The Impact of Leverages and Operating Efficiency on Market Value Added: Empirical Evidence from India

Abstract

This article examines the relationship between the market value added and degree financial leverage, degree of operating leverage, and asset turnover ratio of the listed firms in India. The period of study was from 2013 to 2019, and data were collected in annual frequency. Our study concludes that the degree of financial leverage and asset turnover ratio are significantly and negatively associated with the market value added of listed firms in India. We also reported an insignificant relationship between market value added and degree of operating leverage. Our results indicate that promoters holding and firm size are important factors driving the performance of the firms listed in India.

Keywords

Market Value Added (MVA)Degree Operating Leverage Degree of Financial Leverage (DFL)Asset Turnover Ratio Generalized Methods of Moments (GMM)

Introduction

A company’s profitability has always been a subject of research among academicians and practitioners in order to reveal the prominent factors that determine business success. ‘Profitability’ has been the most extensively investigated topic in the literature of finance, and to date, its relevance has not yet declined.

Profitability is a firm’s ability to gain profit during a period of time from sales, assets, and certain capital stocks. Management performance can be evaluated through profitability as management controls the overall affairs of the firm. Profit is one of the tools for survival in the current competitive and dynamic business environment. An increase in profitability will be an appropriate indication for investors, as well as increasing their conviction. Therefore, profitability facilitates the top management in raising owner’s capital for the organization at the time of need. Also, when demand for the equity share of the firm increases in the capital market, it will create an upsurge in its equilibrium price. However, profitability is considered as a short-term measure; ultimately, the aim of any firm is enhancing shareholder wealth.

The ultimate objective of any firm is shareholder’s wealth maximization. The creation of value for shareholders is the key penalty area for the top management of an enterprise. According to Kim et al. (1999), an enterprise that primarily focuses on shareholder value, generally having good financial health and performance, which ultimately leads to improvement in the overall economy. Stock return is the most straightforward way to understand the increment in shareholder wealth for listed firms, but for a sustainable stock return, the management should focus on company value in the long run. In long run, firm value as well as shareholders' wealth is important. The company must concentrate on other stakeholders’ welfare also, such as society, customers, employees, and suppliers. These stakeholders are the pillars of corporate governance, which is the directive principle of any organization. Hence, there is a need for reliable and accurate measures to evaluate the performance of a company.

There are several conventional measures to evaluate the performance of a company, such as return on capital employed (ROCE), return on sales (ROS), return on assets (ROA), and return on equity (ROE). These measures are criticized for not including the cost of capital in their calculation. Thus, this leads to the inability of accounting measures for predicting the firms’ value (Ehrbar, 1999; Stewart, 1991).

Recently, new value-based measures such as economic value added (EVA), cash value added (CAV), shareholders value added (SVA), market value added (MVA), and discounted economic profits (DEP) were introduced. Various consultancy companies developed these recent value-based measures to understand the real performance of the companies. These measures also help in shifting focus from accounting earning to cashflows for better understanding of performance. Conventional measures such as net operating profit after tax (NOPAT), earning per share (EPS), ROI, and ROE, which are used to measure the efficiency and effectiveness of the companies, have been criticized as they do not include the complete cost of capital. Therefore, corporate performance cannot be measured with the help of accounting earnings as this cannot predict the firms’ value consistently.

According to O’Hanlon and Peasnell (1998), the value-based measures, however consider cost associated with the capital, that is, debt and equity in calculation. As capital raised by the organization does not come for free, firms should consider cost of capital employed in their operations while evaluating their performance.

In 1991, EVA was first announced by Stream, Stewart, and they termed it as a measure of shareholder wealth creation. Stewart (1991) claims that EVA is most suitable for depicting shareholder wealth maximization. Many studies support EVA (Bao & Bao, 1998; O’Byrne, 1996), but some research criticized Stewart’s claim because they found weak relations between EVA and firms (Biddle et al., 1997; Ismail, 2006; Krame & Peters, 2001; de Wet, 2005). However, the evaluation process is complicated, and most of the indicators reflect only a single period of performance.

