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The “supply-side effect” brought about by the imperfection of the capital market has increasingly been concerned. The purpose of this paper is to study how will the uncertainty of equity financing brought about by the equity financing regulations in emerging capital market affect company's capital structure decisions.

This paper establishes a theoretical model and tries to introduce equity financing uncertainty into the company's capital structure decision-making. The paper uses mathematical derivation method to get some basic conclusions. Next, in order to characterize the quantitative impact of specific factor on capital structure, numerical solution methods are used.

The model shows that firm's value would decrease with the uncertainty of equity financing, because of the relationship between firm's future cash and their financing policies. The numerical solution of the model suggests that the uncertainty of equity financing is one of the important factors affecting the choice of optimal capital structure, the greater the uncertainty is, the lower optimal capital structure is.

The research of this paper has certain academic value for further understanding of the issues.

The purpose of this paper is to propose a novel way of determining optimal capital structure, applied to sub-groups of Swiss dairy farms from 2003 to 2014. Optimization of capital structure is carried out with respect to two performance indicators from an economic value added perspective.

Optimal values of capital structure are obtained based on a minimization of correlation between economic performance indicators and a distance function of the debt-to-asset ratio distribution to its quantiles. The approach differs from existing approaches in relying solely on empirical data and in using fewer external parameters, which are difficult to estimate, such as risk aversion coefficients. An unbalanced panel data set from the Swiss Farm Accounting Network with almost 14,000 dairy farm observations serves as input data to the model.

Concise optimal values of capital structure result for regional and temporal sub-groups of Swiss dairy farms. Comparing the evolution of optimal values for these sub-groups with existing models of optimal capital structure, the authors infer that dairy farmers in the mountain region are less risk averse than their counterparts in the valley region and that falling interest rates increase the optimal value of debt-to-asset ratio.

The straightforward computation of optimal values for capital structure without intermediate parameters is useful and new. In addition, the authors’ model can be used as a tool for comparison and validation of previous models with the same aim, e.g. for comparison of risk aversion coefficients or qualitative behavior of optimal values for capital structure.

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.

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 paper analyzes the effects of the capital structure on company performance (return on assets). The analysis is conducted in a large sample of high-tech manufacturing and service companies in the transition economy (Russian Federation). In addition to the aggregated analysis, separate investigations are conducted to scrutinize the impact of company age, size and location factors (the effects of agglomerations). This research postulates the existence and variability of the optimal capital structure and its dependence on economic crisis.

We utilized a large sample that includes 1,826 enterprises over the period from 2013 to 2017. The estimation was performed using the panel-corrected standard error estimation technique (Prais–Winsten regression) to account for the panel nature and distributional properties of our data. The existence of the optimal capital structure was assessed based on a curvilinear (quadratic) function.

The results are consistent with the Static Trade-off Theory and show that this theory is applicable to countries with transition economy. They demonstrate that effective management of the capital structure can increase return on assets by 16–22%. The optimal share of borrowed capital is higher for small businesses compared to larger ones and for enterprises located in agglomerations compared to those located in other regions. A greater increase in profitability can be achieved by larger firm companies compared to smaller ones. High share of borrowed capital leads to negative profitability, i.e. to losses by enterprises. No significant differences in profitability growth were identified between young and mature enterprises. The optimal share of borrowed capital that maximizes return on assets is in the range of 0–21%.

Due to the SPARK policies, our access to the data has been limited to a five-year window, which imposed certain limitations on the choice of econometric methods we could have employed and somewhat limited our ability to contrast the effect of the crisis period with the period of stability. In this sense, although our results pertaining to the effect of the crisis could be treated as conservative, future research should consider extending the panel to include more years into consideration.

We identified significant differences between optimal capital structures and actual capital structures for high-tech enterprises. The contribution of this study is that the calculations were made for a country with a transition economy under crisis conditions. Countries with transition economies and developing countries tend to be characterized by a high level of interest rates on loans and a high proportion of borrowed capital in total assets. This poses difficulties for companies relying on borrowed capital to finance their operations. At the same time, our results demonstrate that in transition economies, enterprises in high-tech industries do have an optimal capital structure that allows maximizing firm performance. That is, Static Trade-off Theory is applicable to transition economies characterized by high interest rates on loans.

