Editorial Board

A Refereed Monthly International Journal of Management

An Empirical Investigation on the Association between the Leverage of Indian Unicorn Firms and Their Financial Performance

Priyanka

Research Scholar,

HSB, Guru Jambheshwar University of Science & Technology,

Hisar, Haryana.

priyankarunach0803@gmail.com

Karam Pal Narwal

Professor,

HSB, Guru Jambheshwar University of Science & Technology,

Hisar, Haryana.

karampalhsb@gmail.com

Abstract

The present research attempts to investigate how leverage affects the unicorn startup’s performance in India by employing the panel data methodology. For this study a sample of 25 unicorns is selected from the list of unicorns belonging to two startup hubs, first one is Delhi -NCR and second Bangalore, spanning the years 2016-2017 to 2020-2021. The findings revealed that leverage has significantly reduced startup performance as indicated by the financial ratios return on assets and return on equity. The other firm-specific variables such as firm age and liquidity are found to be playing a significant role in influencing the firm performance specifically if it is calculated through return on assets. The present study will enrich the body of literature by providing the groundwork for further studies in this area.. Moreover, it will also help the potential entrepreneurs, small business owners and managers in taking their financial decision efficiently which will consequently help in improving their financial performance for their long run survival.

Keywords: Firm Performance; Unicorns, Leverage; India; panel data.

Introduction

Entrepreneurship is a crucial component of industrial development and serves as backbone of a country's economic development. Start-ups are the engines of economic growth (Deloof and Vanacker, 2018). They help the country’s economy in various ways like creating employment opportunities, more innovation, more wealth, improving the economy and enhancing the GDP of the nation. Start-up is a newly formed company started by one or more entrepreneurs and which is in its initial stage of development, aiming at developing an innovative product or service, validating it by bringing it to the market and that provide a solution to some existing problem faced by people in real life. The business model developed should be capable of being scaled up. Start-ups are the crucial elements for the economic growth and development of any country. The cornerstone for any country’s economic growth and development is its startup culture. To conduct all the activities of start-ups efficiently, the financing sources are needed. Finance is one of the essential elements required for the proper growth and development of these businesses. There are multiple sources of financing options available for smaller businesses. They include bootstrapping, bank finance, venture capital, angel financing, private equity, etc. The majority of capital at the initial stages for small businesses came from insiders such as the entrepreneurs themselves, the start-up team members, family and friends, and so on (Berger and Udell, 1998, Calopa et al. 2014). An another study conducted by Gartner et al. 2012 have also demonstrated that the main source of finance for venture development in initial phase is the entrepreneur’s own individual contributions and later on they derive finance from external sources of capital.

It also have been demonstrated that the initial financial decisions significantly affect a company’s performance, survival, its operating activities, risk of failure, and potential for growth as well (Cassar, 2004). Some of the prior studies such as (Krishnan and Moyer 1997, Abor 2005, Zeitun and Tian 2007, Chadha and Sharma 2015, Mittal and Madan 2018, Thanh CO et al. 2021, Islam and Iqbal 2022) examined how financial choices affect the performance of firms among countries. Various researches have reported that leverage significantly negatively affects a company's performance (Zeitun and Tian 2007, Sekabira 2013, Rajamani 2021, Islam and Iqbal 2022), however, certain studies showed that leverage enhance the performance of businesses (Abor 2005, Thanh CO et al. 2021). On the contrary, some studies have found no significant impact of financing choice on firm performance measured using ROA (Chadha and Sharma 2015) ROE and gross margin (Ebaid 2009). Along with leverage, in the existing literature, we also found that the firm performance gets significantly influenced by firm characteristics as well such as firm size, sales, growth, asset tangibility, liquidity and business risk etc.

Most of these studies are related to listed firms and well-established SMEs but there is a scarcity of research on start-up, especially for unicorn firms. Hence, there is a need to research how leverage impacts the performance of unicorn firms. In this present study, we attempt to identify the factors which determine performance of unicorn firms in developing countries like India where institutional, and organizational structure differ a lot from developed nations. A sample of 25 unicorn companies from the Bangalore and Delhi NCR are included in the study.

The research paper starts with Section I introduction and objective of the current research, section II reviewed the literature on the topic of capital structure and performance of businesses. Section III explained the research methodology used, the findings and implications of the current research are discussed in section IV and lastly the Section V contain the conclusive part of the study. 

Literature review

This section of the paper aims at to covers the studies globally conducted which are focused on the area of capital structure choice, its impact on performance of firms are briefly described hereunder.

