The Relationship Between Capital Structure and Firm Performance: New Evidence from Pakistan

Abstract

The necessity for a theoretical explanation of the negative association between capital structure and company performance is identified in this study. By focusing on accounting metrics of business performance, this study is the first to investigate the moderating effects of firm size between these variables using logical reasoning. Due to the possibility of endogeneity, this study applies a two-step system GMM approach with data from 285 non-financial enterprises from PSX over a 21-year period. For robustness, we employed pooled OLS, fixed effect, and two-step difference GMM. Our data show that leverage has a detrimental impact on business performance, with size acting as a moderator in the same direction. Our analysis empirically supports some studies while refuting others due to inconsistent results in the literature, but no study has theoretically justified their negative link. We believe that because larger companies have more and easier access to capital markets, they focus primarily on the amount of return, even if the investment is inefficient in terms of the rate of return, but small businesses do not. As a result of this thinking, firm managers' performance suffers as a result of leverage.

1. Introduction

Firm value is dependent upon the decisions of its past and future investments. These investments are to be financed by debt, equity, or a mix of both. Research has shown that firm performance is affected by financing decisions, which makes it one of the main areas of concern for management. It can, therefore, be argued that capital structure is the main strategic concern that has ever been central in Corporate Finance. The most widely accepted corporate objective function is the maximization of firm value. As firm value is dependent upon financing decisions, capital structure plays a significant role in a firm’s success (Kumar et al., 2017).

Discussion on the relationship between capital structure decision and firm performance started with the seminal article of Modigliani and Miller. They posit that the decision of choice of capital structure is irrelevant to firm performance (Modigliani & Miller, 1958). This theory is based on the assumption of a perfect capital market that does not exist in the practical world. Later on, further theories were proposed to account for imperfect markets of which the most widely accepted is the trade-off theory (Myers, 1984). It is to be noticed that there is no such theory that can completely explain the relationship between capital structure and firm performance (Le & Phan, 2017). The real world is complex and diversified whereas all these theories are based on many assumptions. Theorists are not aware of these complexities and the diversification of the capital markets. One such problem is that researchers use a mix of various measures for firm performance even though they are unrelated (Venkatraman & Ramanujam, 1986). Therefore, there is a need to study these measures separately and develop different theories to explain these measures (Gentry & Shen, 2010).

Numerous studies in the literature discuss the relationship between capital structure and firm performance. Their empirical evidence shows mixed results. Contrary to the theoretical explanations for their positive relationship (Jensen & Meckling, 1976; Modigliani & Miller, 1963; Myers, 1984; Myers & Majluf, 1984), no theory discuss their negative relationship despite its empirical evidence. These studies may be improved by studying the measures of firm performance separately (Abdullah, 2016; Gentry & Shen, 2010; Venkatraman & Ramanujam, 1986) and by studying the moderating and/or mediating roles of other variables. This study is one such attempt to incorporate both of them. We use firm size as a moderator of the relationship between capital structure and firm performance based on the following discussion. NPV is considered to be the best capital budgeting technique (Bierman & Smidt, 2012; Damodaran, 1999; Koller et al., 2000). Despite its wide acceptance, some researchers have critically evaluated NPV and have identified several flaws in it (Berkovitch & Israel, 2004). One of the major flaws is that NPV is biased towards bigger-sized projects as it focuses on absolute return instead of the rate of return. The other reason to select the firm size as a moderator is that big firms have more and easy access to debt as compared to small firms. The capital market acts as a “spare tire” for big firms even in times of financial crisis. These arguments lead to the discussion that firm size moderates the relationship between capital structure and firm performance.

This research adds to the literature in four ways. To begin with, most capital structure theories explain its positive impact on firm performance (Jensen & Meckling, 1976; Modigliani & Miller, 1963; Myers, 1984; Myers & Majluf, 1984), but none of these theories explain its negative impact on firm performance (Jensen & Meckling, 1976; Modigliani & Miller, 1963; Myers, 1984; Myers & Majluf, 1984). This study suggests that a new theoretical notion is needed to address the negative association between capital structure and company performance, both empirically and intellectually. Second, most studies have employed a combination of diverse variables as proxies for business performance in the literature. Market and book measures are used to categorize firm performance in this study. Because these metrics may not be connected and may represent various dimensions, we explain them independently (Abdullah, 2016; Gentry & Shen, 2010). Third, research reveals that the relationship between capital structure and company performance is bidirectional, implying that they influence one another. To deal with the problem of endogeneity, this study used the generalized method of moments (GMM). Finally, we discovered only one study that employed company size as a moderator in this association, but they did not present any logical justification for doing so. To our knowledge, this is the first study to employ logical reasoning to use size as a moderator for studying the realationship between capital structure and business performance.

