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This study aims to evaluate the impact of listed Portuguese companies' specific determinants on adjustment of actual debt towards target debt ratio. The specific determinants on adjustment of actual debt towards target debt ratio that we consider are: asset tangibility, size, profitability and market to book ratio.

Dynamic panel estimators are used to determine adjustment of the actual level of debt towards optimal level of debt, revealing the level of transaction costs borne by companies. OLS regressions are also used, in order to estimate the impacts of companies' specific determinants on debt adjustment.

The results suggest that transaction costs are relevant in listed Portuguese companies' access to debt. Tangibility of assets and size are determinants that contribute for a greater adjustment of debt towards optimal level. The results also suggest that the capital structure decisions of listed Portuguese companies can be explained in the light of trade‐off and pecking order theories, and not according to what is forecast by market timing theory.

Through this study, the level of adjustment of actual debt towards target debt ratio in the context of companies belonging to under‐developed capital markets are determined, in the particular case of this study, belonging to the Portuguese capital market. Furthermore, from target debt ratio depending on companies' specific determinants, the explanatory power of trade‐off, pecking order and market timing theories are investigated. The results contribute for a deeper understanding about companies' capital structure decisions.

Sees the objective of teaching financial management to be to help managers and potential managers to make sensible investment and financing decisions. Acknowledges that financial theory teaches that investment and financing decisions should be based on cash flow and risk. Provides information on payback period; return on capital employed, earnings per share effect, working capital, profit planning, standard costing, financial statement planning and ratio analysis. Seeks to combine the practical rules of thumb of the traditionalists with the ideas of the financial theorists to form a balanced approach to practical financial management for MBA students, financial managers and undergraduates.

The purpose of this paper is to study the dynamics of capital structure in the context of Indian manufacturing companies in a partial‐adjustment framework during the period 1993‐1994 to 2007‐2008.

This paper specifies a partial‐adjustment model and uses the generalized method of moments technique to determine the variables which affect the target capital structure and to find out the factors affecting the adjustment speed to target capital structure.

Firm‐specific variables like size, tangibility, profitability and market‐to‐book ratio were found to be the most important variables which determine the target capital structure across the book and market leverage and the factors like size of the company, growth opportunity and the distance between the target and observed leverage determine the speed of adjustment to target leverage for the Indian manufacturing companies.

The behavioural variables like managers' confidence and attitude towards raising the external finance have not been incorporated in the model to determine the target capital structure due to the data constraint.

This paper has implications for corporate managers in India, for example, to consider the various adjustment costs while altering the financing decisions of the company with other variables like flexibility of the manager, direct cost of debt and equity.

This paper is first of its kind to study both the determination of target capital structure and the speed of adjustment to target capital structure in the context of Indian companies.

Various factors may influence project finance when a multi-sourced debt financing strategy is used for financing capital investments, in general, and public infrastructure investments, in particular. Traditional indicators lack comprehensive consideration of the influences of many internal and external factors, such as investment structure, financing mode and credit guarantee structure, which exist in the financing decision making of BOT projects. An effective approach is, thus, desired. The paper aims to discuss these issues.

This paper develops a financial model that uses an interval number to represent the uncertain factors and, subsequently, conducts a standardization of the interval number. Decision makers determine the weight of each objective through the analytic hierarchy process. Through the optimization procedure, project investors and sponsors are provided with a strategy regarding the optimal amount of debt to be raised and the insight on the risk level based on the net present value, as well as the probability of bankruptcy for each different period of debt service.

By using an example infrastructure project in China and based on the comprehensive evaluation, comparison and ranking of the capital structures of urban public infrastructure projects using the interval number method, the final ranking can help investors to choose the optimal capital structure for investment. The calculation using the interval number method shows that X2 is the optimal capital structure plan for the BOT project of the first stage of Tianjin Binhai Rail Transit Z4 line. Therefore, investors should give priority to selecting a capital contribution ratio of 45 per cent for this investment.

In this paper, some parameters, such as depreciation life, construction period and concession period, are assumed to be deterministic parameters, although the interval number model has been introduced to analyze the uncertainty indicators, such as total investment and passenger flow, of BOT rail transport projects. Therefore, more of the above deterministic parameters can be taken as uncertainty parameters in future research so that calculation results fit actual projects more closely.

This model can be used to make the optimal investment decision for a project by determining the impact of uncertainty factors on the profitability of the project in its lifecycle during the project financial feasibility analysis. Project sponsors can determine the optimal capital structure of a project through an analysis of the irregular fluctuation of the unpredictable factors in project construction such as construction investment, operating cost and passenger flow. The model can also be used to examine the effects of different capital investment ratios on indicators so that appropriate measures can be taken to reduce risks and maximize profit.

The goal of this paper is twofold: to assess the influence of specific corporate and market features on automobiles and parts sector's profitability in Euro area and to identify this particular sector's optimum debt level.

For the paper's purposes, the authors applied a panel data analysis on an annual basis for the period 2005–2017.

There is a strong statistical significance of debt ratio, growth domestic product per capita growth, E.C.'s economic sentiment index (ESI), the European Central Bank key interest rate and the Euro area crisis on sector's profitability, while weak statistical significance appears to emerge for the firm's size. Moreover, the authors find average 14.4% profitability for the entire sector of the Euro area, without significant fluctuations among firms and/or during the examined time period. Another interesting finding of this study is that results are consistent with the theory of Modigliani Miller that financial leverage at a “low” level is beneficial for the firm, but beyond a turning point, it becomes counterproductive. This turning point for the automobiles and parts sector in Euro area has been computed at 47.3%.