Cressy (1996) explored the association between survival and financial capital, survival and human capital, and whether both financial and human capital explained startup survival and whether there was any credit rationing of startups in the UK. According to the findings, there was a spurious correlation between survival and financial variables, and survival was largely explained by the variable of human capital. Owner age and team size were discovered to be the most important human capital variables. The decisions regarding the provision of finance were discovered to be demand-driven, with the bank elastically providing funds when startups approached them for funds, implying that startups self-select for bank finance based on the human capital they possess and firms with more human capital were more likely to use the funds, so there was no startup credit rationing.

Krishnan and Moyer (1997) investigated the capital structure and business performance of major Asian emerging countries such as Malaysia, Korea, Hong Kong and Singapore. They showed that debt-to-equity ratio has significantly reduced the return on equity and return on invested capital of these firms, and also further determined that a country of origin has an impact on both company’s financial mix and business performance too.

Davila (2003) examined whether venture capital-backed firms grow more quickly than non-venture capital-backed startups and whether the startup's stock value increased over the course of subsequent investment rounds. The results showed that employment rates grew both before and after the venture capital investment event, implying that VC funding events were important indicators of the startup's quality. The increase in the number of employees has boosted the valuation of startups in subsequent rounds of financing. Startups that received venture capital funding grow faster than startups that did not received venture capital funding.

Abor (2005) studied how debt composition in capital structure influenced the listed firm’s profitability of Ghana (1998-2002) by utilizing the regression analysis, and where the profitability was measured by ROE. The results showed that TD, STD both has significantly improved ROE, while LTD has shown negative relationship. Profitability was discovered to be positively correlated with sales growth and firm size. Using panel data analysis Zeitun and Tian (2007) analyzed the financing decisions of the listed firms of Jordan from 1989-2003 and investigated its impact on firm performance too. The research has considered accounting (ROE, ROA) and market measures (P/E ratio, Tobin's Q and MBVR) to represent the firm performance. The findings demonstrated that all debt metrics had worsens the firm performance which was measured in terms of ROA and Tobin's Q here. The other firm variables such as firm size, growth and tax have positively influenced these firm performances while risk and tangibility have impacted them negatively.

Pouraghajan et al. (2012) used multiple regressions to investigate the association between listed Iranian companies performance and capital structure between 2006 and 2010.The ROA and ROE of the firm are found to be significantly negatively impacted by debt to asset ratio. Firm age is determined to be insignificant and all other variables like growth opportunities, tangibility, asset turnover and firm size are having a positive impact.

Sekabera (2013) investigated the role that capital structure plays in influencing the efficiency of micro financial institutions. The findings suggested that government policy needs to restrict MFI’s utilization of grants and borrowing as they degrade their operational and financial sustainability, while share capital and assets boost it. Chadha and Sharma (2015) explored the financing decision of listed manufacturing firms of India and assessed their performance. For analyzing the financial data (2003-2013), panel data methodology and ratio analysis are used. The measures used for evaluating the firm performance are return on equity Tobin's Q and return on assets. The research outcomes suggested that firm’s leverage has significantly reduced the listed firm performance measured using ROE while there was no significant impact found for ROA and Tobins-Q. The other factors such as tangibility, asset turnover, sales growth, firm size, firm age and firm’s ownership structure have shown a positive impact on firm performance.

Cole and Sokolyk (2017) used data from the Kauffman firm survey to investigate how different types of debt financing at firm startups affect firm survival and subsequent revenue growth (2005-2012). Multivariate probit model, Cox proportional hazard model, and multivariate weighted least square regression were used as methodologies. It distinguished between business and personal loans received by startup companies to better analyze the lender screening and monitoring effect which are involved in startup credit financing. It was found that 55% of startups in the data have been funded by personal loans given to firm founders rather than to the business, and that 76% of startup enterprises use credit in some form during their first year of operation. While low-quality firms are less inclined to be able to do so, high-quality firms have a higher chance of obtaining business loans.

Mittal and Madan (2018) study has looked into how the financial patterns affected the profitability of Indian e-startups operating in several sectors between 2012 and 2016 using panel data regression. The results showed that enterprises use a variety of funding sources to support their startup endeavors. Both LTD and STD had a negative relationship with profitability, while the LTD's effect was negligible. Equity and profitability had a positive relationship. For both profit metrics, STD and equity exhibited about similar relevance. In particular, LTD has little effect on ROE but an immense effect on ROA.