The ability of a firm to create shareholders’ wealth in its entire lifetime is reflected by MVA. A firm value is created if its corporate decision gives more benefits compared to its cost in monetary terms. Effective management also has a significant role to play in enhancing the value of the firm as it helps the firm to take more robust corporate decisions, which lead to an improvement in the firm’s performance. Nevertheless, myopic and short-term solutions for long-term problems and weak actions by management can depreciate stock prices, which can be seen in lower MVA. Market awareness regarding the forecasts about performance of the firm is also revealed by MVA. MVA reflects firm performance with the help of stock price, which is publicly available. It is not based on complex data and methodology. MVA is calculated by subtracting the book value of equity with the book value of debt from the market value of the company equity with the market value of debt. It depicts the shareholder’s wealth creation ability. Sales growth, operating profitability, level of capital, and cost of capital can be considered as core value drivers. Operational efficiency, financial efficiency, and investing competency can be reflected in MVA. Hence, MVA is a suitable measure of firm efficiency and effectiveness. The firm’s efficiency, degree of financial leverage (DFL), and degree of operating leverage (DOL) impact firm performance as well as the value of the firm (Lang et al., 1996).

MVA is better than the other traditional measures, which are used to measure firm performance in many aspects. MVA enhances decision making by reducing the agency cost, which leads to quicker decision making by the management (Lovata & Costigan, 2002); (Biddle et al., 1999). Also, compared to MVA, other measures are not strongly related to stock return, which is a true reflection of shareholders’ wealth (Maditinos & Row, 2006); (Lehn & Makhija, 1997). Ferguson et al. (2005) also conclude that stock performance is improved by MVA. MVA proves to be a suitable variable to explain stock returns of the firms (Erasmus, 2008; Kim, 2006; Palliam, 2006). Finally, authors found a correlation between MVA and EVA (Uyemura et al., 1996). The outcomes of this study help managers to understand the drivers of MVA, which can help managers to enhance their portfolio. Hence, this article attempts to understand the drivers of MVA. To the best our knowledge, we have not come across many studies that found an association between value-based profitability measures and DFL, DOL, and ATR.

This article contributes to existing literature in several ways. First, studies that conclude about drivers of MVA are scarce. Thus, our study is the first one that attempts to find the drivers of MVA. This study provides more insight about factors that affect MVA or market value of the firm. Second, there is no standard literature related to studies that found a relationship between MVA and DFL, DOL, and ATR. Hence, this article attempts to find the relationship among them. Several attempts were made to find the relationship between EVA and DOL and DFL (Alipour & Pejman, 2015). Also, several attempts were made to study how leverages and firm’s efficiency affect the market value of firms. The practical implication of this study is that it helps financial managers to understand drivers of MVA. Also, the interplay between MVA, leverage, and efficiency in firm’s value addition can be understood, which will help in making decisions regarding asset allocation. Therefore, the understanding of MVA help in overall improvement in their portfolio. The underlying agenda of this article is to provide empirical evidence that MVA is affected by efficiency, DFL, and DOL. There are several studies concerning this topic, but most of them are on developed economies. Connelly et al. (2010) studied MVA based on emerging markets, but we could not find any study that explored it in relation to the Indian context. This article adds to the body of literature on MVA in general and in the context of emerging economies (India) in particular.

We examine the relationship of MVA with DOL, DFL, and ATR. We use a sample of 562 companies listed in the Indian stock market for the period 2012–2019 in order to test the relationship between MVA and DOL, DFL and ATR. The results of the study help managers to find out which variable contributes most to MVA. A better understanding about the drivers of MVA would help stockholders improve their strategy and asset allocation in their portfolio. It also prompts organizations to build and improve their essential corporate decisions, that is, capital budgeting decisions, financing decisions, dividend decisions, and working capital decisions. This article further concludes that other non-economic variables such as customers, product quality, employer, and innovation should be given consideration to explain more variations in MVA of firms, which, to date, has been unexplored.

The rest of the article is organized as follows: the second section presents literature review, the third section includes hypothesis development, the fourth section reflects the data, variables, and methodology used, the fifth section contains empirical analysis and findings, and the sixth section reflects conclusion.

Literature Review

MVA is a value-based performance measure which approximates the shareholders’ value addition. MVA is the difference between market value of the capital and capital charge over a certain time period. According to Young and O’Byrne (2000), MVA can be calculated by deducting total capital of the firm including debt capital from the firm’s market value including debt. MVA is related to EVA as MVA is the present value of future values of EVA. It is the measure of performance. Hence, it is considered the best external indicator of addition to measure shareholder wealth. Stewart (1991) concludes that MVA reflects shareholder wealth creation.

