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Capital Structure of the Food Processing Industry

Info: 10442 words (42 pages) Dissertation
Published: 12th Jan 2022

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Tagged: Business


Sr. No. Particulars Page No.
1 Abstract 2
2 Introduction 3
3 Objective 5
4 Literature Review 6
5 Hypothesis 12
6 Sample Size & Research Methodology 13
7 Data Analysis & Interpretations 16
8 Conclusion 21
9 References 22
10 Annexure 23


Food processing industry being a capital intensive manufacturing industry, the management is challenged to choose from various financial sources when raising capital for these huge investments. In the following studying, we endeavour to study the degree of impact of determinants such as profitability, tangibility, liquidity, growth and firm size on the company’s debt to equity ratio. A multivariate regression model was employed to study the dependency relationships of the capital structure with the explanatory variables.  Our study revealed that 67% of change in the dependent variable can be attributed to the explanatory variables. Profitability, Tangibility and liquidity has a statistically significant negative relationship with the debt to equity ratio whereas firm size and growth were proved to be statistically insignificant. Our study was grounded on the trade-off theory and pecking order theory, which were used to explain the dependency.


Capital is the investment required to start and maintain the existence of an organisation. It may be in form of money or assets. There are two types of Capital based on their sources:

  1. Equity Capital
  2. Debt Capital

Equity Capital refers to the capital raised through Shareholders (owners) in the form of Shares. It also includes the retained earnings of the company in the form of reserves and surplus.

Debt Capital refers to the capital raised through Borrowed Money in the form of Loans, Short Term Commercial Papers, Bonds and Debentures.

In simple terms, Capital Structure indicates how a firm funds its day to day activities and long term growth. It involves taking decisions on the different combination of capital raised from various sources, best suitable for the organisation’s growth and its shareholders. The management tries to design a perfect Capital structure for the organisation in terms of risk/rewards payoff for its shareholders.

Equity Capital vs Debt Capital

Both these sources of fund raising have their own benefits and risks attached.

Many consider Equity Capital as the most expensive type of capital a company can employ, as its cost is the return a firm has to pay to attract investors. But there is more flexibility as compared to Debt Capital; which is less expensive to Equity Capital.

External as well as internal factors have to be considered before choosing the right combination of both fund sourcing options.

Modigliani and Miller (MM) Approach

Two professors, Modigliani and Miller, studied Capital Structure closely in 1950 and developed a Capital Structure Irrelevancy Theory. It suggests that the valuation of a firm is irrelevant to the capital structure of an organization. In other words, whether an organization is highly leveraged or has lower debt component, it has no bearing on its market value; infact its market value depends on its Operating Profits. This approach takes into consideration the following assumptions:

  • No taxes are levied
  • No transaction cost is charged for buying and selling of securities
  • No bankruptcy cost is charged
  • There is a symmetry of information; wherein investors and corporates will have access to the same information
  • The cost of borrowing will remain the same for investors as well as the corporates
  • Debt financing does not affect the EBIT of a company

A proposition from this approach is that, debt holders and equity shareholders of an organisation have the same priority i.e. the earnings are to be split equally among them. Other one says that, since the financial leverage is in direct proportion to the cost of equity, the debt holders have a preference claim on the earnings.

The Trade-Off Theory of Leverage

In opposition to the assumption considered in MM approach, Taxes are levied on corporates in reality. This theory does consider the tax benefits accrued on interest payments; but the same does not exist in the case of dividend payment in equity. Therefore, this theory suggests that an organization can capitalize its requirement on debt unless the cost of bankruptcy exceeds the value of tax benefits. Thus, laying more importance on debt capital until a threshold limit, will add significant value to the firm. This approach implies that a change in debt equity ratio has an impact on Weighted Average Cost of Capital(WACC)[1]. As a result, higher the debt, lower is the WACC.

Pecking Order Theory

Pecking order theory doesn’t give one a structured target debt-to-equity ratio but ranks the different financing options, and is seen contrasting to the trade-off theory. Myers & Majluf pointed out that firms prefer internal finance (Retained earnings)[2]. If this internal funding is unavailable or insufficient, the firm will issue the safest security first. They will start with borrowing debt, then hybrid debt and equity securities[3] and equity as a last resort.

The pecking order theory cannot state a target or preferred debt to equity ratio, because of the existence of two kinds of equity, internal and external, one is at the top of the pecking order rank table and the other one at the bottom. Each firm’s relative debt to equity ratio reflects its cumulative requirements for external sources of finance.

“And make sure that Capital Structure we have in place is the right capital structure. I think that’s the reason that we have been successful.”

