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Extending the work of Bayou (2001), we empirically investigate the relationship between firm size and resource productivity to assess whether the productivity of resources (value in use) and their underlying value at sale (value in sale) vary with firm’s size.

We use seemingly unrelated regression of revenues and equity values on assets and employees for a large sample over a wide time period and across all industries. We compare companies that are growing, declining, or continuing in size relative to their industry.

With some variability on growth, we find that smaller companies hold more productive resources based on their capacity to generate more revenues per unit of resources (assets) relative to large companies. Further, as predicted, a firm’s workforce has productive value in use, but limited value after a firm’s sale as measured by equity values.

Collectively, our findings suggest that firm size matters in influencing resource productivity, and a workforce has productive value in use, but low value in sale.

Unlike previous studies on capital structure decisions, the purpose of this paper is to focus on US real estate investment trusts (REITs) in order to find out the main determinants of capital structure choice for real estate companies and in order to verify if they are related to factors similar to those affecting the decisions of public firms in other sectors.

Using a methodology similar to Rajan and Zingales, a sample of 119 listed REITs with different investment strategies and in different property sectors was analyzed. The analysis is carried out in order to determine the basic factors underpinning the capital structures by selecting financial items and ratios related with leverage (such as asset size, profitability ratios, tangibility of assets, growth opportunities, operating risk and geographical diversification of investments).

Results show that REITs follow a pecking order theory of financing since more profitable firms are less levered and REITs with more growth opportunities have higher leverage ratios. The tangibility of assets turns out to be positively correlated with leverage, while REITs whose operating risk is high prefer a lower financial risk and consequently a lower gearing. Finally, it is not clear how size affects leverage decisions and more diversified REITs appear to be riskier.

The research also addresses the issue of asymmetric information and the debt‐equity choice for REITs sampled on the basis of their size, highlighting differences with other business sectors.

The purpose of this research is to examine the growth rates of commercial banks and credit unions around the financial crisis and recovery. Credit unions are analyzed as a group and by field of membership. Specifically, this research analyzes the growth rates of assets, deposits, and loans.

This research employs univariate tests of differences to examine the median growth rates for commercial banks and credit unions. Unbalanced pool regressions analyze growth rates during the pre-crisis, crisis, and recovery periods, controlling for size, net charge-offs, and unemployment.

Univariate test results that control for size show that banks grow at faster rates than credit unions for most of the pre-crisis years. However, medium sized credit unions grow at faster rates for most of the crisis and recovery years. Results of unbalanced pool regressions suggest that, overall, credit unions grow at slower rates than do banks. However, during the crisis and recovery, credit union growth is significantly greater than that of banks, after controlling for net charge-offs, size, and unemployment. Credit union growth varies by field of membership type.

Although a large volume of research examines commercial bank performance around the financial crisis, only a few papers assess the performance of credit unions. And very few papers compare commercial banks and credit unions. This paper explores how the recent financial crisis influenced the growth of commercial banks and credit unions from 2005 to 2013.

U.S. bank holding companies (BHCs) have experienced dynamic changes over a period of 2000–2010. We find that the size distribution of sample banks becomes highly positively skewed with a small number of big banks becoming super-sized, and these big banks tend to take extra risk by holding derivative positions for trading purposes. The ten largest risk-taking banks hold about 70% of total assets of all the sample banks in 2010. We investigate whether the risk-taking activities of the BHCs translate into higher risk-adjusted return performance. In extensive panel regression analyses, we find that the risk-taking strategies of large banks by holding derivative positions for trading purpose do not show the clear evidence of enhancing risk-adjusted performance. We find that negative impacts of extra risk-taking on the risk-adjusted performance become bigger with the size of banks.

The purpose of the paper is to explore the impact of board size on the accounting returns and asset quality of Indian banks.

This paper uses ordinary least squares regression, robust regression and panel data methods for estimation, based on data collected for a sample of 29 Indian banks that are listed on the National Stock Exchange (NSE) and form part of the NSE-500 index over a period of eight financial years 2009-2016. The data pertaining to the board size of the sample banks is collected from the annual reports of banks, whereas the data relating to return on assets (ROA) and ratio of the gross non-performing assets to total assets and control variables (bank age and bank size) is extracted from ACE Equity database.

This paper concludes that the size of the governing board has a positive impact on the accounting returns (measured through ROA) of the Indian banks. Further, board size is observed to be insignificant in determining the asset quality of Indian banks.