2. Literature Review and Hypotheses

The first part of this section describes the major theories of capital structure as cited by many scholars. Apart from the MM theorem, almost all other theories support that leverage brings a positive change in firm value. The second part of this section describes the empirical findings of various researchers on the studies of capital structure and firm performance.

2.1. Major Theories of Capital Structure

MM theorem: It is regarded as the foundation for capital structure theories. This theory asserts that the choice of capital structure does not have any impact on firm performance. Firm value is determined by its assets and not by the decision of financing those assets. Basic assumptions of this theory for a perfect capital market include (a) no taxes, (b) no transaction costs, (c) no bankruptcy, (d) information is timely and equally available to all investors, (d) value maximization is the objective function of every corporation, (e) lending and borrowing rates are equal, and (f) all firms operating at the same level have equal risk (Modigliani & Miller, 1958).

MM alternative proposition: Modigliani and Miller revised their first proposition that debt provides tax-shield to the firms thus improving their value (Modigliani & Miller, 1963).

Trade-off theory: A firm chooses an optimal capital structure where it trades off between the costs and benefits of debt, and financial distress thus maximizing firm value (Myers, 1984). This theory posits that after deducting financial distress costs, the value of a firm with debt is equal to the value of a firm without debt plus tax shield. Firms that adopt trade-off theory tend to set their capital structure that is different for every firm depending on their characteristics.

Agency theory: According to this theory, there are two kinds of agency costs. One is the agency cost of equity and the other is the agency cost of debt. Agency cost of equity is a conflict that exists between managers and shareholders of a firm. Agency cost of debt is a conflict that exists between debtholders and shareholders. This theory asserts that managers of firms with high debt are under pressure to create enough cashflows to pay interest by investing in profitable projects. This mechanism leads to an increase in firm value (Jensen & Meckling, 1976). This theory shows consistent results with the trade-off theory since it is based on previous theories.

Signaling theory: Mangers use inside information to send signals to the market by the choice of their capital structure. Managers usually issue equity if they believe that their firm is overvalued, otherwise they usually issue debt if they believe that their firm is undervalued. Issuing debt covenants by a firm is a positive sign for the market that the firm is confident about future earnings. Issuing debt covenants binds a firm to pay the cost of debt. Failure to pay interest may lead to bankruptcy. This is a signal for the market that the firm can create many cashflows to pay its cost (Ross, 1977).

Pecking order theory: Firms follow a specific order to finance their projects. Internal financing i.e., retained earnings is preferred over external financing but in external financing, debt financing is preferred to equity financing. A firm issue shares only when it cannot borrow more debt at a lower cost than equity financing. Debt financing raises market perception thus increasing a firm’s value (Myers & Majluf, 1984).

Market timing theory: A firm’s capital structure is a reflection of its past decisions. The choice of financing its projects is dependent upon the cost of debt and equity at the time of financing. If a company chooses debt financing rather than equity financing, it is because the cost of debt is lower than the cost of equity at the time of finance. This, in turn, leads to a rise in the value of the firm (Baker & Wurgler, 2002).

2.2. Empirical Research

There is abundant literature available on the relationship between capital structure and firm performance. Contrary to the theoretical studies in this area where most studies suggest a positive relationship between them, empirical studies show mixed results about their relationship. However, it is still debatable whether there is a positive relationship between leverage and firm performance or a negative relationship between them due to these mixed results.