Peterson and Peterson (1996) stated that market capitalization affects MVA, as it is one of the variables used in the calculation of MVA, which is outside the management control. Zaima et al. (2005) conclude that GDP, which is an indicator of economic condition, affects MVA. MVA is the discounted present value of EVA, and it is similar to net present value (NPV), which can be used for project analysis (Hartman, 2000). Hillman and Keim (2001) conclude a direct relationship between MVA and stakeholder’s management proxies. Hence, stakeholder management improves stockholder prosperity. On the contrary, Yook and McCabe (2001) found an inverse relationship between average stock return and per share MVA. Therefore, low MVA is an indicator of underprice resulting in an encouraging future stock return.

Firm profitability is one of the crucial determinants of MVA. Many studies found the relationship between earning and MVA (Nakhaei, 2016; Yahaya & Mahmood 2011). The market value of equity increases because of increase in share price of the firm. Therefore, MVA is also positively impacted by increase in market value of the equity, as it is one of the components of MVA. Stock price is dependent on profitability of the firm, as the share price is the present value of the cash flows received in the future. Prasad (2015) studied the relationship between some selected accounting-based performance measures and MVA in the context of Indian infrastructure companies. The authors conclude that MVA has a significant relationship between ROE, ROCE, and EPS. Yaqub et al. (2015) conclude that there is evidence of a direct and significant relationship between EVA and MVA for the study period, and that stock returns are corelated with these measures.

Profitability, which is an important determinant of MVA, is impacted by many factors that may be controllable or uncontrollable with respect to firm decision making. Controllable factors should be given emphasis, as they can be controlled by the firm’s decision making. Leverage is an important factor that impacts a firm’s profitability significantly. There are many studies that provide evidence that financial as well as operating leverage impact profitability. Leverage can be a proxy of risk which independent of the impact of return—also, leverage measure business risks as it causes financial risks. Roden and Lewellen (1995) in their study suggests a direct relationship between Profitability & leverage. According to Malik (2011), leverage and profitability are negatively correlated.

Operating leverage impacts profit positively, which implies that sales, fixed costs, and quasi fixed are increasing, but increase in sales is more rapid. Hence, profitability will be higher due to higher operating leverage (Chen et al., 2019). DOL provides a good description of the level to which a firm depends on fixed cost to maximize its profit (Watson & Head, 2010). An upsurge in profit is a outcome of monitoring the fixed cost of the firm, such that total sales proceeds covers a higher margin than fixed costs. Zubairi (2012) concluded operating leverage makes organizations unpredictable because if the operating revenue is not sufficiently high to cover all fixed costs, then there might be operational loss or low operational profit. Therefore, in a time of rising sales or inflow of revenue, high degree operating leverage will boost financial performance and vice versa.

ATR is a measure that assists in measuring firm efficiency in generating sales proceeds from its assets. It is the relative efficiency of a business organization for generating revenue through its assets. It is useful in generating higher sales. The amount of asset turnover depicts the firm’s asset exploitation and reflects the firm operating efficiency, as part of its product strategy. Profitability is also impacted by ATR, as this is an essential variable that affects a firm’s profitability. Feltham and Ohlson (1995) found a direct impact of financial statement investigation in the valuation of the organization. Dupont analysis delivers a way to split return on net operating assets (RONA), a performance measure into ATR and PM to found some underlying drivers of operating profitability. Jensen et al. (2012) concluded that PM and ATR are inversely related when companies are involved in earing management activities. This study concludes that due to the articulation of financial statements, upward earning management should cause an upsurge in PM and a similar decline in ATR and vice-versa.

A vast literature already exists, which provides adequate evidence to understand the relationship between leverage (financial efficiency), operational efficiency, and firms’ profitability. However, we hardly find any study that examines the relationship between leverage (financial efficiency), operational efficiency, and MVA of Indian firms. India is an emerging market where firms have to focus on the value addition objective. Hence, this study attempts to provide the solutions to the subsequent research questions.

Does DFL have a relationship with MVA of Indian listed firms?

Does DOL have a relationship with MVA of Indian listed firms?

Does ATR have a relationship with MVA of Indian listed firms?

