Keywords

Capital Structure, Leverage, Profitability, Tangibility, and Business risk

Introduction

A capital structure choice is a strategic management decision that affects the revenue and profitability of the firm's shareholders. Capital structure is a kind of long-term financial capital that determines how to invest best in fixed assets as well as guarantee profitability via a mix of loan and equity. Instead of deducting interest and the tax advantage from net income, some companies just deduct interest and the tax benefit. On the other hand, shareholders have a residual claim with the company's assets, while debt holders have a superior claim with the company's assets. A company's profits per share may be increased by incorporating borrowed capital into its capital structure (Chaklader & Chawla, 2016). The financial strategy known as capital structure includes borrowing money in order to maximize profits. In terms of investment, Leverage refers to debt, sometimes known as borrowed money to fund Asset acquisition. A company's Assets may be financed or purchased using either debt or equity. Leverage is the most contentious issue in finance, and it is the one that academics are still under debating.

Using Financial Leverage to fund Assets is referred to as Capital Structure. Because Capital Structure has a substantial effect on the owner's market return, and has consequences with the trading value of the shares. It is clear that the capital structure is the vital decision of the Management In specific terms, not only the Management does the business influence funding decisions, but the funding decisions also influence Management because the incorrect mix of money is used, the performance and survival of the commercial organization may suffer significantly. Nevertheless, businesses involved in financing decisions may be concerned with a wide variety of policies outside the direct authority of the firm's Management. The company chooses an acceptable amount of Leverage to guarantee the business's viability (Hanitha vijeyaratnam & Anandasayanan, 2015). Moreover, arguments revealed how successful businesses would likely draw in more shareholders than unprofitable ones, since they provide a guarantee of profit and security. Businesses face reduced financial difficulty and liquidation as a result of their increased debt payment capacity. This increases their reputation and availability in the stock sector and reduces their financing costs. High-profitability corporations may reconfigure their financial performance by increasing or decreasing the earnings per share.

The study of Modigliani and Miller (1958) found that when all shareholders have complete information, all trading expenses are zero, and there is no tax difference between capital gains and dividends, then the capital structure has no influence on shareholder's performance. But, the actual economies are beyond ideal. Numerous finance choice theories have been created throughout time to show the purpose of the capital mix and its involvement in business value. Leverage is often used to refer to the borrowing ratio, which expresses the connection between money borrowed and owner funds in a company’s capital structure. It differs across companies and sectors. Businesses with equity are referred to as unlevered. While firms with debt and equity are referred to as levered. The firm's funding decision is predicted with the current capital market conditions. There are no implications for restructuring the capital structure if the level of standards or banking sector theories does not consider an optimal capital structure for the company (Sulagna Mukherjee & Mahakud, 2010).

Literature Review

There have been many studies looking at the best capital structure to maximize a business's value while keeping the cost of capital to a minimum. A company's capital structure has a dramatic effect on the value of the firm. For many years, theorists have struggled with optimum capital structure decisions. An early study was made with several assumptions, including the capital structure that was different from debt and equity with the relevant statistics to examine the firm's gross profit. Researchers, academics, practitioners, and the business sectors have a long structure of attention with the capital structure. Many researches have been conducted in this area to determine the factors of company capital structure.

Chaklader and Chawla (2016) investigated the drivers of capital structure for companies listed on the NSE CNX 500 from 2008 to 2015. According to the regression equation results, the independent variables describe 73.74 percent of the changes in capital structure. In their study, Chaklader and Chawla (2016) found that the capital structure directly relates to the size of the firm and its tangibility. However, the non-debt tax shield and liquidity have an insignificant relationship with capital structure. Another research used regression and correlation analysis to investigate the connection between the variables influencing leverage in listed manufacturing firms in Sri Lanka from 2008 to 2012. The consequence of the study found that the essential factors manufacturing firms in Sri Lanka are non-debt shield and profitability. Likely, tangibility will not create any impact on the capital structure of the manufacturing firms in Sri Lanka (Hanithavijeyaratnam & Anandasayanan, 2015).

Wellalage and Locke (2013) investigated the connection between company characteristics, Capital structure, and corporate governance in major listed firms in New Zealand. Data’s of 40 companies listed on the NZX50 Stock Exchange are gathered during eight years, and observations are examined using conditional quintile regression. Company working capital, Tangibility, Tax shelter, Annual growth, Risk, Company size, and Industry may influence Capital structure decisions in developing Market companies. These factors, however, have varying effects on various degrees of Leverage quintiles. Nonetheless, the findings suggest that finance regulations should change company type and firm characteristics and should be tailored to the various borrowing needs of listed companies (Wellalage & Locke, 2013). Ghose and Kabra (2018) used an empirical survey of listed companies from 2004–2005 to 2015–2016 to investigate the significance of Capital structure in Indian enterprises. The research discovered that 32% of Indian companies chose their own Leverage. The study finds a positive effect of Tangibility, Productivity, and Industry median leverage on the Capital structure and a negative impact of profitabilitNi distinctiveness on the Leverage. These findings are consistent with theoretical predictions and previous empirical findings (Ghose & Kabra, 2018). Every company is confronted with risks and uncertainties; the larger the firm, the stronger it is anticipated to be in such hazardous in uncertain circumstances. A larger company develops stronger methods and techniques of combating market risk and uncertainty. A larger company is anticipated to have a greater chance of offsetting unpredictable losses (Bhattacharyya & Saxena, 2010).