The novelty of this study lies in the detailed analysis of high-tech industries in Russian Federation. This analysis makes use of sophisticated econometric techniques for the first time in this context.

This chapter focuses on testing optimal capital structure theory: The role of intervening variable debt to equity ratio (DER) on the influence of the financial performance, Ownership Structure of Independent Board of Commissioners (IBCO), Audit Committee (ACO), and Institutional Ownership on Firm Value. The research design was explanatory research using path analysis. Using purposive sampling, 61 manufacturing companies, observation period from 2014 to 2018 with 286 N samples. The research novelty empirically can prove the role of intervening variable DER on the effect of return on assets (ROA) on firm value and shows the market response to the ROA is fully reflected by DER, indicating the existence of an optimal capital structure. The role of DER on the effect of ROE and IBCO on firm value is a partial mediation with the inverse direction. This phenomenon shows that the mechanism of forming a balance between the responses of investors and creditors relates to debt financing.

Determining an optimal capital structure for a company is a multi‐facetted problem that has challenged and fascinated academics and practitioners for a long time. This study investigates capital structures used in different countries and industries and explores the different theories on capital structure that have been put forward to date. A trade‐off model, incorporating taxes and financial distress costs, is applied to determine the optimal capital structure for three companies listed on the JSE South Africa. One of the conclusions drawn from the results of this analysis is that great care needs to be taken in ensuring the reasonableness of the input data and the valuation model. Secondly, significant amounts of value can be unlocked in moving closer to the optimum level of gearing. Lastly, even when one is using a model such as the one illustrated, it may be preferable to try to operate within an acceptable interval rather than to try to attain the absolute optimum capital structure.

The purpose of this paper is to examine whether corporate ownership affects corporate capital structure. This study also seeks to find out whether there is difference in dynamics of the capital structure between these two groups of firms.

Based on panel data of China's listed firms from 1998 to 2007, this paper employs a static empirical model to validate the difference in capital structure between these two groups of firms, and then, a dynamic empirical model is used to explore the dynamic adjustment of the capital structure.

The empirical results show that there is structural difference in static capital structure between state-owned and private listed firms. Further study results tell us that the adjustment to an optimal capital structure is to be faster for the private firm than for the state-owned firm.

The findings suggest that compared with state-owned firms, private firms face higher financial friction in financing activities, but have more incentive to adjust toward optimal capital structure to maximize the shareholders' benefit. This study offers insights to corporate managers interested in privatization, when a state-owned firm is privatized, that firm becomes subject to the disciplining forces of the market and more active to pursue maximum market value of the firm, thus the adjustment to an optimal capital structure to be faster for private firm than for state-owned firm.

This paper for the first time looks at the influence of ownership on capital structure, from both static and dynamic perspective. And this study is helpful for regulators, and corporate managers to understand the corporate financial management behavior.

With the majority of highway projects in China having entered their operational phases, the maintenance and repair of the pavement is receiving increasing attention. One problem that needs to be addressed urgently is that of how to raise the proper funds for highway maintenance to ensure the sustainable operation of the project. To this end, the aim of this study is to investigate the capital demand for operation and maintenance of a project by means of a refinancing scheme, in order to reduce the possibility of project bankruptcy and to enhance the economic value of the project.

Based on an analysis of the dynamic complexity of the highway pavement maintenance system, a Markov model is used to predict pavement performance, and an optimal capital structure decision model is proposed for highway public–private partnership (PPP) project refinancing, using the method of system dynamics (SD). The proposed model is then applied to a real case study.

Results show that the proposed model can be used to predict accurately the dynamic changes in the demand for road maintenance funds and refinancing during the period of operation, before making the optimal decision for the refinancing capital structure.

Although many scholars have studied the optimal refinancing capital structure of PPP projects, the dynamic changes inherent in the demand for maintenance funds for highway PPP projects are seldom considered. Therefore, in the approach used here the influence of the dynamic change of road maintenance capital demand on refinancing is investigated, and SD is used for the optimal capital structure decision-making model of highway PPP project refinancing, to make the decision-making process more reasonable and scientific.