2.2.1. Capital Structure and Firm Performance

The empirical literature on the relationship between leverage and company performance has conflicting outcomes. Several studies have found a link between leverage and company performance (Nguyen et al., 2021; Pham, 2020). Leverage has a beneficial impact on return on investment (ROI) (Al-Ajmi, 2009; Detthamrong et al., 2017). Tobin’s Q is positively influenced by short-term, long-term, and total debt ratios. Apart from the research that shows a positive relationship between leverage and company performance, some studies show a negative relationship (Nguyen & Nguyen, 2020; Nguyen et al., 2021; Rajan & Zingales, 1995; Titman & Wessels, 1988). When measured by total debt, long term debt, and short-term debt, book and market measures of leverage are inversely associated with company performance when measured by return on assets, return on equity, and Tobin’s Q (Le & Phan, 2017). The ratios of short-term debt, long-term debt, and overall debt all have a negative impact on ROA and ROE. Return on equity is impacted by financial leverage. Return on equity is inversely proportional to debt to equity. Several studies, in contrast to this, find no significant relationship between capital structure and firm performance.

Those studies that have mostly used market measures of firm performance, such as price to earnings ratio, return on share price, and Tobin’s Q (Al-Ajmi, 2009; Detthamrong et al., 2017), have found a positive relationship between capital structure and firm performance (Al-Ajmi, 2009; Detthamrong et al., 2017). Those research that has largely employed conventional measures of company success, including as return on assets, return on equity, and return on sales, have revealed a negative link between these variables (Le & Phan, 2017; Rajan & Zingales, 1995; Titman & Wessels, 1988). Capital structure does, without a doubt, influence corporate performance. The number of research that discovered a negative relationship greatly outnumber those that discovered a favorable relationship. The following theories are created based on the above discussion.

H1: Capital structure has a significant impact on firm performance.

H1a: Leverage has a significant negative impact on book measures of firm performance.

H1b: Leverage has a significant positive impact on market measures of firm performance.

2.2.2. Capital Structure and Firm Performance with the Moderating Roles of Size

Firm size plays a significant role in securing loans. The higher the firm size, the easier it is to secure a loan, and vice versa. During the financial crisis of 2008, deleveraging occurred in small firms as compared to weaker evidence of the same for large firms. The capital market acts as a “spare tire” for large firms in such cases. Consequently, we argue that firm size moderates the relationship between capital structure and firm performance. Another argument for using size as a moderator is developed from the capital budgeting technique, net present value. NPV is the most generally used and by far the best technique of capital budgeting for project evaluation as discussed in various books of Corporate Finance (Bierman & Smidt, 2012; Al-Ajmi, 2009; Brealey et al., 2012; Damodaran, 1999; Koller et al., 2000). Despite the widely praised technique, NPV has been criticized for its practical use where managers practically prefer the use of IRR over NPV. There are two flaws in the NPV method of evaluating projects, i.e., (a) the basic assumption of NPV is that every project is to be financed by debt, and (b) NPV is biased towards big-sized projects even if they are undervalued according to IRR. These flaws, when linked together, infer that leverage negatively impacts financial performance whereas size moderates this relationship. Based on these arguments we develop the following hypothesis.

H2: Firm size negatively moderates the relationship between capital structure and book measures of firm performance.

3. Research Methods

3.1. Sample and Data Collection

This study relies on secondary data mostly from the State Bank of Pakistan’s Statistics & DWH Department’s “Financial Statements Analysis for Companies (Non- Financial) listed at Pakistan Stock Exchange” (PSX) documents. Some of the information was gathered from the Pakistan Stock Exchange and the websites of the companies concerned. 62 of the 363 enterprises listed on the PSX at the start of 2019 were deemed defaulters at the end of the year, while data for 16 were unavailable. The final sample includes 285 non-financial companies that were listed on the PSX between 1999 and 2019. To deal with outliers that may cause findings to be skewed, data is winsorized at the 5^th and 95^th percentiles.

3.2. Construction of Variables

3.2.1. Dependent Variable

Firm performance is a multi-dimensional construct that has been measured differently by various authors. Gentry and Shen (2010) have classified the measures of firm financial performance into book measures and market measures. Using the same classification, this study uses return on assets (ROA), return on equity (ROE), and return on sales (ROS) as book measures, whereas, the price-to-earnings ratio (PE), return on share price (ROSP), and market to book value of equity (MBV) as market measures.