- Henry Kravis (Co-Founder of Kohlberg Kravis Roberts & Co)

Reasons for selecting the food processing sector

The food processing industry in India is a sunrise sector that has gained prominence in recent years. Availability of raw materials, changing lifestyles and relaxation in policies have given a considerable push to the industry’s growth. Food processing industry is of paramount significance for the development of India because of the vital bond and synergy it promotes between the two pillars of India’s economy- industry and agriculture. Strengthening this link is imperative to enhance the value of agricultural produce, ensure adequate returns to the farmers and simultaneously make the Indian agricultural products competitive in the global market. Growing at over 20 percent per annum, Indian food processing industry is pegged at around US$ 150-170 billion.[4]

The primary reason behind choosing the food processing industry is because food processing sector stock is the most resistant and stable stock among the other sectors when faced with the economic downturns due to the inelastic demand of the products. Being a capital intensive manufacturing industry, the management is challenged to choose from various financial sources when raising capital for these huge investments. The companies therefore must strengthen the internal factors in order to maintain the growth and sustain, this can be achieved by managing the capital structure efficiently to maximize the wealth of the firm, specifically owner’s wealth maximization. To maximize company’s value and minimize the cost of capital, a manager should set up an optimal capital structure. Capital structure of the food processing industry encounters instability; inappropriate capital structure might stall production which might disrupt the business. The problem in determining capital structure decision is how to mix between debt and equity in the company’s capital structure that will influence its market value. Food processing companies have a high composition of capital structure characterized by a high level of total debt to total equity. Hence, deterring the factors and their relative significance of their impact on the capital structure can help managers make effective decisions.


The objective of this research paper is to identify the factors that are considered by companies before they make financing decisions. Furthermore, to study the degree of significance of impact of determinants on capital structure and understand the interdependence of these independent variables. This paper also tests the regression model based on Trade-off and Pecking Order theories and analyses the reasons behind change in financials and their relative impact on the debt to equity ratio.

Literature Review

Empirical tests on the determinants of capital structure have confirmed that the M-M model does not hold true in the present competitive market and capital structure decisions are relevant. These decisions are affected by a host of factors which are relative to the industry and market dynamics. The capital structure decisions have been studied using regression models to validate the significance and degree of impact of determinants of an industry on the management’s capital structure decisions mainly the debt to equity ratio.

Dr. Anurag Pahuja and Ms. Anu Sahi (2012) studied the factors affecting capital structure decisions. This research paper uses Debt Equity Ratio as a Dependent variable and size, growth, profitability, liquidity, tangibility as Independent variables. The data is taken into consideration for a sample of 30 companies constituting SENSEX for a period of 2008-10. This analysis is based on agency theory and pecking order theory. The authors employed a correlation to conclude that debt-equity ratio is positively related with liquidity and growth with Pearson correlation coefficient of .603 and .742 respectively, both being significant at 1 percent level of significance for 2008. But the Pearson correlation of debt equity ratio with size, profitability and tangibility are negative; statistically insignificant. The same was tested and proved for the years of 2009 and 2010. Hence, implying that growth and liquidity factors largely impact the capital structuring decisions of an organisation, supporting the pecking order theory of capital structure. The other factors such as profitability, tangibility and size do not have a significant impact on capital structure.

Dr. Abhay Raja and Ms. Niyati Dav (2013) appraised the empirical relationship between capital structure and profitability. This research paper uses Return on Equity (a tool of profitability) as a dependent variable and derived measures of short term debt, long-term debt, total liability (TL) as independent variables. The data is taken into consideration for a sample of 100 companies constituting BSE 100 for a period of 2007-12. In order to quantify the magnitude of impact of each variable on profitability, regression method of Ordinary Least Square (OLS)[5]is used as a tool for analysis. The Durbin-Watson’s test is used to verify the presence of auto-correlation. R2 is used to measure the level of confidence.The general trend of companies being more inclined towards long term debt (LTD) than short term debt (STD) was confirmed by the research’s conclusion. This is evident from the figures of average STD/TL (0.1973) and LTD/TL (0.2279). Also, sample companies had considerably higher equity in comparison of total liabilities (average E/TL = 1.5298). It was further concluded that companies employed around 25% long-term debt against their equity (LTD/E = 25.52).The findings proved that ROE is positively correlated with STD/TL but negatively correlated to LTD/E, indicating that the finance managers have to be careful about the availability of the funds; in case funds are required on immediate basis, it would have a negative impact on ROE. Hence, a striking balance between STD and LTD is vital while taking capital structuring decisions.

Anshu Handoo and Kapil Sharma (2014) conducted a research to study the impact of determinants on the capital structure decisions of Indian firms. This paper has a sample size of 870 listed Indian firms that includes both private sector & public sector (government) companies for the period 2001-2010. Ten independent variables and three dependent variables have been tested using multiple regression analysis. This has been done by making five assumptions i.e. the Normality Assumption Test, the Homoscedasticity Assumption Test, the Linearity Assumption Test of each of the independent variables with the dependent variable, the Durbin-Watson Statistic Test for detecting serial correlation and the Multicollinearity Test The research has been conducted using regression analysis with 3 scenario based models & a set of 10 hypotheses those are tested along the lines of the models namely Model 1 (Indian companies and short term debt), Model 2 ( Indian companies and long term debt) & Model 3 (Indian companies and total debt). This paper implies that factors such as profitability, growth, asset tangibility, size, cost of debt, tax rate & debt servicing capacity have significant impact on the leverage structure chosen by the Indian firms.