This paper contributes to the literature and practitioners in a number of ways. First, to the best of the authors’ knowledge, this is the first study on the impact of board size on the accounting returns and asset quality of Indian banks. The findings of the study contribute new theoretical insights to the body of knowledge on the influence of the size of the board, which may be useful for future researchers. Second, banks may enhance their financial performance by taking cognizance of the findings of this study. Finally, equity investors may make use of the findings of this article in deciding on whether to invest in a bank’s stock/lend to the bank based on board size of the bank.

This paper examines the profitability and performance measurement of U.S. regional banks during the period 1994–2011, using the GMM estimator technique. Our study extends prior research by including several factors not previously considered using U.S. data.

We use bank-specific, industry-specific, and macroeconomic determinants of profitability contemporaneous with our performance indicators. We follow the accounting fundamental analysis path in explaining the bank performance.

Among the performance measures, the efficiency ratio and provisions for credit losses are negatively and equity scaled by assets is positively related to profitability. However, these relationships either reverse (efficiency ratio and provisions for credit losses) or become insignificant (equity scaled by assets) when the target becomes change in profitability. The level of nonperforming assets is negatively related to profitability across all measures of profitability used. Macroeconomic variables are largely unrelated to profitability during the year they are measured. However, they have a significant relationship with earnings change measures, suggesting they have a lagged effect on profitability. The slope of the yield curve is especially strong in this regard.

We use our determinants to model changes in bank profitability one year ahead, in addition to including several factors not previously considered, using the predictive focus of the fundamental analysis research.

The purpose of this paper is to investigate the relationship between bank-specific characteristics and profitability in European banking sector to find the role of internal factors in achieving high profitability.

A regression analysis is built on an unbalanced panel data set comprising 175 observations of 35 top European banks over the period 2009-2013. To this end, the empirical data are collected from Bankscope and a comprehensive set of internal characteristics is examined.

All the determinant variables included in the model have statistically significant impacts on European banks’ profitability. However, the effects are not uniform across profitability measures. Regression findings reveal that size and capital ratio are significant company-level determinants of bank profitability in Europe, while higher loan loss provisions result in lower profitability levels. Findings also suggest that banks with higher deposits and loans ratio tend to be more profitable but the effects on profitability are statistically insignificant in some cases.

This study has considerable policy implications, as the performance of the European banking sector depends on its efficiency, profitability and competitiveness. In view of these findings, some suggestions may be functional for bank regulatory authorities to intensify and sustain robustness and stability of the banking sector.

The results provide interesting insights into the characteristics and practices of profitable banks in Europe. Few econometric studies have empirically explored the determinants of bank profitability in Europe so far, even though similar studies have been conducted in several developed countries. Therefore, this paper tries to close an important gap in the existing literature improving the understanding of bank profitability in Europe.

Reviews previous research on the factors affecting the proportions of debt and equity used to finance firms, describes the Saudi Arabian tax system (based on net worth) and stock market; and examines the capital structure 1993‐1997 of a sample of 35 publicly traded Saudi companies. Uses multi‐linear regression models to investigate the relationships between capital structure and other variables in 5 sectors and illustrates their varied leverage ratios. Discusses and analyses the positive links between leverage ratios, firm size and share of government ownership; and negative links with growth, return on assets and profitability margin.

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.

This study empirically investigates the relationship between goodwill assets and capital structure (i.e. debt ratio) of firms and the moderating effect of financial market development on the relationship between goodwill assets and capital structure.

This research applied a quantitative method. The article collects large samples of listed firms from 23 developing and nine developed countries and applied the panel data techniques. This research used firm-level data from the DataStream database for both developed and developing countries. The study uses 4,912 firm-level data from 23 developing countries and 4,303 firm-level data from nine developed countries.

The findings reveal a significant positive relationship between goodwill assets and capital structure in developing countries, but goodwill assets have a significant negative relationship with capital structure in developed countries. Moreover, financial market development positively moderates the relationship between goodwill assets and the capital structure of firms in developing countries. The results inform firm managers that goodwill assets serve as additional collateral to secure debt financing. Moreover, policymakers should formulate a debt market policy that recognizes goodwill assets as additional collateral for the purpose of obtaining debt capital.

The study has several implications. First, goodwill assets are identified as a factor of capital structure in this study. Fixed assets have been identified as one of the drivers of capital structure in previous research, although goodwill assets are seldom included. Second, this article shows that along with demand-side determinants, supply-side determinants also play an important role in terms of the firms' choice about the capital structure. Therefore, firms should take both the demand-side and supply-side factors into consideration when sourcing for external financing (i.e. debt capital).

The study considered goodwill as a component of capital structure. The study analysis includes a large sample of enterprises, including 4,912 big firms from 23 developing countries and 4,303 large firms from nine industrialized or developed countries, which adds to the current capital structure information. Furthermore, a large sample size increases the results' robustness and generalizability.