\(\text{ROA}_{i,t} = \frac {\text {Net income}_{i,t}} {\text {Total assets}_{i,t}}\)       (1)

\(\text {ROE}_{i,t} = \frac {\text {Net income}_{i,t}} {\text {Total shareholders' equity}_{i,t}}\)       (2)

\(\text{ROS}_{i,t} = \frac {\text {Net income}_{i,t}} {\text {Total net sales}_{i,t}}\)       (3)

\(\text {PE}_{i,t} = \frac {\text {Share price}_{i,t}} {\text {Earnings per share}_{i,t}}\)       (4)

\(\text {ROSP}_{i,t} = \text {ln} \frac {\text {Share price}_{i,t}} {\text {Share price}_{i,t-1}}\)       (5)

\(\text {MBV}_{i,t} = \frac {\text {Market value of equity}_{i,t}} {\text {Book value of equity}_{i,t}}\)       (6)

3.2.2. Independent and Control Variables

Total debt to assets (TDA) and total debt to equity (TDE) are used as proxies of the primary explanatory variable, capital structure. This study uses size, growth, age, and tangibility as control variables.

\(\text {TDA}_{i,t} = \frac {\text {Total liabilities}_{i,t}} {\text {Total assets}_{i,t}}\)       (7)

\(\text {TDE}_{i,t} = \frac {\text {Total liabilities}_{i,t}} {\text {Total equity}_{i,t}}\)       (8)

\(\text {Size}_{i,t} = \text {ln(total assets)}\)       (9)

\(\text {Growth}_{i,t} = \frac {\text {Total assets}_{i,t}} {\text {Total assets}_{i,t-1}}\)       (10)

\(\text {Age}_{i,t} = \text {Difference between observation} \\ \qquad \qquad \text { year and establishment year}\)       (11)

\(\text {Tangibility}_{i,t} = \frac {\text {Net fixed assets}_{i,t}} {\text {Total assets}_{i,t}}\)       (12)

3.2.3. Moderating Variable

Firm size plays a significant role in securing loans. The higher the firm size, the easier it is to secure a loan, and vice versa. NPV is the most generally used and by far the best technique of capital budgeting for project evaluation as discussed in various books of Corporate Finance (Bierman & Smidt, 2012; Brealey et al., 2012; Damodaran, 1999; Koller et al., 2000). Despite the widely praised technique, NPV has been criticized for its practical use where managers practically prefer the use of IRR over NPV. There are two flaws in the NPV method of evaluating projects. One of the flaws is that NPV is biased towards big-sized projects even if they are undervalued according to IRR. These arguments infer that leverage negatively impacts financial performance whereas size moderates this relationship. Based on these arguments, this study uses size (as given in equation 9) as a moderating variable.

3.3. Models

This study uses the following equations for the linear estimation of the dependent variable, where, FP_{i, t} is firm performance, CS_{i, t} is capital structure, and X_{i, t} is a vector of control variables (size, growth, age, and tangibility). Equation (13) is used to check the direct impact of capital structure on firm performance. Equation (14) shows the moderating role of size between CS and FP.

\(\text {FP}_{i,t} = \alpha + \beta \text {CS}_{i,t} + \gamma \text {X}_{i,t} + \varepsilon_{i,t}\)       (13)

\(\text {FP}_{i,t} = \alpha + \beta \text {CS}_{i,t} + \delta \text {CS}_{i,t} \times \text {Size}_{i,t} + \gamma \text{X}_{i,t} + \varepsilon_{i,t}\)       (14)

3.4. Estimation Technique

The most straightforward method of the estimate is to ignore the problem of heterogeneity and use the OLS estimation methodology on pooled data, i.e., setting α_i = α for all i. This method frequently yields an upward biassed coefficient estimate for the dynamic term. To deal with this difficulty, which transforms that variable, a fixed effect estimation technique is usually used. The transformed variable, on the other hand, still has a relationship with the transformed error. As a result of this modification, the estimate is skewed downwards. The instrumental variables technique is used to solve these bias issues.

Difference GMM (generalized method of moments) rectifies endogeneity issues by (a) transforming all regressors through differences, and (b) removing fixed effects in the process (Arellano & Bond, 1991). The problem with difference GMM is that it enlarges the gap in an unbalanced panel by subtracting the previous observations from the contemporaneous one. As a result, using difference GMM on an unbalanced panel may cause the results to be skewed. System GMM, on the other hand, addresses endogeneity issues by (a) increasing efficiency by adding more instruments and (b) changing the instruments to make them uncorrelated (exogenous) with the fixed effects. It creates a system of two equations, one for the original equation and another for the modified equation. System GMM, unlike difference GMM, subtracts the average of all future observations from the current one. As a result, it may be computed for all observations, independent of data gaps. As a result, data loss is minimized (Arellano & Bover, 1995; Blundell & Bond, 1998). The xtabond2 command in Stata was used to obtain the results.