Ronald Anderson (2002) carried out a research within National Bank of Belgium to explore the relationships among a company’s capital structure, its choice of liquid asset holdings & growth. The research assumed that long-term dependence on high levels of debt tends to be linked with high levels of liquid asset holdings which identifies possible slow growth. The sample size includes one set of companies from UK & another from Belgium & hence is divided into two sections. The study compares its findings with two empirical studies before its own namely Kim, Mauer and Sherman (1998) and Opler, Pinkowitz, Stulz, and Williamson (1999). The implications of this study are that there appears to be a robust positive relationship between growth opportunities and corporate liquidity. Cash flow volatility appears to be positively associated with liquid asset holding. There does not appear to be a stable or robust relationship between cash flow and corporate liquidity. The paper hence concludes that there is in fact positive correlation between leverage and liquid asset holding.

Dr. Rohit R. Manjule conducted a research on Indian companies with regard to their debt-equity mix and the effect of various other factors on their capital structure decisions. The research was conducted using secondary data with a sample size of 151 companies based on their market capitalization ranks as on March 2012. The debt-equity ratio differs significantly among various industrial sectors with a range of 0.295 to 4.079 with the former observed in case of IT and the latter in case of Finance & Investment sector. Low debt equity ratios, i.e., below 0.5 were observed in case of the Engineering and Personal Care sectors and medium, i.e. between 0.5 to 0.99, in case of Energy, Pharmaceutical and Chemical sectors.

The Electricity, Auto and Diversified sectors indicated an average leverage with a range of 1to1.49, while the Construction, Cement, Steel and Finance industries indicated a high leverage extent with ratios above 1.5. In most cases, the debt-equity ratios of the firms within an industry were similar barring a few outliers. But using the ANOVA technique, significant differences in the ratios across industries were found. A correlation matrix analysis revealed that the company’s profitability, solvency and size had a significant effect on its leverage. A regression analysis with these variables as the explanatory variables and debt-equity ratio as the independent variable revealed an R squared of 0.245, i.e. hardly 25% of the variation in the debt-equity ratio could be explained by the explanatory variables. A further regression analysis revealed that determinants of the debt-equity ratio of different industrial sectors are not similar with different factors having varied impacts on capital structures of different industries.

Rajesh Bagga and Jaspinder Kaur (2016) inspected the manufacturing and service industries in India to identify factors affecting their respective capital structures. The sample consisted of 104 manufacturing companies and 92 companies from the service sector, with their secondary data for 11 years, from 2003-04 to 2013-14. Application of stepwise regression analysis on manufacturing companies, after excluding variables where multicollinearity existed, revealed that tangibility had the most significant impact on the capital structure with other significant variables which include age, liquidity, tax and profitability. The financial leverage remained unaffected by the other variables which include growth, size, non-debt tax shield and operating leverage. On the contrary, application of multiple regression analysis to service sector companies revealed that only profitability significantly affects the capital structure decisions.

Aviral Kumar Tiwari & Raveesh Krishnankutty (2015) investigated the chief determinants of the capital structure decisions of the Indian firms using a quantile regression analysis based on standard least panel squares estimators for the level of debt. This methodology was adopted by the authors to overcome the possibility of flawed estimates on account of the skewed distribution of the independent variables Sales, profitability, tangibility, size, growth opportunities and Non-debt tax shield. The researchers employed balanced panel data procedures for a sample of 298 Indian firms during 2001- 2010 to study whether the capital structure models developed in the Western setting provide a plausible explanation for capital structure decision of the Indian firms. The research results were non-sensitive to the changing of the independent variable and the fixed and random effect model were found to be inefficient.

According to the results, for the lowest quantile- Sales and tangibility are positively significant, and NDTS[6] and profit are negatively significant implying that the capital structure decisions of companies which are keeping very low level of debt are determined by their high sales, high tangible assets and the high amount of depreciation. For median quantile, profit is found to be negatively significant and tangibility is positively significant. For the highest quantile, in model one, Sales and profit are negatively significant and tangibility and growth opportunities are positively significant. The companies are having high debt in their capital structure, decisions are influenced by low sales, low profit with large amount of fixed assets and high growth opportunity.

Emil K Bratlie & Andreas Jøtne (2012) conducted an empirical study on the company specific factors that affect the capital structure decisions in the global airline industry. An econometric approach was used by conducting an OLS[7] regression analysis over two dimensions- cross sectional and time series to analyse the data for a sample comprising 39 airlines from different parts of the world over a ten year period (2000- 2010). The researchers assumed the dependent variables to be book or the market debt ratio while the independent variables consisted of company specific factors: size, profit, growth, tangibility of assets, leasing, financial strength, strategy, ownership situation and transparency. The research results depicted that the market model explained 31.1% of the variation in capital structure of airline companies, and that six out of seven (except leasing) independent variables were significant whereas the book model only explained 18.5%, and had only one significant variable. The research concluded that the market models are more forward looking than the book models, which implied that stakeholders base their decision on the future expectations rather than historical values.