Hypothesis Development

In earlier literature, mixed results were found on the relationship between DFL, DOL, and the firm’s profitability. Some researchers found a direct relationship between DFL and profitability (Chandrakumarmangalam & Govindasamy, 2010; Kumar, 2014; Mseddi & Abid, 2010; Murugesu & Subramaniam, 2016), while some others observed a negative relationship (Ahmad et al., 2015; Gatsi et al., 2013; Kaur & Kaur, 2015; Khodaei Valahzaghard & Taherinejhad, 2012). Few other researchers found no linear relationship between the above two variables (Asraf & Desda, 2020; Sen, 2018).

Some researchers found a positive relationship between DOL and profitability (Chandrakumarmangalam & Govindasamy, 2010; Chen et al., 2019; Kumar, 2014; Mseddi & Abid, 2010; Murugesu & Subramaniam, 2016), while some others evidenced a negative relationship; Baker (1973), Selling and Stickney (1989). Also, many studies conclude that there is no significant relationship between DOL and profitability (Asraf & Desda, 2020; Sen, 2018).

From the existing literature, we could not find studies that found a relationship between value-based profitability measures and DFL, DOL, and ATR. In line with the above-mentioned discussion, this article examines the relationship between MVA and DFL, DOL, and ATR. Therefore, we propose our first two hypothesis as follows:

H1: There is a significant relationship between degree of financial leverage and the market value added in Indian firms.

H2: There is a significant relationship between degree of operating leverage and the market value added in Indian firms.

The individual components of performance also have a linkage with the firm’s profitability. The earlier strands of literature found enough evidence that there is a significant relationship between the ATR and profitability. Some researchers reported a significant positive relation between ATR and profitability (Popa & Ciobanu, 2014; Szymanska, 2015), whereas some researchers also support the existence of a negative and significant association between profitability and ATR (Reed & Reed, 1989; Selling & Stickney, 1989). In addition, some researchers found no significant relationship between ATR and MVA (Warrad & Al Omari, 2015), although we could not find evidence of studies concerning the association between ATR and MVA of firms. Hence, to study the relationship between ATR and MVA of the firms, we propose our last hypothesis as follows:

H3: There is a significant relationship between asset turnover ratio and market value added in Indian firms.

Data and Methodology

Sample Selection

In this article, we examine the relationship between MVA, financial leverage, DOL, and ATR for a sample drawn from Bombay Stock Exchange (BSE) listed firms. In this study, we used the Centre for Monitoring Indian Economy’s (CMIE) Prowess database for gathering data on dependent, independent, and control variables for the sample firms drawn from BSE listed companies during 7 financial years from 2013 to 2019. Moreover, data regarding the weighted cost of capital (WACC) were obtained from the Bloomberg database.

During finalizing our sample, we removed the following companies.

Firms engaged in banking, insurance, and other finance-related business, as they not only come under the jurisdiction of the Indian Companies Act, 2013 but are also governed by other laws. Their financial statements disclosure is different from non-financial companies, which makes comparison difficult (Aktas et al., 2015).

To make a balanced panel, we also excluded all those companies whose data are missing or not reported regularly over the sample period.

After using the above filters, we were left with 562 companies whose data were available throughout the study period. Hence, we have arrived at 3934 firm-year observations.

Dependent Variable

The objective of this article is to study the relationship between MVA and leverage and operational efficiency of the firm. Although MVA is an absolute proxy and associated with the size of the firm, it shows that the larger the size of the firm, the higher the MVA. Hence, we try to normalize the value of MVA by dividing the value of MVA by the net sales [MVA_i,t].

Market value added: (market value of debt + market value of equity) – (book value of debt + book value of equity)

Independent Variables

As regards the hypothesis discussed in the hypothesis development section, there are three independent variables that are introduced as follows:

Degree of Financial Leverage:

DFL is a measure that shows sensitivity of a net income of the firm with respect to change in the company’s operating income (DFL_i,t).

DFL = PBIT/PBT

Degree of operating leverage: DOL is a measure that shows the sensitivity of operating income of the firm with respect to change in the company’s sales (DOL_i,t).

DOL = Change in PBIT/Change in net sales

Assets turnover ratio: ATR is an efficiency ratio that shows a firm’s capability to yield revenue from its assets by comparing net revenue with mean of the total asset (ATR_i,t).

