes that firms rely on internal financing as their prominent choice followed by debt and lastly equity stocks. The theory considers information asymmetry between managers and investors as base for its argument because different information causes financing cost to vary across various sources of funds (Abor, 2005). Trade-off theory recommends an optimum debt ratio which can be obtained by equating the present value of benefits with the present value costs arising out of the given CS choice (Myers, 2003). Benefits of debt financing can be reckoned as tax savings (Modigliani and Miller, 1963), optimal investment strategy (Myers, 1977), lowering agency problems because debt acts as ‘watch dog’ on managers and controls wasteful and high-risk spending (Jensen & Meckling, 1976; Margaritis & Psillaki, 2007). Against this, debt financing has an inherent burden of insolvency as it brings commitment of cash flows which increases likelihood of bankruptcy (Ebaid, 2009). Therefore, every firm needs to trade-off between these costs and benefits arising from debt financing. Over a period, developments in capital markets have added new dimensions that affect behaviour of firms and their decision-making practices. Following these changes, Baker and Wurgler (2002) have proposed market timing theory that undertakes the effect of prevalent capital market conditions on CS decision. According to Baker and Wurgler (2002), companies issue new stock when shares are overvalued and buyback the stock when they are undervalued and try to align their CS with stock market environments. Research studies from Abor (2005), Margaritis and Psillaki (2007), Azhagaiah and Gavoury (2011), Pouraghajan and Malekain (2012), Fosu (2013), Chadha and Sharma (2015) and Appaiah et al. (2020) have attempted to provide empirical evidence on financing choice and its impact on firm value and profitability.

Though numerous studies have been steered in this direction, reported findings are inconsistent and contradictory which create scope for further probing. This study aims to examine the relationship between debt financing and market value of companies operating in one of the fastest growing economies, that is, India. Findings of this study have several contributions. First, the study analyses how firm size moderates the relation between debt financing and market value and enhances the understanding of this relationship. Second, the study exclusively focuses on market value of firm and hence macroeconomic variables are also considered which were often ignored in past literature (Chadha & Sharma, 2015). Third, the study is focused on capital goods industry as it requires huge funds to set up their operating facilities and carrying out their manoeuvres. Companies working in this industry account for higher payback period as high initial cost and longer operating cycle takes time to recover primary investments. Such distinctive characteristics make the financing decision more complex and substantial. Besides, the extant literature has studied only selected sub-segments of this industry such as iron and steel (Banerjee & De, 2014), energy (Tailab, 2014), and cement (Bhayani, 2009; Singh & Singh, 2016). However, this study considers the whole sector to address this gap. The capital goods industry contributes about 12% to the total manufacturing activity in India which translates to about 2% of GDP (CARE Ratings, 2020). The industry is a market of US$70 billion in 2019 and will reach to US$110 billion by 2025 (IBEF, 2019). The findings of this study will assist practitioners to design their CS after considering its impact on market value moderated by firm-size.

The remaining study is structured as follows. Second section highlights the review of major research inquiries and proposes a conceptual model basis the literature review. Further, research methodology has been portrayed in third section and data analysis and major findings are discussed in fourth section. Last section presents the concluding comments and future scope of research.

Review of Literature and Hypothesis Development

The literature on CS and firm performance can be grouped into three broad categories: (a) debt financing and accounting performance; (b) debt financing and market value of firm and (c) effect of firm-specific and macroeconomic factors on firm value. This section portrays the discussion on the literature relating to these three broad themes.

Debt Financing and Accounting Performance

Financial performance of any organization can be premeditated by accounting measures extracted from annual reports. Gross profit margin (Ebaid, 2009), return on asset (ROA) (Banerjee & De, 2014; Majumdar & Chhibber, 1999; Pandey & Sahu, 2017a; Pouraghajan & Malekain, 2012;), return on equity (ROE) (Abor, 2005; Ebaid, 2009; Twairesh, 2014) and earnings per share (Desai & Desai, 2018; Nguyen & Nguyen, 2020) are commonly used indicators of financial performance. Tax based (Modigliani & Miller, 1963) and trade-off (Jensen & Meckling, 1976) theories have advocated positive relationship between profitability and debt financing as high profit firms need to shield their income against taxes and therefore resort to higher borrowings. Empirical evidences from Fosu (2013) and Detthamrong et al. (2017) have supported this conclusion. On the contrary, pecking order theory (Myers & Majluf, 1984) proposes negative relation between profitability and borrowings as firms with higher profits rely on internal sources and retained earnings to fund their operations. Unjustified borrowings lead to direct or indirect bankruptcy costs, committed cash payments and increase the likelihood of liquidation. Azhagaiah and Gavoury (2011) have concluded inverse relation between debt financing and profitability of IT firms of India as increasing burden of interest cost tends to reduce net earnings significantly. Majumdar and Chhibber (1999), Pouraghajan and Malekain (2012) and Banerjee and De (2014) have also reported adverse effect of borrowed funds on profitability of firms measured by ROA and ROE. Excessive use of borrowings to avail tax advantage may deteriorate profitability (Zeitun & Tian, 2007) and hence companies have to trade-off between the benefit of tax-shield and finance cost.