Null Hypothesis:

  • Ho1: There is no statistically significant relationship between Capital Structure decisions and the firm’s Liquidity hence, Liquidity doesn’t affect the capital structure of an organization
  • Ho2: There is no statistically significant relationship between Capital Structure decisions and Profitability hence, profitability doesn’t affect the capital structure of an organization
  • Ho3: There is no statistically significant relationship between Capital Structure decisions and Firm Size hence, Firm Size doesn’t affect the capital structure of an organization
  • Ho4: There is no statistically significant relationship between Capital Structure decisions and Tangibility hence, tangibility doesn’t affect the capital structure of an organization
  • Ho5: There is no statistically significant relationship between Capital Structure decisions and Growth hence, Growth doesn’t affect the capital structure of an organization

Alternate Hypothesis:

  • H11: There is astatistically significant relationship between Capital Structure decisions and the firm’s Liquidity hence, Liquidity does affect the capital structure of an organization
  • H12: There is a statistically significant relationship between Capital Structure decisions and Profitability hence, profitability does affect the capital structure of an organization
  • H13: There is astatistically significant relationship between Capital Structure decisions and Firm Size hence, Firm Size does affect the capital structure of an organization
  • H14: There is a statistically significant relationship between Capital Structure decisions and Tangibility hence, tangibility does affect the capital structure of an organization
  • H15: There is a statistically significant relationship between Capital Structure decisions and Growth hence, Growth does affect the capital structure of an organization

The Null Hypothesis is rejected when the Co-efficient of correlation is high and the respective p-value of the variable is less than 0.05 proving that capital structure decisions are dependent on the respective independent variable.

Sample Size & Research Methodology

The collected financial data for the sample companies is the foundation of our research and we have therefore briefly discussed about the procedure of data collection and inclusion or exclusion of companies from our sample size.

To collect data for this thesis we have used the Bloomberg terminal database. Bloomberg was selected as the primary source as it comprises comprehensive time series data that provides us with all the vital data needed to conduct our structured analysis.

To study the affect and significance of the determinants of capital structure, we have opted to analyse the food processing industry. The primary reason behind choosing the food processing industry is because food processing sector stock is the most resistant and stable stock among the other sectors when faced with the economic downturns. This helps us to effectively study the determinants even during times when the world was struck by the most gruesome financial crisis.

A time period of 10 years was taken into consideration from FY 2008 to FY 2017 for all the 8 sample companies. A long term time horizon helps us study the overall effect of the determinants and study the changes in capital structure decisions, market dynamics and company-specific internal environment overtime.

The Indian food processing industry is dominated by the 4 blue chip players accounting for more than 75% of the industry market share.[8] To get a more vivid picture of the capital structure decisions of the industry, we have adopted  extreme/deviant case[9] – purposive sampling method [10] by taking the 8 largest companies of the industry based on market capitalization which were listed on or before March 31, 2007. This helps us reduce the sampling error as these companies serve as true representative samples[11] for the industry at large. In our research we have endeavoured to substantially reduce the sample selection bias by selecting the sample companies by taking into account reasonable explanations for their inclusion or exclusion.

We have filtered out Varun Beverages, Manpasand Beverages, Hatsun Agro, Parag Milk Foods, Kwality Ltd, DFM Foods, Prabhat Dairy and Apex Frozen from our sample selection -though these companies fall in the top market capitalization bracket- as they were listed after March 31, 2007.  If we included these companies in our sample, it would have raised the possibility of diminishing the reliability and coherency of our research because of potential faults (null values) in the data. As a consequence, we have excluded these companies from our analysis. But this also serves as one primary limitation of our model as we have not accounted for some of the prime players in the market as of September 2017.

GlaxoSmithKline Consumer Healthcare Ltd. changed their accounting years from year ending on December 31 to March 31 in FY 2013. For this reason, the FY 2013 financials were not reported on December 31. To eliminate this discrepancy, we have excluded the FY 2013 data set for GSK Con and taken FY 2007 into consideration to get a dataset for 10 financial years. The financial information for FY2013 was accounted for in the figures of FY2014 by the company hence it does not affect the overall accuracy of the data.

The purpose of this research is to study the relative dependency of the capital structure decisions of the companies on the varied independent determinants. To study the degree of the impact of multiple explanatory variables (X) on the dependent variable (Y) we have employed a multivariate linear regression model[12] as the number of explanatory variables (X) are greater than one. This econometric modelling technique is used for forecasting future trends and expected figures based on historic data, time series data analysis to study the changes in trends over a period of time and to find the causal effect[13]relationship between response and predictor variables. Regression Analysis is employed over other techniques primarily due to the following two benefits of regression:

  1. It indicates the statistically significant relationships[14] between dependent variable and independent variable by analysing their relative coefficients’ p-value and adjusted R squared value
  2. It indicates the strength of effect of multiple explanatory variables on a dependent variable

We also plot the correlation coefficient matrix to analyse the relative correlation between the independent variables and dependent variable and also between the independent variables to test for multi-collinearity of the regression model. This is done to test the distinction of the impact of each independent variable on the dependent variable to study the relative extent to which each independent variable explains the dependent variable.

To study the effect of determinants on the capital structure, we have taken the Debt to Equity Ratio as our dependent (Y) variable whereas we have taken 5 determinants- Liquidity, Profitability, Firm Size, Growth and Tangibility as the explanatory (X) variables.  Only 5 explanatory variables were considered as adding more variables may explain the variation in the dependent variable to a greater extent, but it also opens a Pandora’s box for greater errors of adding determinants that do not affect the Y variable at all making the model more complex and less reliable leading to an issue of overfitting. We have assumed a level of significance, of 5 per cent, to test our estimated coefficients of the regression equation to determine their statistical significance.

