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The purpose of this paper is to examine the nature of the relationship between business risk and financial leverage. While past theoretical and empirical studies on this topic use similar variables, overall, their findings are inconclusive. In this paper, the author contends this is partially due to inappropriate proxies for business risk that are commonly used in these research papers. To correct for this misspecification, this paper proposes an alternative proxy for business risk that is isolated from the effects of financial leverage.

Past research on the relationship between business risk and financial leverage uses some variations in a firm’s operating cash flow as a proxy for business risk. This proxy cannot solely reflect business risk and may very well be affected by the level of financial leverage, especially for financially distressed firms. This paper proposes an alternative proxy for business risk that is isolated from the effects of financial leverage. This proxy is the cost of capital of an all-equity firm. The theoretical model developed in this paper is based on deriving the optimum level of debt as a function of business risk in the context of the Modigliani and Miller Proposition II model.

The findings show a positive linkage between business risk and financial leverage. This relationship is robust to the various forms the cost of financial distress function may take.

The mixed findings in past research papers regarding the relationship between business risk and financial leverage are mainly due to “inappropriate” measures of business risk that do not only reflect one firm attribute and are contaminated with other factors mainly financial leverage. As such, since the variable of interest is misspecified, the outcome of these studies cannot be credible. This paper attempts to correct for such misspecification by proposing a proxy that only reflects business risk. In addition, the proposed model is based on the widely acceptable Modigliani and Miller static theory of capital structure.

This paper aims to seek answers to a primary question: “How much do divergent leverage factors account for fluctuations in time-varying financial leverage in leading hospitality sub-sectors decomposed by four exclusive sub-portfolios?” In the path of seeking answers, this paper investigated the effects of both firm-specific and macroeconomic indicators to firms’ varying financial leverage in those primary sub-sectors overtime.

In each sub-sector portfolios, firms were sorted based on market-to-book values (Mktbk _it ) with median breakpoint percentiles. For hypothesis testing, this paper constructed panel regression models with firm fixed-effects to layout fluctuant financial leverage phenomenon engaged with a set of 11 leverage factors in each Mktbk _it sorted sub-sector portfolios.

Results exhibited assorted evidences. The bottom line was: firms with different market capitalization rates in each portfolio acted differently in regard to the magnitude of financial leverage across time.

The final sample of 415 firms in four sub-sector portfolios sufficiently embraced financial leverage composition in the hospitality industry across time. However, by reason of lack of data in the other intra-hospitality industries, such as gaming and/or cruise lines, findings did not represent the firms operated in those sub-industries.

This paper departed from the established context of the previous literature in the manner that it expects to add to the literature by demonstrating the core drivers causing the deviations in financial structure in four exclusive, hospitality industry sub-sector portfolios with varying leverage proxies overtime.

This study attempts to document the impact of financial leverage on corporate innovation in the Chinese nonfinancial public firms listed on Shenzhen and Shanghai stock exchanges.

The firm-level data are collected from CSMAR database for ten years, ranging from 2007 to 2016. The authors have employed the panel fixed effects model and further system GMM approach for analysis. The sample is segregated on the basis of state (SOE) and nonstate ownership (NSOE) to check for the diverse effects. In total, three different proxies of financial leverage are used to unearth the varying impact of short-time and long-term leverage separately. Further, corporate innovation is divided into input innovation (R&D/Sales and R&D/Assets) and output innovation (patents and inventions).

The results suggest that financial leverage is detrimental to the input innovation while conducive for the output innovation when measured by the number of patents. Contrarily, leverage has a negative influence over the output innovation when measured by the number of inventions. This implies that leverage is more damaging for the highest form of innovativeness (inventions) in China. Input innovation is more sensitive to the changes in long-term leverage versus short-term leverage. Further, the authors find that innovation in SOEs is more sensitive to the changes in the leverage as compared to the NSOEs. The results are free from the threat of endogeneity and identification problems, as reported by the system GMM model.

The authors did not segregate the sample on the basis of industry/sector.

The firms pursuing a strategy of radical innovation should try to keep their debt levels lower in order to achieve a higher innovation performance. Although, a rise in the leverage may mean an increased access to finance for a firm but such an access comes at a cost in the form of damage to the corporate innovation. However, increased debt financing may not be so bad for the firms that want to achieve a moderate and not the highest level of innovation. Such firms can produce recurring and synergic effects with debt financing and moderate innovation, once they achieve a level of innovation performance that satisfies their financiers.