Financial resources are means to long-term ends of business such as acquisition of fixed assets, capital expenditure as well as short-term, that is, operational ends like working capital financing. Companies opt for different sources of financing with varying maturity based on their requirement and hence it is worthy enough to differentiate the effect of short-term borrowing on profitability with that of long-term debt. Past studies such as Abor (2005), Ebaid (2009), Twairesh (2014), Hamid et al. (2015) and Nguyen and Nguyen (2020) have recognized this fact and adopted separate measure of CS to incorporate short-term borrowings and assessed its effect on accounting performance indicators. Lenders, while granting short-term loans, are exposed to less risk and hence charges lower cost compare to long-term loans. Therefore, short-term borrowings improve profitability whereas long-term loans deteriorate firms’ performance due to stringent terms (Abor, 2005). On the contrary, Ebaid (2009), Hamid et al. (2015), Twairesh (2014) and Nguyen and Nguyen (2020) have concluded strong negative impact of long-term as well as short-term debts on profitability.

Debt Financing and Market Value of Firm

Initially, scholars have focused on examining the effect of financing decision on accounting indicators which further extended to stock market-based indicators. Since primary objective of finance managers is to maximize shareholders’ wealth, it is inevitable to study the association between wealth of investors and financing choice (Kinsman & Newman, 1999). Artikis and Nifora (2012) have constructed leverage as risk factor and assess its strength in explaining the changes in return of stockholders. Using stepwise regression, they have concluded that leverage contains significant information that explains the stock market variations. Gupta and Kumar (2016), in their research on manufacturing firms, have evaluated value relevance of operating, financial and combined leverages in determining equity returns and have concluded significant impact of all leverages on market returns. Wealth of shareholders is reflected in market value of equity of the company and has been measured by Tobin’s Q (TQ) ratio (Appaiah et al., 2020; Budiharjo, 2020; Chadha & Sharma, 2015; Desai & Desai, 2018) and market-to-book value ratio (MBV) (Kansil & Singh, 2018; Putri & Rahyuda, 2020; Zeitun & Tian, 2007) in earlier as well as recent studies. Zeitun and Tian (2007) have used multiple indicators of accounting (three measures) as well as market based (four measures) performance and concluded that short-term debt has significant positive effect on TQ whereas long-term and total debt are insignificant determinants of TQ as well as MBV. Chadha and Sharma (2015) and Desai and Desai (2018) have also confirmed the same and advocated weak relation between CS choice and market-based performance indicated by TQ. Appaiah et al. (2020) have analysed effect of debt with varying duration on TQ for Ghanaian companies. They have concluded positive and significant effect of short-term debt on TQ whereas total debt ratio affects TQ adversely and significantly. Such positive impact can be explained as short-term debt is available at lower cost and more convenient terms than long-term which augments the financial performance and creates positive value for shareholders. Pandey and Sahu (2017b) have examined the effect of debt financing on TQ and concluded negative impact for Indian manufacturing firms. It is evidential that very few research studies have focused on financing decision and TQ ratio in the Indian context and that too fail to provide concluding evidence on the same. But cross-border studies have concluded significant impact of debt financing with the fact that maturity of debt have dynamic effect on firm performance hence the present study formulates following hypothesis relating CS and TQ.

H₀₁: There is a significant effect of long-term debt on Tobin’s Q ratio.

H₀₂: There is a significant effect of short-term debt on Tobin’s Q ratio.

MBV is more sensitive towards changes in shareholders’ wealth and can better reflect the same as against TQ which may dilute the intensity of market movement due to its computational method. Budiharjo (2020) has analysed panel data of Indonesian food and beverage industry using moderated regression model and concluded positive influence of debt financing on market value which provided empirical evidence for Modigliani and Miller (1963) and signalling (Ross, 1977) theory. On the contrary, Kansil and Singh (2018) and Putri and Rahyuda (2020) have reported negative and significant effect of debt financing on stock-market performance (measured by MBV) for Indian and Indonesian companies, respectively. Like previous section, existing literature does provide evidence on the effect of borrowings on MBV but portrays conflicting results hence following relationship has been hypothesized and will eventually be tested.

H₀₃: There is a significant effect of long-term debt on market-to-book value ratio.

H₀₄: There is a significant effect of short-term debt on market-to-book value ratio.