Table 1: Dependent & Independent Variables and their Definitions

Determinants Definitions

Dependent Variables

Debt – equity ratio

Total debt (outsider’s funds) by Total Owner’s Funds


Independent Variables Profitability

Return on total assets (Net Income/ Total assets)
Firm Size Natural logarithm of Sales
Tangibility Net Fixed Assets divided by Total Assets
Growth Growth of Net Fixed Assets
Liquidity Ratio of current assets to current liabilities

A Multivariate Regression Model can be formulated for this study as follows:

Z = βo+β 1Profitability + β2 Size+β3 Tangibility+β4 Growth+β5 liquidity + ε

  • Z= Regression Score
  • β0= Regression constant (intercept)
  • β1= Regression Coefficient
  • Profitability, Size, Tangibility, Growth and Liquidity are the independent variables
  • ε (epsilon) = error term which is usually taken as 0 for multivariate tests


  • Existence of a Linear Relationship between the response and predicting variables
  • Multivariate-Normality– Multivariate linear regression model assumes that the variables are normally distributed
  • No Multicollinearity–  The independent variables are not highly correlated with one another
  • Homoscedasticity– The variance of error terms is similar across all the explanatory variables

Variables Explanation:

Debt to Equity

The Debt to Equity ratio of a company is used to measure the financial leverage of a company.  The ratio tells us how much debt a company uses to finance its assets relative to the amount of equity i.e. the amount of value represented in shareholder’s equity.

Debt to equity ratio can be calculated by dividing a company’s total liabilities by its total shareholder’s equity.

Debt-Equity Ratio = Total Liabilities/ Shareholder’s Equity

This ratio tells us if a company is taking up debts to increase its value by using borrowed money to fund various objects. A high debt-equity ratio usually means that the company is aggressive in financing its growth with debt. If a lot of debt is used to finance operations of the company it could lead to higher potential earnings for its shareholders. Since taking up debt doesn’t dilute the shareholder’s stake, it leads to higher earnings as the profits aren’t divided amongst more shareholders. However, it is very important that the cost of debt is lower than the returns or the value of the shareholder’s stocks may take a hit.

Profitability (Return On Assets)

It is a ratio that indicates how profitable a company is relative to its total assets. It indicates how efficiently the management is using its assets to generate earnings. Its calculated by comparing the company’s annual earnings by its total assets. Comparing two companies based on ROA from different industries may not give an accurate picture. Hence investors should use previous ROA numbers or the ROA from their peers.

Return on Assets= Net Income/ Total Assets

Firm Size (ln(sales))

The natural log of sales is a preferable alternative while computing firm size, over the sales in regression because when we are calculating growth for a time period on a continuous basis, we end up having an exponential in the calculus. Natural log is used to get rid of this exponential. Moreover, the first differential of natural log of sales is simpler and the coefficients on the natural-log scale can be directly interpreted as approximate proportional differences.

Firm Size= ln(Sales)

Tangibility (Net Fixed Assets by Total Assets)

Fixed Assets by Total Assets is used to measure how much percentage of the total assets are made up of fixed assets.

Fixed Assets by Total Assets Ratio= Net Fixed Assets / Total Assets

A high ratio, 0.5 or higher, indicates an inefficient use of working capital which reduces the enterprise’s ability to carry accounts receivable and maintain inventory and usually means a low cash reserve. This will often limit your ability to respond to increased demand for your products or services.


Net Fixed Assets is the price of all fixed assets (including Land, buildings, equipment, machinery, vehicles) less Depreciation accumulated, i.e. effectively property, plant and equipment post depreciation. Hence it is defined as Total Assets – Total Current Assets – Total Intangibles & Goodwill.

Growth of Net Fixed Assets= (Net FA t/Net FA t-1)– 1

Growth of Net Fixed Assets explains the expansionary policy of the company and how the financial resources are being utilized. A higher growth in fixed assets means the increase in manufacturing capacity usually to fulfil the increasing demand. Growth in assets is preferred over growth in sales while studying capital structure as growth in assets directly signifies the deployment of capital and an increase could be attributed to efficient uses of capital whereas a growth in sales could be attributed to other factors apart from an increase in production capacity.

Liquidity (Current Ratio)

It is a liquidity ratio. It measures a company’s ability to pay off its short & long term obligations. The current ratio can give a sense of the efficiency of a company’s operating cycle or its ability to turn its product into cash. Current ratio is calculated by comparing a company’s Current Assets and its Current Liabilities. A high current ratio (i.e. more than 1) indicates the company can pay off its liabilities easily as it indicates that the company has more saleable assets than its liabilities. Based on how the company’s assets are allocated, a high current ratio may indicate that the company is using its finances inefficiently.


  • One limitation of using the current ratio is that it cannot be efficiently used to compare different companies. Because operations between industries can differ largely, comparing this ratio between two different industries may not give an accurate figure.
  • Unlike many other liquidity ratios, it includes all of a company’s current assets, even those that cannot be easily liquidated. Hence a high current ratio may not necessarily mean that the company can pay off its debts easily.

Current Ratio= Current Assets/Current Liabilities

Data Analysis and Interpretation

The capital structure decisions of a company are subjective to a wide array of internal as well as external environment conditions. Through an industry specific study, we aim to study the factors affecting the debt to equity ratio of the sample firms and attribute them to all the companies in the food processing industry.