To the best of authors’ knowledge, this is probably the first study to check the impact of firm-level financial leverage on both input and output innovation in the Chinese public-listed nonfinancial firms' panel data perspective till now.

The study seeks to find the effect of financial leverage on the firm performance of non-financial companies listed in the Tokyo stock market.

The study collected data from 263 companies in the automobile and industrial producer sectors listed on the Tokyo stock exchange between 2001 and 2021. The generalized method of moments was used to estimate the effect of leverage on financial performance due to its ability to overcome the problems of endogeneity and autocorrelation.

The study found that the equity multiplier has a positive and statistically significant effect on return on assets (ROA), return on equity (ROE) and earning per share (EPS). The study discovered that the interest coverage ratio has a positive and statistically significant effect on ROA, ROE, EPS and Tobin’s Q. The results revealed that the degree of financial leverage and debt to earnings before interest, taxes, depreciation and amortization (EBITDA) have a negative and statistically significant effect on ROE, EPS and Tobin’s Q. The study also found that the capitalization ratios of the firms have a negative and statistically significant effect on ROA, ROE, EPS and Tobin’s Q.

The use of debt financing, which presents financial leverage, indicates that the companies can make enough earnings to pay off the interest and principal (debt service obligations), which were shown by the interest coverage ratio, as well as to pay all the long-term fixed expenses, which were shown by the fixed charge coverage ratio. Interest and fixed charge coverage have a positive statistically significant effect on the financial performance of automobile and industrial producer companies.

The study focused on the effect of financial leverage on financial performance by relying on pecking and trade-off theories to contribute to the existing body of literature in finance.

The agency theory predicts that there are conflict of interests between managers and shareholders over free cash flow and major operating decisions. Earnings management can help managers hide and retain their private benefits of control. Given that, the purpose of this study is to investigate whether financial leverage reduces agency and information problems caused by earnings management.

The research uses a sample of annual data of 200 firms listed on the Tehran Stock Exchange during 2002-2016. The data required is obtained from the Rahavard Novin database. The research uses multivariate regression models that regress financial leverage on earnings management proxies and other determinants of capital structure.

The research documents that firms with higher income smoothing and the absolute value of discretionary accruals, as the proxies for earnings management, have higher financial leverage. The results suggest that a higher level of financial leverage can discipline managers and generate useful information about firm quality.

The study highlights the informational and disciplining role of debt in the presence of severe uncertainty about firm quality in a developing country.

The fundamental relationship between accounting variables and firm valuation is a recurring theme in capital market research. This paper investigates this relationship within a balance sheet context and highlights the importance of controlling for relevant economic factors. We do this by conditioning explanatory power on the firm's relative financial leverage position, after controlling for cashflows and firm size, and using an arctan regression model to take account of temporary components in cash and earnings flows. Using data for 743 firm‐years for Australian Stock Exchange listed stocks, we find that for firms which are ‘above optimal leverage’: (i) earnings contain a greater level of transitory items, particularly when firm size is small; and (ii) cashflows provide higher incremental information. Our results are consistent with investors perceiving earnings as progressively less informative as the probability of failure increases, and the likelihood of earnings manipulation for the purpose of reducing proximity to debt covenants increases.

This study examines financing decisions by Vietnamese firms and compares the results with the findings observed in economies characterized by market mechanisms and property rights. It uses data from Vietnamese enterprises census 2002–2003. Similar to findings in other countries, financial leverage of Vietnamese firms increases with firm size and managerial ownership and decreases with profitability, and with non-debt tax shield. It is also correlated with industry characteristics. Financial leverage was negatively correlated with fixed assets and positively correlated with growth opportunities, contrary to the findings in other countries. Corporate income tax has a negative, albeit small effect on financial leverage.

The purpose of this paper is to study the key determinants of capital structure for Indian manufacturing firms and which theory implications, i.e. trade off vs pecking order are more applicable in current Indian manufacturing sector scenario.

A sample size of 422 listed Indian manufacturing companies on Bombay Stock Exchange has been considered to do the empirical evaluation. A ten year period from 2003-2004 to 2012-2013 and annual financial standalone data have been considered for study. Ratio analysis and panel data approach have been applied to perform the empirical evaluation. Total debt to total capital and total debt to total assets are used as the proxy for firm financial leverage.