Effect of Firm-Specific and Macroeconomic Factors on Firm Value

Besides CS, firm value is a function of several other company specific as well as macroeconomic variables. Among firm specific factors, dividend policy is one of the most frequently used parameters (Kengatharan & Dimon Ford, 2019; Nautiyal & Kavidayal, 2018; Sharif et al., 2015). Gordon (1959) and Walter (1963) models have advocated relevance of dividend decision with market value of firm whereas Modigliani and Miller (1961) have contradicted this proposition and have presented irrelevance view. Further, Sharif et al. (2015), using panel data of firms listed on Baharian stock exchange, have concluded profitability (measured by ROE), price-earnings (P/E) ratio and firm size as major determinants affecting firm value whereas research findings of Rajhans and Kaur (2013) have proposed sales growth, tangible assets, cost of financing and profitability as key drivers of market capitalization of companies. Nautiyal and Kavidayal (2018) have studied the determinants of market value over an extensive period of 20 years (1995–2014) and concluded adverse effect of dividend policy (measured by dividend pay-out and dividend per share) and positive effect of economic value added on market value of firm. As against financial factors, Subanidja et al. (2016) have checked how corporate governance factors, moderated by leverage, affect firm’s market value. Based on regression results, they have concluded that earnings management, managerial ownership, audit quality and debt ratio are major determinants of firm value.

Though economic conditions of the operating country have significant bearing on market price of shares, limited studies have integrated the same in empirical analysis. Kengatharan and Dimon Ford (2019) have used GDP growth and currency exchange rate whereas Doan (2020) has used GDP growth and inflation rate to represent macro-economic conditions. Zeitun and Tian (2007) have used a dummy variable ‘political condition’ as economic indicator whereas Artikis and Nifora (2012) have used capital market movements and market risk premium to represent economic fluctuations. Most of the studies have concluded no-to-weak positive effect of macroeconomic variables on firm value. Favourable economic conditions improve confidence of investors that boost the market trend upwards and vice-a-versa therefore macroeconomic indicators play crucial role in determining stock performance (Artikis & Nifora, 2012; Zeitun & Tian, 2007).

Moderating Role of Firm Size

Firm size has been considered as one of the most important variables determining firm performance (Abor, 2005; Banerjee & De, 2014; Pouraghajan & Malekain, 2012). Effect of debt financing on firm performance varies according to size of the firm (Detthamrong et al., 2017) as large firms can leverage on economies of scale, better access to credit and lower degree of bankruptcy (Ebaid, 2009; Zeitun & Tian, 2007). On the contrary, large firms confront coordination issues due to longer hierarchical structure and multiple controlling mechanisms (Majumdar & Chhibber, 1999). Hence, it is critical to understand how firm size moderates the impact of debt financing on market value of company. This study implements the approach of Zeitun and Tian (2007) and Detthamrong et al. (2017) to study size-wise differences and group the sample companies as small, medium and large based on their asset size.

Conceptual Model

Figure 1 highlights the major factors identified from literature that represent CS, market value, company-specific and macroeconomic control variables. To obtain more reliable results, the study includes factors like free cash flow, efficiency and market returns which are not discussed in previous subdivision but rationale/source of the same has been explained in the subsequent section (Figure 1).

Figure 1.

Source: The author.

Research Methodology

Variables of Study

This study proposes to analyse the effect of debt financing on market value of selected companies. Use of absolute market price may prone to spurious results because of high volatility and abnormal values. Further, absolute market prices are not directly comparable and hence value of the firm has been measured using TQ and MBV following the extant literature (see Table 1). Use of multiple measures ensures robustness of results and condenses the measurement error. Table 1 highlights the dependent, independent and control variables considered for analysis along with their source/rationale and computation method.

Sample Selection and Data Collection

For analysis, sample companies from capital goods sector are selected using multi-stage sampling technique and data has been collected for a period of 10 years (2010–2019). Secondary data packages such as Prowess from Centre for Monitoring Indian Economy (CMIE) and Ace Equity are utilized for extracting company level and economic data. As per CMIE Prowess, 224 companies are listed under capital goods sector in India which are considered for inclusion in sample. Filters such as continuous listing and availability of data for each variable during study period are applied and based on that 178 firms are selected. Further, companies with negative net worth and extreme values are eliminated to reduce dilution of results. Finally, 164 companies are selected after all necessary treatments and balanced panel of 1,640 firm-year observations has been developed. As pointed in previous section, companies are categorized based on total asset value as ‘Small’ (less than INR 10,000 million), ‘Medium’ (INR 10,000 million to INR 35,000 million) and ‘Large’ (more than INR 35,000 million). Out of 164 companies, 53 firms are classified as small, 53 as medium and 58 as large-size and regression analysis has been conducted at overall industry level as well as sub-segment level data.

Table 1.