ATR = Net sales/Total assets

Control Variables

According to the hypothesis proposed in the hypothesis development section, the following are the control variables whose effect is controlled for study:

Size: natural log of total assets of the company (LN_TA)

Sales Growth: growth of sales from last year to the current year (SGR). It is an incremental change in net sales noticed by the firm on a yearly basis.

Debt Equity: total debt/total equity of the company (DE). It is a financial ratio that reflects the proportion of total long-term debt capital and equity capital in the capital structure.

Promoters Shareholding: total promoters’ equity capital/total equity capital of the company (PH).

Non-promoters Shareholding: total non-promoters’ equity/total equity of the company (NPH).

Age: natural logarithm of the age of the company.

Empirical Research Method

Our research model for testing the hypothesis discussed in the third section is designed with MVA as the dependent variable. We have used three independent variables, that is, DOL, DFL, ATR. Also, we have used some control variables such as LN_TA, SGR, DE, NPH, and PH. The empirical regression model is given as follows:

\begin{matrix} M V A_{i, t} = α + β_{1} M V A_{i, t - 1} + β_{2} D F L_{i, t} + \\ \sum_{k = 1}^{6} δ_{k} C o n t r o l_{k, i, t} + ε_{i, t} \end{matrix}

\begin{matrix} M V A_{i, t} = α + β_{1} M V A_{i, t - 1} + β_{2} D O L_{i, t} + \\ \sum_{k = 1}^{6} δ_{k} C o n t r o l_{k, i, t} + ε_{i, t} \end{matrix}

\begin{matrix} M V A_{i, t} = α + β_{1} M V A_{i, t - 1} + β_{2} A T R_{i, t} + \\ \sum_{k = 1}^{6} δ_{k} C o n t r o l_{k, i, t} + ε_{i, t} \end{matrix}

The vector i represents the number of firms that constitute the sample, and t represents the sample period.

First, we have used the ordinary least square (OLS) regression technique to study the above-mentioned relationship. Then, we find the correlogram of regression errors from an OLS (with fixed and random effects), which indicates the presence of autocorrelation in the dependent variable. Simple OLS might lead to a biased and spurious estimate because of the existence of autocorrelation in the dependent variable. We observe in the literature that OLS estimates are inconsistent (biased upwards) in the presence of a lagged dependent variable and fixed effects. Therefore, to overcome the above issue, we employ a dynamic panel data regression model and the instrumental variable technique in our research for which the GMM estimator is taken into consideration for calculation. The GMM methodology helps in fixing the problem of autocorrelation and the existence of endogeneity in the variables (Arellano & Bover, 1995; Blundell & Bond, 1998).

Empirical Findings

Descriptive Statistics and Correlation Analysis

Table 1 defines the descriptive statistics of the sample firms. The sample firms are mature as average and median log age of the firms are found to be 3.50 years and 3.43 years, respectively, while the average and median MVA by net sales ratio are 0.79 and 0.33, respectively, which implies that on an average, the companies have positive MVA. While the average sales growth is found to be around 9%, the median stands at 7.9% on a year-to-year basis. The average debt–equity ratio stands at 1.19, while the median stands at 0.61. This indicates that a considerable amount of debt is included in the capital structure of the companies. The average promoters holding stands at 54.30%, while median stands at 55.775%, which means promoters that have strategic control of the firms are also involved in the decision-making process of the firms. The average non-promoters institutional holding is 13.30%, whereas the median is 8.59%, which indicates other than promoters institutions like MF, insurance companies, etc. have also invested in the company as they were stable. The average DOL is 0.15, whereas the median value lies at 0.12. Average and median DFL of the firms are found to be 1.55 and 1.18, respectively. The average ATR is 0.85 times, and the median value is found to be 0.77 times, indicating higher efficiency of firms, that is, 85% of sales of total assets generated by employing their assets.

Table 1.

Descriptive Statistics of the Sample

Variables	Mean	Median	Std. Dev.
MVA	0.7905	0.3304	2.2442
DOL	0.1532	0.1223	2.3479
DFL	1.5570	1.1839	2.8104
ATR	0.8507	0.7778	0.5493
SGR	0.0907	0.0799	0.3069
LN_TA	9.6501	9.5535	1.7335
LN_AGE	3.5075	3.4340	0.5341
DE	1.1951	0.6100	2.1854
PROMOTERS	54.3097	55.7750	15.8497
NPH	13.3098	8.5900	14.1548

Source: Authors’ calculations.