Our study consists of a sample of 8 food processing companies of India and their financial data has been collected and studied for a period of 10 years (FY 2008 to FY 2017). To get a more quantitative view of the entire sample, the descriptive statistics have been studied for the 10-year period.

Table 1: Sample Descriptive Statistics

DES. STATS Debt To Equity Ratio Liquidity Profitability Firm Size Tangibility Growth
Mean 1.1762 1.3437 0.0956 9.5291 0.3658 0.1781
Standard Error 0.1312 0.0589 0.0090 0.1628 0.0199 0.0274
Median 0.8451 1.2259 0.0921 9.9013 0.3792 0.0989
Standard Deviation 1.1735 0.5271 0.0813 1.4566 0.1784 0.2453
Sample Variance 1.3772 0.2778 0.0066 2.1217 0.0318 0.0601
Range 4.4451 2.0705 0.4255 5.7144 0.5790 1.2502
Minimum 0 0.6644 -0.1038 5.7789 0.1081 -0.0855
Maximum 4.4451 2.7350 0.3217 11.4933 0.6871 1.1646

Debt to Equity

The mean is 1.1762 which means that on an average the companies over a 10-year period had 1.1762 times outsiders’ funds to their owners’ funds. A minimum of 0 states that during at least one year, at least one company was debt-free (GSK Consumers Healthcare Ltd. had a zero debt to equity ratio for over 9 years out of 10) and a maximum of 4.4451 signifies a debt-burned year for one of the entities (LT Foods has had a higher percentage of debt over its equity in its capital structure, being a high growth company, it prefers debt to equity to expand operations).


Being a manufacturing industry, a part of the capital of the companies is invested into the the working capital budget. Hence a higher Current ratio of average 1.3437 signifies the higher requirement of current assets to finance the daily working of the company.


An average annual return on assets of 9.56% states that the overall management of assets is efficient as it generates positive net earnings. A negative minimum of -10.38% of Heritage Foods during FY 2008 could be accounted to its high interest payment burden due to the higher proportion of debt in the capital structure.

Nestle during 2008-10 gave an ROA of 32% p.a. proving the inelastic demand for the goods as the market leader was able to register a profit even during the aftershocks of the economic crisis.

Firm Size

Firm Size is calculated as the natural logarithm of sales, hence the mean, maximum and minimum have little to none economical interpretation. A standard deviation of 1.4566 and a maximum of 11.49 and minimum of 5.71 indicate large differences in size between the food processing companies in our sample.


An average of 0.3658 means that 36.58% of the total assets of the industry are fixed. This shows the huge investments in plant and machinery to carry out the production and distribution of consumer goods.


The average growth in Net Fixed Assets of 17.81% shows a constant expansion in the industry in terms of investments in fixed assets. This shows that the industry responds to the growing needs of the population by augmenting its production and distribution capacity.

To study the correlation between the dependent and independent variables and also the multicollinearity between the independent variables, a correlation coefficient matrix was constructed. Pearson’s coefficient of correlation explains how correlated are the variables to one another. The values lie between -1 to 1. A number close to 1 shows the high positive correlation between the variables and a number closer to 0 shows no correlation between the variables. Multicollinearity refers to the state where there is a high correlation between independent variables in a multivariate linear regression model. Highly correlated variables express the same information hence it is difficult to decipher the individual effects of each of the independent variables on the dependent variable. Statistically highly correlated independent variable are not preferred as they do not add any additional predictive power to the regression model. In regression modelling, it is difficult to find independent variables that are not correlated with each other. The highest correlation for the following sample is between Firm Size and profit (0.5688). Though this collinearity exists we will not omit any variable as the relation is moderately positive.

  Debt To Equity Ratio Liquidity Profitability Firm Size Tangibility Growth
Debt To Equity Ratio 1          
Liquidity -0.4067 1        
Profitability -0.6642 0.2472 1      
Firm Size -0.2324 0.1154 0.5688 1    
Tangibility -0.2339 -0.4625 -0.0369 -0.2417 1  
Growth 0.1306 -0.1920 -0.1176 -0.2136 0.2098 1

Table 2: Collinearity Matrix: Pearson’s correlation coefficient

To test our hypothesis, a multivariate regression analysis was considered with one dependent variable (Y) and 5 independent variables (X). The estimated results obtained from the different models are cited in the table below.