Operationalization of Variables

Variable Name	Computation Method	Source/Rationale
Firm value
Tobin’s Q ratio	$\frac{Equity market value+Book value of debt}{Total assets}$	Chadha and Sharma (2015); Budiharjo (2020)
Market-to-book value ratio	$\frac{Market value of shares}{Book value of shares}$	Kansil and Singh (2018); Putri and Rahyuda (2020)
Debt financing
Long-term debt ratio	$\frac{Long - term debt}{Total asset}$	Abor (2005); Ebaid (2009)
Short-term debt ratio	$\frac{Short - term debt}{Total asset}$	Twairesh (2014); Appaiah et al. (2020)
Control variables
Sales growth	$\frac{S_{t} - S_{t - 1}}{S_{t - 1}}$	Rajhans and Kaur (2013)
Profitability	$\frac{Net income}{Shareholders equity}$	Sharif et al. (2015)
Free cash flow	$\frac{Operating cash flow - Capital expenditure}{Total asset}$	Myers (2001); Mansourlakoraj and Sepasi (2015)
Dividend payout ratio	$\frac{Dividend per share}{Earnings per share}$	Nautiyal and Kavidayal (2018)
Asset turnover ratio	$\frac{Sales}{Total assets}$	Asset turnover reflects firm’s ability to utilize its fixed assets and indicates efficiency of operations which affects shareholders’ return expectations.
GDP growth	$\frac{G D P_{t} - G D P_{t - 1}}{G D P_{t - 1}}$	Kengatharan and Dimon Ford (2019); Doan (2020)
Market returns	Annual return of NSE NIFTY 50	Index returns indicate the overall position of capital market and become the primary base in setting expectations of shareholders.

Source: Complied by author.

Econometric Methods

Panel methodology is appropriate for inferential analysis and model building while studying longitudinal cross-section data as it controls individual heterogeneity and collinearity among variables (Twairesh, 2014). For comparison purpose, multiple regression models are estimated using ordinary least square (OLS) as well as PLS methods. OLS method is based on pooling of data set on one another and ignores cross sectional effects. Following regression models are formed using OLS method.

Pooled OLS Model

T Q_{i t} = α + β_{1} L D R_{i t} + β_{2} S D R_{i t} + β_{3} S G_{i t} + β_{4} P R F_{i t} + β_{5} F C F_{i t} + β_{6} D P R_{i t} + β_{7} A T_{i t} + β_{8} G D P G R_{i t} + β_{9} M R_{i t} + u_{i t}

M B V_{i t} = α + β_{1} L D R_{i t} + β_{2} S D R_{i t} + β_{3} S G_{i t} + β_{4} P R F_{i t} + β_{5} F C F_{i t} + β_{6} D P R_{i t} + β_{7} A T_{i t} + β_{8} G D P G R_{i t} + β_{9} M R_{i t} + u_{i t}

Panel data analysis has been performed using fixed-effects (FE) and RE models to study variation in intercept between cross sectional units (Nautiyal & Kavidayal, 2018). FE model assesses the firm-wise variation in intercept assuming same slope, constant variations and time invariant individual effects (Sharif et al., 2015). On the contrary, RE treats individual intercept as random variable with mean value β₁ and express intercept of each company as β_1i = β_{1 +} ε_i where ε_i is random error with zero mean (Gujarati, 2003).

Fixed Effects Model

T Q_{i t} = β_{1 i} + β 2 L D R_{i t} + β_{3} S D R_{i t} + β_{4} S G_{i t} + β_{5} P R F_{i t} + β_{6} F C F_{i t} + β_{7} D P R_{i t} + β_{8} A T_{i t} + β_{9} G D P G R_{i t} + β_{10} M R_{i t} + u_{i t}

M B V_{i t} = β_{1 i} + β 2 L D R_{i t} + β_{3} S D R_{i t} + β_{4} S G_{i t} + β_{5} P R F_{i t} + β_{6} F C F_{i t} + β_{7} D P R_{i t} + β_{8} A T_{i t} + β_{9} G D P G R_{i t} + β_{10} M R_{i t} + u_{i t}

Random Effects Model

T Q_{i t} = β_{1} + β_{2} L D R_{i t} + β_{3} S D R_{i t} + β_{4} S G_{i t} + β_{5} P R F_{i t} + β_{6} F C F_{i t} + β_{7} D P R_{i t} + β_{8} A T_{i t} + β_{9} G D P G R_{i t} + β_{10} M R_{i t} + ε_{i} + u_{i t}

M B V_{i t} = β_{1} + β_{2} L D R_{i t} + β_{3} S D R_{i t} + β_{4} S G_{i t} + β_{5} P R F_{i t} + β_{6} F C F_{i t} + β_{7} D P R_{i t} + β_{8} A T_{i t} + β_{9} G D P G R_{i t} + β_{10} M R_{i t} + ε_{i} + u_{i t}