It was empirically found that size, age, asset tangibility, growth, profitability, non-debt tax shield, business risk, uniqueness and ownership structure are significantly correlated with the firm financial leverage or key determinants of capital structure in Indian manufacturing sector. Also, other variables like dividend payout, liquidity, interest coverage ratio, cash flow coverage ratio (CFCR), India inflation and GDP growth rate are empirically found to be insignificant to determine the capital structure of Indian manufacturing sector. There is no single theory implications, i.e. trade off vs pecking order which can explain the capital structure nature of Indian manufacturing sector and rather it is a mix of both the theories.

The findings of the study would enhance the literature on capital structure and is significant for the Indian manufacturing firm’s decisions as it includes the most recent data and covers the period of both pre- and post-recession of 2008-2009.

The real estate sector in India has assumed growing importance with the liberalisation of the economy. Developments in the real estate sector are being influenced by the developments in the retail, hospitality and entertainment (e.g. hotels, resorts and cinema theatres) segment, economic services (e.g. hospitals, schools) and information technology-enabled services (such as call centres), and vice versa. This paper aims to study the determinants of capital structure by taking into account 125 major Bombay Stock Exchange (BSE) listed real estate companies selected on the basis of their market capitalisation.

To discover what determines capital structure, nine firm level explanatory variables (profitability-EBIT margin, return on assets, earnings volatility, non-debt tax shield, tangibility, size, growth, age debt service ratio and tax shield) were selected and regressed against the appropriate capital structure measures, namely, total debt to total assets, long-term debts to total assets, short-term debts to total assets, total liabilities to total liabilities plus equity, total debt to capital used and total debt to total liabilities plus equity. A sample of 125 real estate companies was taken and secondary data were collected. Consequently, multivariate regression analysis was made based on financial statement data of the selected companies over the study period of 2009-2015.

The major findings of the study indicated that profitability, size, age, debt service capacity growth and tax shield variables are the significant firm-level determinants.

The present study is carried out by taking data of only 25 companies listed on the BSE and time period covered from 2009 from 2015. Time period and sample size may be limitations of the current study.

The present study is an empirical analysis of the determinants of leverage of real estate sector in India with most recent available data. Different regression equations have been formed to develop the models using firm-specific determinants and different measures of leverage or capital structure. Data were regressed using SPSS application software, and the resulting (or obtained) regression outputs are analysed. This study will help the Indian real estate companies to the know the impact of different variables while raising short-term and long-term loans.

The current study will benefit all stakeholders of society who are fascinated to be acquainted with the financing of real estate companies and the factors affecting long-term and short-term financing of this sector. Specifically, public engrossed in different modes of investment and financial institution will be the prime gainers.

The present study has been completed using authentic data from the annual reports and database. This study uses explanatory variables and different measures of leverage which were limited in use in previous studies. Moreover, this research is a comprehensive study that deals with developing different regression models by using diverse measures of leverage.

The purpose of this study is to look at the effect of financial leverage on the performance of Saudi listed companies. It particularly proposes to examine the heterogeneity of this relationship depending on firm profitability and firm size.

The paper uses a sample of 120 nonfinancial companies listed on the Tadawul stock exchange during the period 2017–2020. Data is obtained from the companies’ financial reports. This study uses the system GMM and the quantile regression. The first methodology examines the effect of leverage decisions on firm performance, whereas the second one tests the heterogeneity of this relationship.

GMM results demonstrate the adverse effect of leverage on firm performance in terms of return on assets, return on equities and Tobin’s Q. Besides, quantile regression results show that this relationship is heterogeneous. Particularly, leverage seems to have a greater adverse effect on the performance of high-profitable firms than low-profitable firms. Moreover, leverage has a negative effect in larger firms, whereas the influence becomes negative in smaller ones.

This study is unique in that it approaches the capital structure issue from a different perspective, where the leverage decision is distinctly considered at various levels of firm profitability and firm size. In addition, the majority of the existing studies is carried out in developed countries. However, the results might not apply to emerging countries given the specificity of their institutional structure. In this regard, Saudi Arabia has a distinctive business climate characterized by the absence of corporate tax and an illiquid bond market.