Adjusted R-squared depicts that fraction of the total sample variation on the response variable that is explained by the explanatory variables. The R- squared value ranges from 0 to 1 where 1 is obtained if the model explains 100% of the sample variation. Hence, it is preferable that the model obtain a high R-squared. In the above model, the adjusted R- squared value of 0.6710 states that 67.10% of variation in value of Debt to Equity Ratio is due to the 5 determinant ratios- Liquidity, Profitability, Firm Size, Tangibility and Growth. The F value is at 33.23 and is significant at confidence level 99%. The Multiple R[15] (Correlation coefficient) states the linearity of the data, it is a measure of degree of predictability of a variable attributed to a linear function of the multiple independent variables. The 0.8317 Multiple R proves that the data is highly linear. The interpretation of the coefficients of the variables is done under the assumption of ceteris paribus.[16]

Table 3: Multivariate Regression Model Summary

Determinants Standardized Coefficients
  Beta Significance (p-value)
Intercept 4.0408 1.62659E-06
Liquidity -1.0459 6.29367E-08
Profitability -8.419 1.1094E-09
Firm Size 0.0433 0.5223
Tangibility -3.0996 4.9223E-08
Growth 0.3789 0.2453
Multiple R 0.8317  
Adjusted R Squared 0.6710  
F Value 33.23  
F Value Significance 1.26288E-17  

Hypothesis Testing


A higher liquidity indicates that a firm has more current assets to pay off short term liabilities. Hence, we expect liquid firms to employ a greater degree of equity financing as the expected cost of equity is lower and, therefore, to have a lower target leverage.

The study discovered accordant results and showed that there is moderate negative relation between liquidity and capital structure (correlation -0.406, Beta -1.0459). The p-value of less than 0.05 indicates the significance of dependency of capital structure on the liquidity of the company.

Hence the null hypothesis is rejected and it can be concluded that liquidity negatively affects capital structure of an organization, i.e. a higher liquidity reduces the cost of equity for the company and hence equity is preferred over debt, reducing the overall debt to equity ratio.

  • Vadilal Ltd. has seen a declining trend in its debt to equity ratio from 3.26 in FY11 to 0.67 in FY17, this is mainly attributed to a rise in preference of equity over debt as the owner’s funds have nearly tripled since FY11. The company’s weak liquidity position is primarily because of its working capital mismanagement. Over the last 5 years (i.e., from FY2013 to FY2017), the company has similar liquidity ratios which indicate that all through these years Vadilal has been unsuccessful in improving its working capital management.
  • In the financial year 2016-17, KRBL had a current ratio of 1.79, which is an increase from 1.74, from the year 2015-16.  The company has strong liquidity position and therefore, it has a good ability to leverage operations and growth.
  • GSK Consumers has huge diversification in terms of products. However, being a zero debt company it has high levels of liquidity which is indicated by an average current ratio of 2.22 over the years.


A higher profitability increases the reserves of the company making it financially stable and wealthy. The firms usually take on debt to finance the capital intensive projects, when these companies are able to monetize these investments and create a steady income stream, they can use this surplus cash to pay off the existing debt and finance future projects. Low profitable firms tend to have lower retained earnings for future capital investments; hence, have to resort to more debt. This is in line with the Pecking Order Theory that argues that an increasing profitability increases the retained earnings available for deployment. Retained earnings being an internal source of finance is the cheapest way of funding new investments, high profitability implies less need of debt and equity and also lower degree of leverage.

The study’s results are harmonious to the above theory. It showed a moderately strong negative relationship between capital structure and profitability (correlation -0.6642, Beta -8.419). The p-value of less than 0.05 indicates the significance of dependency of capital structure on the profitability of the company.

Hence the null hypothesis is rejected and it can be concluded that profitability negatively affects capital structure of an organization due to availability of internal funds.

  • Nestle Ind. has been the market leader in the food processing sector and has been consistently profitable. In 2015, due to Maggi ban crisis the profitability of the company was hit hard dropping from 20% in 2014 to 9% in 2015. This forced the company to borrow funds through short term debt to fund its working capital and recovery phase hence increasing its debt to equity ratio from 0.0063 to 0.0110.
  • Heritage Foods suffered a period of low profitability with a loss in FY 2009. This lead to a depletion of its retained earnings, forcing it to borrow funds from the external sources. It resorted to the next cheapest source of financing- Debt, this lead to a substantial increase in the firm’s long term debt and hence also increased its Debt to Equity ratio to 2.142 in FY 2009. Since then, the company has stabilised in terms of sales and has been profitable in the past 4 years averaging near the industry average of 9.56% (ROA). This helped the company pay off a part of its debt and also raise funds from the equity market due to its positive fundamentals and increased market confidence.

Firm Size

The trade-off theory states that an increase in the firm size will decrease default risk. This will induce firms to resort to tax-free debt shield because of the low bankruptcy costs and also increase its debt to equity ratio. On the contrary, a bigger firm receives greater market confidence and can easily borrow money from the public by parting with its equity. This would increase the equity holding of the company and reduce its debt to equity ratio.

The study reveals a negative correlation and positive regression coefficients between size and capital structure (-.2324, Beta 0.0433) result in acceptance of the null hypothesis that firm size doesn’t affect the debt equity ratio as the p-value is substantially greater than 0.05 (p-value: 0.523) hence, the dependency is not statistically significant.

  • On one hand, from FY 2007 to FY 2012, LT Foods has seen as increase in its size from ln(sales) being 8.51 and 9.56 respectively complemented with an increase in debt to equity ratio from 2.43 to 4.445. On the other hand, Heritage foods saw a rise in its size from 9.54 to 10.18 in FY 2012 and FY 2017 respectively whereas it decreased its debt to equity ratio by a third from 1.5 to 0.5269 during the similar time period. This gives a view of the contradictory nature of the effect of firm size on capital structure decisions indicating their insignificant dependence.