The paper focuses on issues of profitability, capital structure and optimal debt ratio of an important sector of the economy, the automotive sector. As regards the Euro area automotive sector, it is a dynamic sector with a significant multiplier effect for the European economy as it is strongly correlated with other industrial sectors as chemicals, steel, textiles, information technology and so forth, having an outstanding multiplier effect on the economy.

The purpose of this paper is to examine the optimal leverage ratio, speed of adjustment, and which factors contribute to achieving the target of selected telecom companies in a partial adjustment framework from 2008 to 2017. Further is to analyze the likelihood of bankruptcy of sample companies by Altman Z-Score model and to suggest which theory of capitals structure is better in explaining leverage strategies and judicious mix of debt and equity structure of the selected telecom companies.

This paper chooses a partial adjustment model and uses the generalized method of moments technique to identify the variables that influence the target leverage ratio and the factors that influence the speed at which the target leverage is adjusted. Second, the Altman Z-score model is used in this paper to research the financial status of telecom companies using financial instruments and techniques.

For Indian telecom firms, firm-specific variables such as profitability, NDTS and Z-score lead to greater debt adjustment towards optimal level target leverage. The paper also highlights new paradigms in the Indian telecom sector, stating that top market leaders such as Bharti Airtel, BSNL, Idea, Vodafone and R.com, among others, should focus on debt reduction and interest payments, as well as implement new strategies to solve the crisis and change financial policies.

It mainly focuses on firm-specific variables because the firm-specific variables affect the leverage framework. The country-specific variables are not taken into the study. These results may be unique to telecom companies due to some peculiarities existing in the telecom sector in India. Although other sectors, both national and international level, can be taken into consideration.

This paper has ramifications for corporate executives, investors and policymakers in India, for example, in terms of considering different transition costs while changing a telecom company’s financing decisions.

To the best of the authors’ knowledge, this is the first paper of its kind to look at both financial and econometric tools to assess financial performance using the Altman Z-Score model, as well as decide leverage strategies and the pace with which they can be adjusted to target leverage in the context of Indian telecom companies.

This paper explores the connection between capital structure and financial performance within European listed firms. The primary objective is to demonstrate an inverse U-shaped relationship between these two variables and pinpoint an optimal debt-equity mix.

In this study, we adopt a dynamic modeling approach to investigate the relationship between a firm’s capital structure and financial performance. Drawing on well-established theories and prior empirical studies, our model examines 3,121 dividend-paying firms from 41 European countries over 14 years, from 2008 to 2021. To enhance the reliability of our findings, we employ two distinct estimation techniques: the fixed effect model (FE) and the system generalized method of moments (System-GMM).

This study reveals an inverse U-shaped relationship between the firm’s financial performance, measured by the return on equity (ROE) and its capital structure (total liability to total assets ratio). Furthermore, an optimal capital structure of about 29% is determined for all firms in the sample, and about 21%, 28% and 41% industry-specific capital structure for manufacturing, real estate and wholesale trade, respectively.

This paper contributes to existing knowledge by empirically determining an optimal capital structure for listed firms across various industries in Europe, which very few studies have attempted to do in the past. An optimal capital structure is an invaluable benchmark for managers and other stakeholders, informing their decision-making.

Determining the optimal capital structure becomes more complicated by the presence of an agency problem. The issuance of debt as a corporate governance mechanism introduces the asset substitution problem – the agency cost of debt. Thus, there is a recognized need for models that can resolve the agency problem between the debtholder and the manager who acts on behalf of the shareholder, leading to optimal capital structure choice, and enhanced firm value. The purpose of this paper is to model the debtholder-manager agency problem as a dynamic game, resolve the conflicts of interests and determine the optimal capital structure.

As there is no satisfactory model for dealing with the above issues, this paper uses a differential game framework to analyze the incongruity of interests between the debtholder and the manager as a non-cooperative dynamic game and further resolves the conflicts of interests as a cooperative game via a Pareto-efficient outcome.

The optimal capital structure required to minimize the marginal cost of the agency problem is a higher use of debt, lower cost of equity and withheld capital distributions. The debtholder is also able to enforce cooperation from the manager by providing a lower and stable cost of debt and a greater debt facility in the overtime framework.

The study develops a new dynamic contract theory model based on the integrated issues of capital structure, corporate governance and agency problems and applies the differential game approach to minimize the agency problem between the debtholder and the manager.

This study seeks to identify the main determinants of the optimal capital structure by reexamining the interpretation of the conventional set of explanatory variables used as proxies for the costs and benefits of debt in the context of the dynamic tradeoff theory.

The authors isolate the variation in leverage due to different targets from that caused by deviations by aggregating the data across a dimension identifying firms with similar targets – credit rating category.

Contrary to theoretical priors, large and profitable rated firms have lower targets. The authors show that size and profitability proxy for non-financial risk and that, for rated firms, non-financial risk is positively correlated to the optimal leverage. The benefits of a better rating outweigh the costs of foregone tax shields for firms with relatively low non-financial risk. The authors find support for that theory in institutional trading – institutional investors do not punish highly rated firms when credit downgrades occur.

This paper contributes to the capital structure literature by developing a new approach based on data aggregation. This study is the first, to the authors’ knowledge, to find a positive effect of the firm's non-financial risk on target leverage among rated firms. The authors argue that the benefit of a better credit rating is an increasing function of the rating itself. The authors also contribute to the literature on the impact of credit ratings on the capital structure choices of the firm.

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.