Note: This table shows the descriptive statistics, namely mean, median, and standard deviation of the explanatory variables.

Table 2 shows the correlation between different variables. With this study, we substantiate that our variables do not have multi-collinearity as every coefficient is less than 0.5 (Deloof, 2003).

Table 2.

Correlation Analysis of the Sample

Variables	MVA	DOL	DFL	NPM	SGR	LN_TA	LN_AGE	DE	Promoters	NPH
MVA	1.0000
DOL	0.0313	1.0000
DFL	−0.0485	−0.0270	1.0000
ATR	0.0303	−0.0326	0.0315	1.0000
SGR	0.0104	0.0092	0.0096	0.2371	1.0000
LN_TA	0.1596	−0.0183	0.0163	0.0223	−0.0100	1.0000
LN_AGE	−0.0069	−0.0213	0.0306	0.0563	−0.0220	0.1285	1.0000
DE	−0.0188	0.0025	−0.0322	−0.1649	−0.0526	0.1663	−0.0259	1.0000
PROMOTERS	−0.0111	−0.0024	0.0519	0.1594	0.0843	−0.1091	−0.0761	−0.0053	1.0000
NPH	0.2396	−0.0098	−0.0229	0.0935	0.0093	0.6127	0.1392	−0.0172	−0.3870	1.0000

Source: Authors’ calculations.

Note: This table shows the correlation analysis between different variables.

Relationship Between Market Value Added and Degree of Financial Leverage

We found an inverse relationship between DFL and MVA, which was found to be significant (p-value stands at 0.0565), as presented in Table 3 and Equation (1). The negative significant relationship implies that the higher the DFL, the lower would the MVA be for firms incorporated in India. Previous works evidenced a substantial and inverse relationship between DFL and profitability of a firm (Ahmad et al., 2015; Gatsi et al., 2013; Kaur & Vineet Kaur, 2015; Khodaei Valahzaghard & Taherinejhad, 2012). The inverse relationship between DFL and Return on Assets due to the fact that a rise in financial leverage would lead to an increase in the cost of fund, which increases pressure to firms to sell their products more enough to maintain the level of net profit and also for breakeven. Ahmad et al. (2015) also identified the negative result.

Table 3.

MVA_i,t Dependent Variable: (market value added)

Variable	Equation (1)	Equation (2)	Equation (3)
MVA_i,t _–1	0.360593***	0.368502***	0.360702***
DFL	−0.010736*
DOL		−0.013649
ATR			−0.318932**
DE	−0.005472	−0.005549	−0.006715
LN_TA	−0.482104***	−0.475303***	−0.594027***
LN_AGE	−0.518565	−0.506788	−0.431236
Promoters	−0.014373*	−0.015131*	−0.014368*
NPH	−0.005683	−0.005253	−0.0047
SGR	−0.016894	−0.016367	0.063432
J-statistic	20.00846	19.78995	19.93638
Prob(J-statistic)	0.457401	0.471137	0.461915
AR(1)	0.0015	0.0013	0.0016
AR(2)	0.5101	0.5034	0.5048

Source: Authors’ calculations.

Notes: This table shows the result of GMM regression model between the dependent variable market value added (MVA) and the independent variables, that is, degree of financial Leverage (DFL), degree of operating leverage (DOL), asset turnover ratio (ATR), and net profit margin (NPM) separately. The control variables are size (LN_TA) proxied by natural log of total assets, annual sales growth (SGR), debt-to-equity ratio (DE), promoters holdings (Promoter), non-promoters holdings (NPH),and the natural log of age of the firms (AGE).

Period fixed effects were found and controlled for in all regressions. The notation ‘*’ is used for values significant at 10% level, ‘**’ for values significant at 5% level, and ‘***’ for values significant at 1% level.

No earlier literature exists, which cites the relationship between MVA and DFL. The result of this study enabled us to understand the relationship between the two variables. However, in prior studies, another proxy of profitability was considered and documented an inverse relationship between profitability and DFL.

According to Equation (1), two control variables were found to be significant, that is, promoters (promoters) and total assets (LN_TA).

Promoters holding has a negative relationship with MVA (p-value stands at 0.08), indicating lower promoter holdings leads to a higher MVA. A plausible cause for the above relationship might be the presence of control rights. Promoters affect management decision making, and they do not allow them to take risky but high return projects. The prime reason for this interference by promoters is to secure their claim over present cashflows. Previous literature found the same relationship (Ersoy & Koy, 2015; Lehmann & Weigand, 2000; Thonet & Poensgen, 1979).