Data Analysis and Findings

Descriptive Statistics

Descriptive statistics presented in Table 2 highlight the overview of data collected using measures like mean, standard deviation, maximum and minimum. Observing the mean values of TQ (1.23) and MBV (2.08), the firms have performed well during the study period but the standard deviation reported in case of MBV (3.38) is much higher than average value indicating high volatility in returns. Long-term debt ratio (LDR) and short-term debt ratio (SDR) respectively measure proportion of long-term and short-term debts in total assets and their average values (17.33% and 13.71%) indicate lower reliance on debt financing by selected companies. Among control variables, sales growth and profitability have also reported satisfactory results. Mean and standard deviation values of dividend pay-out are 17.45% and 48.65% showing instable dividend policy. Besides, lower pay-out ratio signifies that firms retain huge portion of their earnings as reserves for investment purpose. Macroeconomic conditions are also depicting poor-to-average scenarios measured by average GDP growth of 6.93% and market return of 10% during the study period.

Stationarity Test

Non-stationarity is one of the major concerns of time-series data which results into spurious and faulty regression output. To check the same, Levin–Lin–Chu (panel unit root) and Augment Dickey Fuller (individual series) tests have been applied. The results are summarized in Table 3. Both tests are performed at level data with maximum lag selection using Schwarz info criteria and the results support rejection of null hypothesis that series contains a unit root. It confirms that series is stationary and suitable for further analysis.

Table 2.

Descriptive Statistics

	Mean	Std. Dev.	Minimum	Maximum	Obs.
Tobin’s Q ratio	1.2324	1.0663	0.1989	13.7810	1640
Market-to-book value ratio	2.0750	3.3846	−10.2421	110.1094	1640
Long-term debt ratio	0.1733	0.1304	0.0000	0.6322	1640
Short-term debt ratio	0.1371	0.1006	0.0000	0.4951	1640
Sales growth	0.1341	0.3991	−0.8482	11.1555	1640
Profitability	0.1132	0.1486	−2.2762	0.8008	1640
Free cash flow	0.0743	0.0787	−0.4288	0.5753	1640
Dividend payout ratio	0.1745	0.4865	−14.2041	5.9238	1640
Asset turnover ratio	1.1000	0.9890	0.0124	16.3294	1640
GDP growth	0.0693	0.0106	0.0524	0.0850	1640
Market returns	0.1000	0.1589	−0.2505	0.2958	1640

Source: Complied by author.

Correlation Analysis

Degree of association among selected variables has been analysed using Pearson correlation and results are portrayed in Table 4. Both leverage variables, long-term debt and short-term debts, are negatively and significantly (p-values < .01) correlated with performance indicators. But it should be kept in mind that correlation can depict the linearity of relationship and not causation between said variables (Apanisile & Olayiwola, 2019). All control variables have significant association with TQ ratio except sales growth whereas MBV have significant relation with all variables except sales growth and market return. Further, serial correlation coefficients among exploratory variables are less than 0.5 indicating lower degree of multicollinearity in the data (Detthamrong et al., 2017).

Multicollinearity and Autocorrelation

Multicollinearity and autocorrelations are the major anxieties affecting reliability of regression results and to control the same variance inflation factor (VIF) and Durbin–Watson (DW) test has been applied. VIF values (3.238 (overall), 2.391 (small-size), 3.589 (medium-size), 2.419 (large-size)) are within acceptable range (<10) (Gujarati, 2003; Nautiyal & Kavidayal, 2018) hence the problem of multicollinearity has been controlled. Further, minimum and maximum values of DW statistics, across all results, are 1.35 and 2.25 indicating allowable degree of autocorrelation (Gujarati, 2003).

Table 3

Unit Root Test

	Augment Dickey–Fuller Test	Levin–Lin–Chu Test	Nature of Series
Tobin’s Q ratio	512.943*	−17.9787*	Stationary
Market-to-book value ratio	590.303*	−24.7561*	Stationary
Long-term debt ratio	1097.342*	−49.8261*	Stationary
Short-term debt ratio	456.705*	−85.5965*	Stationary
Sales growth	900.230*	−37.0169*	Stationary
Profitability	653.990*	−29.2962*	Stationary
Free cash flow	861.784*	−29.5749*	Stationary
Dividend payout ratio	588.274*	−47.1294*	Stationary
Asset turnover ratio	534.618*	−25.5748*	Stationary
GDP growth	878.618*	−23.1885*	Stationary
Market returns	1532.625*	−57.5509*	Stationary

Source: Complied by author.

Note: * indicates significance at 1% level.

Table 4.