Fixed and tangible assets can serve as a collateral to secured loans, improving the firm’s creditworthiness. Therefore, higher tangibility lowers the risk of a creditor and increases the value of the assets in the case of bankruptcy. This helps the companies to increase their tax-debt shields. Thus a positive relation between tangibility and leverage is predicted. Statistically this strong positive relationship is supported by both the agency and trade-off theory.

The study presents a contrarian view with a negative correlation and regression coefficients between size and capital structure (-.0.2339, Beta -3.0966). The p-value is significantly lower than 0.05 showing a statistically significant relation between capital structure and tangibility.

The result is rejection of the null hypothesis that tangibility doesn’t affect the debt equity ratio.

  • Britannia’s new R&D Centre being built in Bangalore is one of India’s largest food R&D centre with a massive investment of 450 million dollars is just one of the examples of the fixed and tangible asset capital expenditure being incurred by Britannia to expand its markets. But even though the Fixed Assets have increased over the years, the company has seen a reversal in the trend of it debt to equity ratio that has constantly declined since FY 2012.


Firms having high growth opportunities as stated by increase in their net fixed assets are expected to employ a greater proportion of shareholder’ equity, because a comparatively higher leveraged company would lead to a transfer of the wealth from shareholders to debt holders. Therefore, a negative relationship between firm’s growth and debt to equity is predicted. The trade-off theory states that, firms with more investment opportunities will have incentives to use less debt in order to avoid possible underinvestment issues. The negative relationship could also be explained by the pecking order theory as, when managers look into the firm’s future, they would prefer keeping a lower degree of debt in order to avoid new equity offerings when needing capital to potential investments.

The present study presents a divergent outcome, that Growth and Capital Structure have a weak positive relationship as depicted by a positive correlation coefficient and beta (0.1306, 0.3789).  This relationship is found to be statistically insignificant by a p-value of greater than 0.05. Hence the capital structure is independent of the firm’s growth in fixed assets in the above regression model.

We accept the null hypothesis stating that growth does not affect the capital structure decisions of the company.


Most investors believe that the goal in life is to be debt-free, but this ideology is not as simplified as it sounds in the corporate league. No theory can ever emerge with a specific benchmark debt to equity ratio as every capital structure is affected by a wide array of internal and external factors. A perfect debt mix is a relative term as no two companies are the same and no market situation is alike.

In our study we have aimed to understand the factors that affect capital structure decisions of companies from a specific industry. These determinants may have a varied impact when modelled for a different industry or country or time horizon.

From our empirical study we have observed that three out of five coefficients namely; liquidity, profitability and tangibility have a statistically significant negative dependency relationship with the debt to equity ratio whereas the firm size and growth of fixed assets do not significantly affect it. Theoretically, the expected relationship between growth and debt to equity is negative for developed countries. On the contrary, the relationship is positive for developing countries which was proved insignificant by the study. These independent variables were able to account for 67% of change in the dependent variable proving that the model is a relatively good fit.

We can also conclude that neither the pecking order nor the trade-off theory were able to explain all the five results, hence none of the models can be ranked superior or perfect. The impact of determinants on capital structure also highlights the limitations of the irrelevancy model of capital structure as determined by the M-M Model.

[1] Weighted average cost of capital (WACC) is the average rate of return a company expects to compensate all its different investors. The weights are the fraction of each financing source in the company’s target capital structure. (Investopedia)

[2] Retained earnings refer to the percentage of net earnings not paid out as dividends, but retained by the company to be reinvested in its core business, or to pay debt. It is recorded under shareholders’ equity on the balance sheet. (Investopedia)

[3] E.g. Convertible Debentures

[4] Data: IBEF- Food Processing Industry & Grant Thornton

[5] In statistics, ordinary least squares (OLS) is a method for estimating the unknown parameters in a linear regression model, with the goal of minimizing the sum of the squares of the differences between the observed responses in the given dataset and those predicted by a linear function of a set of explanatory variables.

[6] Non-debt Tax Shield

[7] Ordinary Least Squares

[8] Refer to Annexure 1

[9] Extreme/deviant case sampling is used when a researcher wants to study the outliers that diverge from the norm as regards a particular phenomenon, issue, or trend. By studying the deviant cases, researchers can often gain a better understanding of the more regular patterns of behaviour. (Source: Investopedia)

[10] A purposive sample is a non-probability sample that is selected based on characteristics of a population and the objective of the study. Purposive sampling is also known as judgmental, selective, or subjective sampling. (Source: Investopedia)

[11] A representative sample is a small quantity of something that accurately reflects the larger entity. (Source: Investopedia)

[12] Multivariate regression is a technique that estimates a single regression model with more than one outcome variable.

[13] Objective of the research

[14] Specifically, a set of data becomes statistically significant when the set is large enough to accurately represent the phenomenon or population sample being studied.

[15] The coefficient of multiple correlation is computed as the square root of the coefficient of determination, The coefficient of multiple correlation takes values between 0 and 1; a higher value indicates a better predictability of the dependent variable from the independent variables, with a value of 1 indicating that the predictions are exactly correct and a value of 0 indicating that no linear combination of the independent variables is a better predictor than is the fixed mean of the dependent variable. (Source: Investopedia)

[16] All coefficient interpretations are made under the assumption of ceteris paribus (all other variables are kept constant).

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