The Hansen statistic is used to determine the validity of instruments. Failure to reject the null hypothesis at the 0.05 confidence interval supports the validity of the instruments, however, failure to reject the null hypothesis at greater confidence intervals, notably above 0.25, indicates that something is wrong. The error term’s serial correlation/ autocorrelation is also investigated. Failure to reject the null hypothesis at the second-order level indicates that the moment requirements are appropriately provided and that the error term is serially uncorrelated.

4. Results and Discussion

The descriptive statistics of all variables is summarized in Table 1. Variation is the highest for Age and MBVE, whereas, it is lowest for ROA and ROS. Although data is winsorized, yet TDA has the maximum value over 1, whereas, TDE has a minimum value below 0 as some firms have negative equity due to accumulated losses.

Table 1: Descriptive Statistics

The correlation matrix in Table 2 shows the correlation coefficients between independent and control variables. It shows that these variables are not highly correlated and they can be used together.

Table 2: Correlation Matrix

4.1. Estimation Results

Pooled OLS, fixed effects, two-step difference GMM, and two-step system GMM approaches are used to estimate the data. The findings of the first three estimate strategies are presented in the appendices, whereas Table 3 and Table 4 present the results of the two-step system GMM estimation technique.

Table 3: Two-Step System GMM

Robust standard errors are in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.

Table 4: Two-Step System GMM with Interaction Term

Robust standard errors are in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.

The findings of pooled OLS for accounting measures and MBVE are presented in Tables A1a and A1b, respectively. The results of fixed effects for accounting measures and MBVE are presented in Tables A2a and A2b, respectively. The findings of the two-step difference GMM are presented in Table A3. Lever has a negative impact on accounting metrics of firm performance, according to all estimation techniques presented. The association between debt to equity and MBVE is strong, whereas the relationship between debt to assets and MBVE is small. Because the main topic of this paper is accounting metrics of firm performance, MBVE is not examined further. The firm performance will now refer to accounting measurements of firm performance, such as ROA, ROE, and ROS.

Table 3 shows the results of a two-step GMM estimation without moderation for leverage and firm performance. The findings reveal that both TDA and TDE, or leverage measures, have a considerable negative influence on ROA, ROE, and ROS. Furthermore, size and growth have beneficial benefits on business performance, whereas tangibility has a negative impact. The effects of age on ROA and ROE are negative, whereas the effect of age on ROS is insignificant.

The findings of two-step GMM estimation for leverage and firm performance with size as a moderating variable are presented in Table 4. The findings reveal that while debt to assets has a negligible influence on ROA and ROE, it has a positive impact on ROS. The debt-to-equity ratio has little effect on a company’s performance. The findings also reveal that when debt to assets is factored in, size has a negative impact on ROA and ROS, and when debt to equity is factored in, size has a negative impact on all metrics of firm performance.

As evidenced by the negligible value of Arellano Bond’s autocorrelation test, both Table 3 and Table 4 data are free of the autocorrelation problem. At the 0.05 significance interval, the Hansen statistic similarly fails to reject the null hypothesis, implying that the instruments are valid. The findings also suggest that instrument proliferation is not a concern because the number of instruments is less than the number of groups.

4.2. Discussion

Empirical results of our study show that leverage negatively affects firm performance when measured through ROA, ROE, and ROS. The results also show that size moderates this relationship in the same direction. These results are in contrast with the existing theories of capital structure, especially the Trade-off Theory, yet they are consistent with the empirical results of other studies (Le & Phan, 2017; Rajan & Zingales, 1995; Titman & Wessels, 1988).

Firm performance is a multi-dimensional entity with numerous characteristics, but the most commonly researched component is financial performance, so this essay will concentrate on that aspect. Even business financial performance, according to previous research, is not a one-dimensional phenomenon. Although both its market and book measures are widely regarded gauges of financial performance, they are only tangentially related. As a result, they should be examined independently and theories developed specifically for them (Gentry & Shen, 2010).