Correlation Matrix

	TQ	MBV	LDR	SDR	GR	PRF	FCF	DPR	ATR	GDPGR	MR
TQ	1.0000
MBV	0.5228*	1.0000
LDR	−0.1226**	−0.0855**	1.0000
SDR	−0.0785**	−0.0388**	−0.2115	1.0000
SG	0.0603	0.0410	0.0077	−0.0037	1.0000
PRF	0.2388**	0.1779**	−0.1252*	−0.0031*	0.1560	1.0000
FCF	0.1375*	0.0703*	−0.0104	−0.2559*	−0.0502	0.2142*	1.0000
DPR	0.0756*	0.0443*	−0.0656*	−0.0551	0.0275	0.0666	0.0365*	1.0000
ATR	0.0646**	0.0222**	−0.1879	0.2409	0.0708	0.1873**	0.0744	0.0060	1.0000
GDPGR	0.1654**	0.1194**	−0.0340	0.0362	0.0157*	−0.0724	−0.0924	0.0349	0.0134	1.0000
MR	0.0111*	0.0078	0.0511	0.0339	–0.0374	0.0121*	–0.0074	−0.0154	0.0155	0.2050*	1.0000

Source: Complied by author.

Notes: ATR = asset turnover ratio; DPR = dividend payout ratio; FCF = free cash flow; GDPGR = GDP growth; LDR = long-term debt ratio; MBV = market-to-book value ratio; MR = market returns; PRF = profitability; SDR = short-term debt ratio; TQ = Tobin’s Q ratio. * indicates significance at 5% level; ** indicates significance at 1% level.

Selection of Econometric Model

Several econometric tests are conducted for selection of appropriate model from pooled OLS, FE, and RE. Redundant fixed effect-Chow test (Lupoiu & Raceanu, 2019; Pandey & Sahu, 2019; Zulfikar, 2018) has been performed to check applicability of FE model over pooled OLS. The underlying hypothesis of this test is that there are no cross-section effects, that is, intercept of all firms is equal. Further, Hausman (1978) test (Chadha & Sharma, 2015; Pandey & Sahu, 2019) has been applied for the selection between FE and RE models with null hypothesis that RE model is appropriate. Result of Chow test signifies the rejection of null hypothesis and conveys appropriateness of FE model over pooled OLS for TQ ratio (χ² = 1211.751; p < .01) as well as MBV (χ² = 1216.657; p-value < .01) model. Further, computed values of Hausman test are highly significant (p-values < .01) confirming the applicability of FE models over RE. Similar interpretations can be drawn for size-wise estimates as well. Hence, further explanations and hypothesis testing are based on output of FE model.

Result of Econometric Models

Tables 5–8 present the abridged view of regression output using pooled OLS, FE and RE models. Table 5 provides the output for the whole sample whereas size wise results are portrayed in Tables 6–8 respectively for small, medium and large firms. Robustness of econometric models has been analysed using F-test and the obtained values are highly significant (<.01) indicating validity of models. FE regression outcomes of overall data set show negative and highly significant impact of LDR (−0.9782 (0.0000), −3.1538 (0.0001)) as well as SDR (−2.1139 (0.0000), −5.2683 (0.0006)) on both measures of market value. Hence, the study accepts the null hypothesis (H₀₁, H₀₂, H₀₃ and H₀₄) formed in the earlier section. Among the control variables, profitability, asset turnover and GDP growth are significant determinants of TQ as well as MBV. Results support the conclusions drawn by Rajhans and Kaur (2013), Sharif et al. (2015) and Kengatharan and Dimon Ford (2019).

Table 5.

Regression Output of Overall Industry (1640 Obs.)

	Model 1 (TQ)			Model 2 (MBV)
Coeff.	Pooled	FE Models	RE Models	Pooled	FE Models	RE Models
Inter.	2.3166**	2.6828**	2.6248**	5.8877**	6.5574**	6.3419**
Long-term debt ratio	−0.9486**	−0.9782**	−0.9995**	−3.1336**	−3.1538**	−3.2569**
Short-term debt ratio	−0.9045**	−2.1139**	−1.7669**	−1.4772	−5.2683**	−2.9261**
Sales growth	0.0940	0.0298	0.0377	0.7183**	0.6342	0.6838**
Profitability	1.3384**	0.3647*	0.5313**	5.6879**	2.6502**	1.2450**
Free cash flow	0.7915*	0.2915	0.1167	4.1958**	2.1148	3.0969**
Dividend payout ratio	0.1175*	0.0518	0.0605	0.3477*	0.1478	0.2244
Asset turnover ratio	0.0248	0.1248*	0.0943*	0.1528	0.7356**	0.3216**
GDP growth	1.6683**	1.0810**	1.8817**	1.2443**	2.9965**	1.2809**
Market returns	0.1990	0.2338	0.2274	0.7667	0.8528	0.8192
F- value	21.0839**	12.2730**	20.1054**	16.7613**	4.4050**	23.6656**
Adj. R²	0.2816	0.5422	0.3971	0.2115	0.4635	0.3131
Durbin–Watson stat.	1.4344	1.8534	1.7578	1.6610	2.1616	1.9502
Chow (χ²) test		1211.751**			1216.657**
Hausman test			34.6477**			45.1269**

Source: Complied by author.