We found a negative and significant relationship between size of the firm and MVA (p-value stands at 0.00). It may be because there can be a direct relationship between firm size and profitability, but at a specific threshold size, it may become negative (Amato & Wilder, 1985). This is also proved in previous studies (Athanasoglou et al., 2008; Kaen et al., 2010; Qi & Wu, 2006).

We also found MVA of the previous year has a significant (p-value stands at 0.000) and direct relationship with MVA of the current year (Pruitt & Gitman, 1991).

Relationship Between Market Value Added and Degree of Operating Leverage

We find an inverse relationship between DOL and MVA, but the relationship is insignificant (p-value stands at 0.20), as presented in Equation (2) and Table 3. Previous literature also found a negative relationship with profitability (Murugesu & Subramaniam, 2016). DOL shows a negative relation with profitability because of an increase in operating fixed costs caused by a rise in operating leverage. In order to increase the fixed cost, more sales are needed for a firm. This shows firms are not utilizing their assets efficiently to bear the finance costs and other operating fixed costs to transform the amount of debt into a more efficient way. The existing literature also found an inverse relation between operating leverage and profitability, which is also concluded by Baker (1973) and Selling and Stickney (1989). We also found two control variables, that is, promoters holding and firm size, which have a negative relationship with MVA (Athanasoglou et al., 2008; Ersoy & Koy, 2015; Lehmann & Weigand, 2000; Thonet & Poensgen, 1979; Qi & Wu, 2006).

Relationship Between Market Value Added and Asset Turnover Ratio

From Equation (3), as in Table 3, we also found a substantial inverse relationship between ATR and MVA of Indian firms. It implies that the lower the ATR, the higher the market value created or added by firms and vice versa. The above relationship exists because of the cost of sales. If the rate of increase in the cost of sales is higher than sales, then ATR will affect profitability inversely (Selling & Stickney, 1989) and (Reed & Reed, 1989). We also found two control variables, that is, promoters holding and firm size, which have an inverse relationship with MVA.

Conclusion

In this article, we try to determine a relationship between firms’ leverage and efficiency and MVA of firms with the help of dynamic panel data regression and GMM estimator. We used panel data for the period between 2013 and 2019 for those Indian firms whose data were consistently available between this period. In sum, we conclude that DFL and ATR have a significant and negative relationship with MVA of the firms. Also, DOL was found to be negatively related to MVA of the firms, but the relationship is not found to be significant.

To enhance the wealth of the shareholders, a higher MVA is required. A lower DFL is desirable to increase MVA as a high degree of leverage put pressure on overall profitability and may lead to bankruptcy if any negative changes occurred in the business cycle. Also, if the rate of increase in the cost of sales is higher than sales, then ATR will affect profitability inversely. Hence, it affects MVA negatively. We have also found a negative but insignificant relationship between DOL and MVA, as an increase in fixed operating cost inversely affects the profitability of the firms in India.

We also found that there is a significant relationship between MVA and two control variables, that is, promoters holding and the size of the firms. However, last year’s MVA has a significant relationship with MVA of the current year. The results and findings of our study may assist stakeholders, including investors, managers, and lenders in making efficient, performance enhancement, investment, and lending decisions.

This article contributes to the existing literature is several ways. There is no standard literature on studies that found a relationship between MVA and DFL, DOL, and ATR. Hence, this article attempts to find the relationship among them. Second, we do not find any paper that discusses about drivers of MVA. Thus, our study can be considered as an initial attempt to find the drivers of MVA. In this article, we provide more insight about factors that affect MVA of firm; we also demonstrated how leverages and firm’s efficiency affect the market value of firms. The practical implication of this study is that it helps financial managers to understand the drivers of MVA. Moreover, the interplay between MVA, leverage, and efficiency in firm’s value addition can be understood, which will help in making decisions regarding asset allocation. Therefore, this understanding will help in overall improvement in their portfolio.

In this study, we try to establish the relationship between firms’ leverage and efficiency and MVA after controlling for control variables. We only studied how these variables affect profitability because of the limitation of time. This research work offers further scope of studying how this relationship varies among different industries and different economies.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

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