Modigliani’s seminal work and Miller’s Irrelevance Theorem kicked off the subject of capital structure and business performance (Modigliani & Miller, 1958). The choice of capital structure is unrelated to firm performance, according to this theorem, but they later changed their study, stating that the choice of capital structure affects firm performance due to tax advantages on debt (Modigliani & Miller, 1963). Later hypotheses to explain the relationship between capital structure and company performance were created. Among these ideas, the Trade-off Theory is the most widely recognized and mainstream perspective on capital structure. This theory proposes using a combination of debt and equity in an optimal structure to maximize a firm’s performance.

There is abundant literature available on the relationship between capital structure and firm financial performance, yet, none of the theories explain their negative relationship despite the empirical evidence reported in various studies as summarized in previous studies (Abdullah & Tursoy, 2021; Kumar et al., 2017). Before building a theoretical argument, it is to be made clear that, in our opinion, book measures are to be preferred over market measures of firm performance due to two major reasons: (a) market measures are based on future expectations rather than previous performance, whereby, expectations may be formulated incorrectly, and (b) market measures are theoretically strong but their empirical performance is inferior to accounting measures, especially in terms of predictability (Abdullah, 2016).

When compared to small businesses, big businesses have more and easier access to financing. Even in times of financial crisis, the stock market serves as a “spare tire” for large corporations. Because of their easy access to more capital, big-company executives adopt behavior and mindset that prioritizes absolute returns over the rate of return. The investment is chosen if the overall absolute amount of return is large, even if the return is low in terms of the percentage of available money. As a result of the limited access to financial markets and indebtedness, it can be stated that small business managers’ mindset is to enhance the rate of return. However, the mindset of the managers changes as we move up the size of the firm due to easy access to capital markets and debts where their target is to increase the absolute amount of return even if the investment is less efficient in terms of rate of return.

The number of studies that found a negative relationship between leverage and firm performance is far greater than the number of studies that either found a positive relationship or an insignificant relationship. Our study is merely a contribution to the literature and an idea to the researchers that there is a need for a theoretical proposition for the negative effect of leverage on firm performance.

5. Conclusion

In the actual world, where markets are flawed, capital structure has a clear impact on corporate performance. However, whether this influence is positive or negative is still up for debate. The direct relationship between capital structure and firm performance has been studied extensively. Some of these studies have found a positive association between these variables, while others have found a negative relationship (Abdullah & Tursoy, 2021; Kumar et al., 2017). Because of these conflicting results, deciding whether to finance investments with debt or equity remains a mystery (Kumar et al., 2017). These varied results are due to two factors: (a) firm performance may be measured broadly using accounting and market measurements. These variables are unrelated and should be researched individually, with different hypotheses created (Gentry & Shen, 2010); and (b) firm size moderates the link between capital structure and firm performance, since large firms have greater and easier access to debt than their smaller counterparts.

As the number of studies that support a negative relationship between leverage and firm performance exceeds the number of studies that support a positive or an insignificant relationship, this study is merely an idea for researchers to put forward a theory that can explain the negative aspect of their relationship. Using data from PSX, for 21 years, this study uses the GMM estimation technique by taking firm size as a moderator to the relationship between capital structure and firm performance. Due to the mixed results in the literature, our results are in line with some studies whereas, they are in contrast with other studies but that is mainly because previous studies have used a mix of accounting measures and market measures of firm performance.

Our study reports the negative impact of capital structure and firm performance whereby firm size moderates this relationship in the same direction. Previously, none of the capital structure theories has explained the negative impact of leverage on firm performance. Our study proposes that this negative relationship is due to the change in the mindset of firm managers due to a change in size. As bigger firms have more and easy access to debt than small firms, their managers focus on increasing the absolute amount of return even if the rate of return is lower. Contrarily, managers of small firms try to maximize the rate of return instead of the absolute amount of return due to limited access to capital markets.

This study has certain limitations that can be addressed by studies in the future. Our study focuses mainly on the moderating roles of firm size between capital structure and accounting measures of firm financial performance. Future studies can check the same roles of firm size between capital structure and market measures of firm financial performance. Future studies may apply decile methodology on the proxy of firm size as the moderator. This might further enhance the results of the study.

Appendix