Notes: FE = fixed-effects; MBV = market-to-book value ratio; RE = random-effects; TQ = Tobin’s Q ratio. * indicates significance at 5% level; ** indicates significance at 1% level.

Table 6.

Regression Output of Small Size Firms (530 Obs.)

Coeff.	Model 1 (TQ)			Model 2 (MBV)
Coeff.	Pooled	FE Models	RE Models	Pooled	FE Models	RE Models
Inter.	3.0593**	3.9236**	3.7153**	5.3049**	6.7124**	6.3134**
Long-term debt ratio	–1.6878**	−2.0297**	−1.9897**	−2.8912**	−3.7611**	−3.6206**
Short-term debt ratio	−0.5147	−4.3001**	−3.0395**	0.0046	−5.5366**	−3.4404*
Sales growth	0.1804	0.2081	0.2159	0.4678	0.4353	0.4472
Profitability	1.3405**	0.1006**	0.3490	2.2982**	0.6886*	1.0548
Free cash flow	0.0508	0.9503	0.7146	0.7217	1.2731	0.7679
Dividend payout ratio	0.0121**	0.0078*	0.0087**	0.0217**	0.0165**	0.0177**
Asset turnover ratio	0.0097	0.1021	0.0645	0.0500	0.0331	0.0010
GDP growth	0.1479**	0.3169**	0.5791**	1.0084**	0.4564**	0.8322**
Market returns	0.3586	0.4499	0.4286	0.7276	0.8867	0.8420
F-value	7.4892**	9.1603**	9.0116**	7.4711**	7.4718**	8.2482**
Adj. R²	0.1967	0.4875	0.3285	0.1764	0.4300	0.2977
Durbin–Watson stat.	1.5269	1.9932	1.8692	1.6665	1.1283	1.0098
Chow (χ²) test		347.295**			325.427**
Hausman test			47.4414**			52.1832**

Source: Complied by author.

Notes: FE = fixed-effects; MBV = market-to-book value ratio; RE = random-effects; TQ = Tobin’s Q ratio. * indicates significance at 5% level; ** indicates significance at 1% level.

In case of overall firm level data, FE models can explain 54.22% and 46.36% changes in TQ and MBV respectively which are higher than other two estimates. Size-wise analysis reveals that selected variables can explain higher variations in performance for large size firms followed by medium and small companies confirming that firm size moderates the effect of same exogenous factors on the endogenous variables.

Table 7.

Regression Output of Medium Size Firms (530 Obs.)

Coeff.	Model 1 (TQ)			Model 2 (MBV)
Coeff.	Pooled	FE Models	RE Models	Pooled	FE Models	RE Models
Inter.	1.7501**	1.9860**	1.9299**	6.5431**	7.6657**	6.8309**
Long-term debt ratio	−0.0872	−0.1803	−0.1709	−2.1291	−1.3283	−1.9182
Short-term debt ratio	−0.9339*	−0.7671	−0.7625	−4.3196*	−8.6790**	−5.4553*
Sales growth	0.1544	0.1083	0.1055	2.1450**	2.3342**	2.2063**
Profitability	0.7227**	0.2393*	0.2953	10.0810**	3.5403**	1.5284**
Free cash flow	1.4192**	0.5147	0.6244	8.1489**	5.2244*	7.0378**
Dividend payout ratio	0.0589	0.0271	0.0309	0.2203	0.1677	0.2009
Asset turnover ratio	0.2701**	0.2012	0.2394*	2.4511**	2.7037**	2.6753**
GDP growth	1.7699**	1.4569**	1.4196**	1.0571**	1.8042**	1.2522**
Market returns	0.0144	0.0393	0.0326	0.3141	0.4790	0.3564
F-value	10.8621**	13.8430**	9.2557**	35.4564**	7.9856**	39.6166**
Adj. R²	0.2437	0.5969	0.3232	0.3696	0.4461	0.3965
Durbin–Watson stat.	1.3584	1.6596	1.5995	1.9504	2.2585	2.0884
Chow (χ²) test		352.009**			358.573**
Hausman test			41.1261**			57.1313**

Source: Complied by author.

Notes: FE = fixed-effects; MBV = market-to-book value ratio; RE = random-effects; TQ = Tobin’s Q ratio. * indicates significance at 5% level; ** indicates significance at 1% level.

Table 8.

Regression Output of Large Size Firms (580 Obs.)