Sunardi et al. (2020) examined the factors determining the firm’s debt policy and performance of listed retail sector companies in Indonesia using regression analysis from 2010-2019. The results showed that firm size and business risk have significantly positively influenced the debt policy, while liquidity and asset growth have shown a positive but less significant impact on the firm’s debt policy. The factor asset growth has positively influenced the performance of these companies, whereas liquidity and firm size have negatively impacted company performance, the variable business risk is found as positive but is statistically insignificant. Rajamani (2021) studied the role of debt financing policy in influencing the performance of SMEs in India. The findings indicated that the efficiency of small and medium-sized businesses is reduced as indicated by its negative relation with STD and LTD.

Thanh CO et al., (2021) attempted to gain insight into how debt level affected profitability of Vietnamese listed companies by using regression analysis on data from 18 listed rubber companies during 2015- 2019. The results have shown that debt to asset ratios improved ROE of the firms while the ratio of long-term debt to total debt has reduced it. The variables liquidity and tangibility are found insignificant to the firm’s profitability while firm size and sales growth have shown a positively influenced it.

Islam and Iqbal (2022) investigated how capital mix affected the performance of listed companies of Pakistan by taking a larger time frame of 21 years. A two-step GMM approach is used for the analysis. The result exhibited a adverse effect of leverage on Pakistani firm’s performance and that this relationship is moderated in the same direction by the firm’s size.

Based on the literature study, we found that several studies have been carried out in developed and developing countries so far regarding the effect of capital structure on corporate performance. But there is a scarcity of research on the factors influencing small and new firm's financial performance. So the present study adds an understanding of existing entrepreneurial literature about the association of financing choice and firm performance in the startup firm context.

Research Data and Methodology

Sample Selection

The study sample has been taken from the unicorn startup’s list available at Invest India website. Their financials have been taken for a period of the latest 5 years from 2016-2017 to 2020-2021 for investigating how leverage affects the success of unicorn startups in India.  The data has been extracted from Ministry of Corporate Affairs (MCA) website and Tofler database. The firms having financial of less than five years or belonging to a region other than Delhi NCR and Bangalore hub are excluded. All those firms are excluded from the current samples which have any missing data for computing the variables of the current study. Therefore, after using each of these critical selection criteria, the study has taken a final sample of 25 unicorn firms for conducting the present study.

Variables Measurement

The different variables along with the proxies that have been employed to measure them based on the study are listed in Table 1. The firm performance used as the dependent variable in this current research. The main independent variable leverage is computed as the total debt-to-equity ratio here. The rest of the independent variables assumed to be significantly influencing the firm performance are: firm age, firm size, tangibility of assets liquidity firm growth and business risk.

Table 1 Description of Variables

Model specification

For achieving the primary objective of the current study, we have employed panel data methodology as because the research data possess both time series and cross-sectional characteristics. The panel model of the present study can be stated as:

Where,

= net profit after tax divided by equity shareholders of firm i at time t

= net profit after tax divided by total assets of firm i at time t

= debt to equity ratio

= age of the firm

= size of the firm in terms of assets

 = a natural log of net sales

 = growth in total assets

 = growth rate of sales

 = asset turnover ratio of firm

 = riskiness in firm

 = residual component

Empirical Findings and Interpretation

Descriptive statistics

Table 2 displays a descriptive overview of each of the independent and dependent variables that has been taken into consideration for the purpose of this study. The company's average profitability is presented through average ROA for the period 2017-2021 is -43%,and the average return measured using ROE is -1.12, which is also found negative, depicting the poor performance of startups in terms of profitability, the results indicated that startup firms are suffering from losses. The mean of the debt-equity ratio is 0.58 which shows that firm uses a higher proportion of debt as compared to equity in its financial structure. The average asset tangibility is 0.08 it is so as the sample involves more service firms, the firms are 7.5 years old on average. The liquidity of firms is found to be high. The average sales growth value is 1.91 indicating significant growth in sales.

Table 2 Descriptive Statistic

Author’s computations

Pearson’s correlation matrix

The prerequisite for the application of panel regression model is the absence of multicollinearity to be there in the explanatory variables of the study. Therefore, the study has applied the Pearson correlation matrix as presented in Table 3 for checking the multicollinearity among the variables.

Table 3 illustrates the coefficient values of all the variables which is found to be less than.70 representing that there is no problem with multicollinearity in the independent variables of this research.