From 2008 to 2017, the impact of capital structure on the financial performance is of Nifty 50 businesses. The findings of the models, (1 and 2) randomly indicate that increasing total Debt decreases return on Assets while increasing Equity increases return on Assets, implying that capital structure has a substantial impact on performance (Singh & Bagga, 2019). In addition, an investigation (Chadha & Sharma, 2015) empirically evaluates the influence of Capital structure on firm value of a chosen sample of 422 Indian manufacturing firms on the Bombay Stock Exchange (BSE) and examined the trends and Leverage effect. To examine the patterns and Leverage impact, yearly financial independent data of 10 years duration from 2003 to 2013 was used. The empirical research was carried out using ratio analysis and the panel data method. Using the panel data, the fixed effect regression method was applied to four distinct models. It was discovered experimentally that there is a high debt level in the capital structure of the businesses and there is no significant connection between a firms’s worth and Leverage. In other words, the Indian manufacturing sector, Leverage has little effect on firm value. However, in the Indian manufacturing sector, factors such as company size, growth, profit, and age are significantly and positively associated with a firm value (Singh & Bagga, 2019). The profit drives the chosen firm-related financial variables of Indian BSE-listed companies. The research period was limited for (2009-19), a span of ten years, and forty non-banking and financial companies. The necessary data is a dynamic in nature. Therefore it was examined for a dynamic panel regression model. According to the findings, business size and growth are the most important determinants of profitability. Furthermore, company size is inversely linked to profitability, while growth is related to the fluctuation of the profit rate of BSE-listed companies in India. Other factors hurt profit fluctuation in this ratio except for risk, and the result is statistically insignificant (Sengottaiyan, 2021).

During the period of 2004–2013, the capital structure and Leverage has impact on firm value of a 422 Indian manufacturing firms. During 2004–2013, the total equity increased significantly, accounting for a larger proportion of total capital than debt. The panel data fixed effect regression method is applied to four distinct models, and it was discovered that there is no direct correlation between company value and leverage. In other words, in the Indian manufacturing sector, leverage has little effect on firm value (Chadha & Sharma, 2016). The findings have also been verified for robustness using alternative definitions of capital structure, such as total debt to total assets and total liabilities to total assets. It has been discovered that factors such as non-debt tax shield, profit, depreciation, and industry median play a significant influence in determining the optimum leverage ratio in India (Sulagna et al., 2010). The debt ratio is favorably linked to Asset structure; Growth, profitability, and Age which are adversely related to the debt ratio (Talberg et al., 2008) Table 1.

Table 1 Framework Hypothesis Of The Study
Sl. No	Variables	Calculations	Hypothesis
1	Profitability		Capital Structure is negatively impacted by profitability
2	Firm Size	(Total Assets)	Capital Structure is negatively impacted by firm size
3	Tangibility		Capital Structure is negatively impacted by tangibility
4	Growth	ln ln(Sales Turnover)	Capital Structure is negatively impacted by growth
5	Risk	Std.Dev.(EBIT)	Business risk inverse association with leverage
6	NDTS		Capital Structure is negatively impacted by non-debt tax shield
7	Liquidity		Capital Structure is negatively impacted by liquidity

Aim of The Study

The goal is to investigate the factors of capital structure in non-financial companies and empirically investigate the effect of Productivity, Growth, Non-debt tax shield, Size, and Liquidity on Indian firms' capital structure. In a developing market such as India, for the Indian manufacturing firms to be efficient, they must have a suitable capital structure. It is critical to understand how Indian companies finance their operations and their net capital structure, Debt-equity balance, and financial development.

Methodology

This research investigates the variables that influence the leverage of businesses listed on the National Stock Exchange (NSE). The research is focused on the top NIFTY 50 firms from 12 different business categories. Out of the listed NIFTY 50 indices, this research solely examines 27 non-financial firms and service-oriented firms. The required information was gathered from the annual reports of the chosen businesses for the fiscal years ending between 2010 and 2019. Following the previous research, “the study excludes financial and banking companies since their capital structures are not comparable to those of non-financial firms. Accounting ratios are used to derive the relevant financial variables from the companies' annual reports. The data was a panel in nature, and it was analysed dynamically using a comparison of Pooled OLS, fixed-effects, random effects, and GMM techniques. The DPD (Dynamic Panel Data) method is often regarded as Arellano and Bond's (AB) (Rev. Ec. Stud., 1991) work, although they popularized the work of Holtz-Eakin, Newey, and Rosen (Econometrica, 1988). It is founded on the premise that the above-mentioned instrumental variables method does not fully utilize all of the knowledge provided in the sample. We may build a more valued Asset of the dynamic panel data model by doing so in a Generalized Method of Moments (GMM) context. Consider the following equations”: y_it = X_itβ₁ + W_itβ₂ + u_i + ɛ_it

Where “Xit includes strictly exogenous repressor’s, Wit are predetermined repressor’s (which may include lags of y) and endogenous repressor’s, all of which may be correlated with ui, the unobserved individual effect. First, differencing the equation removes the ui and its associated omitted-variable bias”.

Results and Discussions

The values of mean, median, and Std. Dev. for dependent and independent variables used in the target estimate are listed in Table 2.