Coeff.	Model 1 (TQ)			Model 2 (MBV)
Coeff.	Pooled	FE Models	RE Models	Pooled	FE Models	RE Models
Inter.	1.9105**	1.7984**	1.9768**	3.3482**	2.2312**	2.8815**
Long-term debt ratio	−0.6921**	−0.2924	−0.3608	−0.1562	−1.2741*	1.0378
Short-term debt ratio	−1.5362**	−0.8123**	−1.1495**	−2.1093**	−0.3253	−1.1232
Sales growth	0.0560	−0.0221	−0.0016	0.1220	0.0563	0.0043
Profitability	1.3098**	0.4978*	0.6296**	3.0027**	1.2534**	1.5841**
Free cash flow	1.1575*	0.3731	0.1816	1.5982	0.0052	0.3259
Dividend payout ratio	0.0439	0.0281	0.0232	0.1603	0.0295	0.0200
Asset turnover ratio	0.1301*	0.3320*	0.0727	0.0440	1.6439**	0.7633**
GDP growth	1.1828**	1.7543**	0.8036**	2.7673**	2.6667**	1.1204**
Market returns	0.2677	0.2454	0.2553	0.5868	0.4844	0.5182
F- value	10.6744**	16.6963**	5.9272**	8.5076**	16.2319**	9.3047**
Adj. R²	0.3307	0.6415	0.4711	0.4045	0.6345	0.4943
Durbin–Watson stat.	1.4040	1.8938	1.7791	1.3880	1.8669	1.7648
Chow (χ²) test		582.278**			596.391**
Hausman test			22.1860**			56.7706**

Source: Complied by author.

Notes: FE = fixed-effects; MBV = market-to-book value ratio; RE = random-effects; TQ = Tobin’s Q ratio. * indicates significance at 5% level; ** indicates significance at 1% level.

Discussion of Results and Implications

Results of panel data regression convey negative impact of debt financing on firm value implying that investors consider debt financing as negative signal and reflects the same in lower market-based performance. Findings of the study confirm the empirical results from Zeitun and Tian (2007), Putri and Rahyuda (2020) and Appaiah et al. (2020) whereas contradict the outcome of Chadha and Sharma (2015) and Budiharjo (2020) as well as the proposition of signalling theory (Ross, 1977). Though negative influence of debt on firm performance has been observed commonly, the significance of the effect has varied greatly among different size of firms. This signifies the moderating role of firm size on the relationship between firm value and debt ratio. As compared to medium and large firms, market value of small size firms (measured by TQ and MBV) has shown higher magnitude of sensitivity towards both debt ratios as depicted by regression coefficients. Possible explanation of such results can be given as small firms are more exposed to risk of bankruptcy (Ebaid, 2009) hence shareholders demand more risk premium that increases cost of equity which in turn reduces market price of shares. In case of medium size firms, only short-term debt has significant negative effect on MBV whereas for large size firm’s long-term debt was found to be significant regressors of MBV. Further, selected control variables have reported varying effects on dependent variables according to firm size. For instance, dividend pay-out is significant only in case of small size firms whereas asset turnover ratio reports significant positive influence in case of medium and large size firms only.

Considering the importance of research in this domain, the findings of the study are valuable for academicians, scholars and practitioners. The study contributes to existing body of knowledge by examining the relationship between debt financing and market value of capital goods sector companies in emerging market like India. Further, it enhances understanding of this relationship by probing the impact of firm size on it. The study proposes and tests the conceptual model linking these independent, depedent and moderating variables. This study’s findings will assist practitioners to design their CS after considering its impact on market value moderated by firm-size.

Conclusion and Research Extensions

The choice of CS has been treated as one of the most crucial areas of corporate decision making (Pandey & Sahu, 2019) and it has been immensely researched by the academicians as well. As debt financing has its own benefits and costs, finance managers need to trade-off between them. This study examines the effect of debt financing on market value of Indian capital goods sector companies and incorporates moderating effect of firm size measured by its asset value. Ordinary as well as PLS regression analysis has been applied on data of 164 listed companies collected for period of 10 years (2010–2019). Based on regression output, the study concludes that long-term as well as short-term borrowings have strong negative impact on market value of selected companies. Further, the findings confirm the moderating role of size as firms with varying size have reported dynamic intensity of adverse effect. Regression weights show that market value of small size firms is more adversely affected by debt ratios than that of medium and large firms. Besides, determinants like profitability and GDP growth are found to be significant irrespective of firm size whereas factors such as efficiency and dividend payout have reported size-wise differences.

Though this study attempts to provide comprehensive and conclusive evidence on debt financing and market value of firm, several areas are identified that require further exploration. The study can be replicated by taking different industry and performance indicator such as P/E ratio and market-value added. Apart from accounting and macroeconomic variables, governance factors such as managerial ownership, institutional ownership, audit practices and disclosure policies also affect market performance inclusion of which provide holistic results.

Footnotes

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author received no financial support for the research, authorship and/or publication of this article.

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