Table 3. Results of Correlation matrix

Source: Author’s calculations

Regression analysis

We have three regression models in the panel data approach: pooled, fixed effect and random effect models. Firstly, we are going to find the best model for regression equation 1. To choose the best model among the three of them we have applied some tests. The Breusch Pagan test is the first test that is used to decide whether POLS is preferable to the random effect model. The p-value is found to be statistically significant (χ2 13.28, P-value 0.00), so the null hypothesis is not accepted here. The Hausman test is the second test that we have performed here. It will determine whether the random effect model is better than the fixed effect model. The value (χ2 6.95, P-value 0.54) indicates that the null hypothesis is accepted in the present case. Thus, as Table 4 explains, the random effect model is determined to be the most optimal model.

These both tests are then run for regression equation 2, the table 4 shows that Breusch pagan test p-value is significant (χ2 15.62, P-value 0.00). So here, the null hypothesis is not accepted means POLS is not found better than random effect model. Afterward, to ascertain if the random effect model is better than the fixed effect model for this current research, the Hausman test was conducted. The p-value is found to be statistically significant (χ2 15.86, p-value 0.04), indicating that the null hypothesis is not accepted here of the Hausman test as random being better. As a result, we conclude that equation 2 is best represented by the fixed effect model. The results for this are presented in table 4.

The R² values for equation 1 and equation 2 are 30 % and 58% respectively, representing the overall effect of all the independent variables taken under this study to explain the dependent variable. The Durbin-Watson values for both the regression equations are 2.26 and 2.22 respectively, which is found within the range of 1 to 3 which means there is no autocorrelation present in the residuals of the panel regression model.

According to the regression results, the Debt equity ratio is found to be negatively significant to startup firm’s performance. Among the other independent variables firm size is found to be the only statistically significant variable and it is influencing firm performance positively.

Table 4 Results of regression models

Note: The significance levels 1, 5, and 10% are shown by ***,**,* respectively.

Moreover, a negative relationship is revealed in between ROE and the firm leverage. It indicates that if a firm is using a greater proportion of debt in its capital structure then it will result in a lower return available to its equity shareholders as illustrated by significant p-value. The result is found in accordance with prior studies (Krishnan and Moyer 1997, Chadha and Sharma 2015, Pouraghajan et al. 2012, Islam and Iqbal 2022).  The other firm specific factors are not found significant here. Some of the empirical studies also reported them as insignificant Among these firm age, firm size, asset turnover are found insignificant in the study of Chadha and Sharma 2015. Liquidity and tangibility are found to be having no relation with the firm performance (Thanh CO et al. 2012). Business risk is found insignificant by (Sunardi et al. 2020). Firm size, growth and tax rate are found as insignificant by ( Krishnan and Moyer 1997).

While analyzing regression equation 2 we have found that leverage has significantly reduced ROA of the sampled unicorn startups. It shows that an increasing debt burden reduces the firm's profitability. As in the initial phase startups rarely makes a profit and as much profit they make it will be absorbed in interest by debt holders. Therefore, debt level leads to poor performance of such newly founded firms. This negative relation between firm's profitability and leverage is found inconsistent with the various previous empirical studies (Zeitun and Tian 2007  Pouraghajan et al.2012, Rajamani 2021, Islam and Iqbal 2022).

Among the various independent variables, firm size is found to be significantly positively influencing the firm's profitability (ROA). Larger firms indeed often have the several benefits of diversification, economies of scale and have a greater access to resources which help a firm in generating more profits and help them to run their business smoothly. Various studies like Zeitun and Tian 2007,     Pouraghajan et al. 2012, Thanh CO et al. 2021 also found it significant in their studies for Jordanian firms, Iranian firms and Vietnamese firms respectively. All other variables of the study like firm age, firm growth, tangibility, asset turnover ratio, liquidity and business risk are found to be insignificant. In contrast in some of the studies, these variables have played an crucial role in stimulating performance of corporate firms.  In the study of Chadha and Sharma 2015 for listed manufacturing firms, these variables (firm size, sales growth, asset turnover ratio, firm age, tangibility) are found to be significantly impacting the financial performance. Zeitun and Tian (2007) discovered in another study that firm-specific factors including tax rate, growth, tangibility and size have an immense impact on the entity’s performance.

We can summarize the results here, that a higher leverage level deteriorates the startup firm's performance in the sampled period. The firms that we have studied in this research, are mostly the younger firms, at their initial stage of development, the debt-equity mix may be the first and foremost important variable having an impact on their firm efficiency, maybe later on these insignificant factors may be playing a significant role in determining their firm success. Currently, along with leverage, some other variables may be at work that has influenced these startup firm performances that we need to identify in the further research. The study implies that startup entrepreneurs need to take their financial decision very cautiously, as it will determine their future performance.

Conclusion

The current research aimed at to examine whether leverage affects the performance of unicorn startups in India or not. This research revealed that the amount of debt held by the unicorn startup firm has adversely affected the unicorn startups in India in terms of their financial performance. For each of the company performance metrics, the debt to equity ratio is found to be statistically negatively significant (ROA and ROE). Firm size is the only variable found significant among the firm-level factors. It is positively related to ROA which indicated that larger firms have a higher capacity to make a profit due to prior well-established records and reputation, and they have easier access to raise funds, and with time it is possible for them to accumulate profits which thereafter they can use for financing the business operations, so larger firms are at a better position than that of financially constrained smaller firms. This study will be helpful for potential startup and small business owners in deciding their appropriate financing mix which is needful for them for their long-term survival and success. It will assist them to focus on the factors that significantly influence their firm performance. Moreover, this study will also act as a guide for researchers, policymakers and governments for formulating their future plans and policies for unicorn financing in India. 

References

Abor, J. (2005). The effect of capital structure on profitability: an empirical analysis of listed firms in Ghana, The Journal of Risk Finance, 6(5), 438-445.

Berger, A.N., & Udell, G.F. (1998). The economics of small business finance: The roles of private equity and debt markets in the financial growth cycle. Journal of Banking & Finance, 22(6-8), 613-673.

Cassar, G. (2004). The financing of business start-ups. Journal of Business Venturing, 19(2), 261-283.

Chadha, S., & Sharma, A. K. (2015). Capital structure and firm performance: Empirical evidence from India, Vision, 19 (4), 295-302.

Co, H. T. T., Uong, T. T. M., & Nguyen, C. V. (2021). The Impact of Capital Structure on Firm’s Profitability: A Case Study of the Rubber Industry in Vietnam, The Journal of Asian Finance, Economics and Business, 8(7), 469-476.

Cole, R. A., & Sokolyk, T. (2018). Debt financing, survival, and growth of startup firms. Journal of Corporate Finance, 50, 609-625.

Cressy, R. (1996). Are business startups debt-rationed? The economic journal, 106(438), 1253-1270.

Davila, A., Foster, G., & Gupta, M. (2003). Venture capital financing and the growth of startup firms. Journal of business venturing, 18(6), 689-708.

Deloof, M., & Vanacker, T. (2018). The recent financial crisis, start-up financing and survival. Journal of Business Finance & Accounting, 45(7-8), 928-951.

Ebaid, I. E. S. (2009). The impact of capital‐structure choice on firm performance: empirical evidence from Egypt, The journal of risk Finance, 10(5), 477-487.

Gartner, W.B., Frid, C.J. and Alexander, J.C. (2012) ‘Financing the emerging firm’, Small Business Economics, 39(3), 745-761.

Islam, Z.U. & Iqbal M.M. (2022). The relationship between capital structure and firm performance: New evidence from Pakistan. The Journal of Asian Finance, Economics and Business, 9(2), 81-92.

Klacmer Calopa, M., Horvat, J., & Lalic, M. (2014). Analysis of financing sources for start-up companies. Management: journal of contemporary management issues, 19(2), 19-44.

Krishnan, V. S., & Moyer, R. C. (1997). Performance, capital structure and home country: An analysis of Asian corporations. Global finance journal, 8(1), 129-143.

Mittal, T., & Madan, P. (2018). Impact of Financing Patterns on Profitability of eStartups in India: A study of selected organizations. International Journal of Research in Economics and Social Sciences), 8(3), 390-399.

Pouraghajan, A., Malekian, E., Emamgholipour, M., Lotfollahpour, V., & Bagheri, M. M. (2012). The relationship between capital structure and firm performance evaluation measures: Evidence from the Tehran Stock Exchange. International journal of Business and Commerce, 1(9), 166-181.

Rajamani, K. (2021). “Debt Financing and Financial Performance: Empirical Evidence of Indian SMEs Listed in BSE-SME Platform”, Eurasian Economic Perspectives, Vol 16 Issue 1,pp 217-230

Sekabira, H. (2013). Capital structure and its role on performance of microfinance institutions: The Ugandan case. Sustainable Agriculture Research, 2(3).

Sunardi, N., Husain, T., & Kadim, A. (2020). Determinants of Debt Policy and Company’s Performance. International Journal of Economics and Business Administration, 8(4), 204-213.

 Zeitun, R., & Tian, G. G. (2014). Capital structure and corporate performance: evidence from Jordan. Australasian Accounting Business & Finance Journal,1(4), 40-61.