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This study aims to investigate the dynamic aspects in the capital structure decisions of firms in Indonesia, offering an extension to the existing literature on Indonesia via a dynamic model, including the existence of target capital structure, the influencing factors, the speed of adjustments and the supporting theories to explain the findings.

This study uses a dynamic partial adjustment model estimated based on a generalized method of moments.

Indonesian firms do practice target capital structure and are influenced by firm-specific factors like profitability, business risk, firm size, liquidity and share price performance due to time-varying factors. A rapid adjustment toward target leverage is detected, thus supporting the existence of the dynamic trade-off theory (TOT). The pecking order theory (POT) also has significant influence, particularly after the new reformation of financing policy, where retained earnings are also preferred as a source of financing apart from merely external financing through bank loans. There are also traces of market timing influences where firms also seem to time their equity issuance.

Despite relatively utilizing recent data and bigger sample firms compared to the previous limited studies on Indonesia, the results of this study, however, need to be cautiously interpreted. First, the sample chosen focused on listed firms, hence may not be generalized to all Indonesian firms, listed and unlisted. Second, the study does not separate firms by sectors and their leverage positions, that is under-levered and over-levered, so as to note that financial decisions may also be affected by the sector in which the firms operate and their leverage positions. These are to be considered in future research.

There is strong evidence that the corporate financing behavior of Indonesian firms is governed by the POT and TOT. Both are dealing with the function of debt. The financial sector reformation does have a positive impact on the banking sector, but not the local corporate bond market. Therefore, regulators and policymakers should bear in mind that banking as well as private bond market in Indonesia must be tailored in such a way that both could act as intermediaries of debt financing, as bond market represents an important component of a diversified financial sector.

This study fills the gap by providing an extension to the existing literature and a deeper insight of the capital structure of Indonesian firms using a more robust dynamic model.

This study aims to examine the existence of capital structure dynamics and speed of adjustment during different economic periods. This study adds to the existing body of literature by investigating the factors influencing adjustment process toward target debt in developing economies.

By employing two-step generalized method of moment (GMM) and sensitivity analysis, the study highlights critical factors which affect firms’ adjustment mechanism for target debt.

Dynamic GMM estimations confirm the substance of past leverage on current debt, which recognizes the existence of dynamic capital structure. The findings corroborate that adjustment process is subject to trade-off between convergence rate and cost of being off-target. The fraction of financing of Pakistani firms confirms the pattern of pecking order hypothesis. The outcome of study clearly validates the significance of dynamic trade-off modeling for optimal capital structure.

As more data become available, the authors would extend this study to investigate the sectoral analysis to find how capital structure dynamics are different across sectors and how distinctive behavior of each sector differently affects the adjustment process toward target debt across each sector. In addition, sector-level and macro-economic factors could be incorporated to examine how external factors affect the firm’s speed of adjustment across sectors.

The present study provides valuable insights for banking and corporate sector, mainly in Pakistan. The companies could take into consideration the firm-level factors which affect the adjustment process toward target debt. Likewise, the borrowing and lending procedures could be advanced by complying with dynamic mechanism of speed of adjustment. Furthermore, the findings of this research provide obstinate grounds for future research.

Both the use of dynamic GMM adjustment model and sensitivity analysis along with Sargan test validate the health of instruments and values.

This paper aims to conduct bibliometric analysis on the studies dealing with capital structure’s speed of adjustment (SoA) and identify the prominent themes while suggesting future research directions in the area. The existing reviews broadly focus on the capital structure, which provides the scope for conducting a review on this sub-aspect of capital structure.

This study uses a three-stage process to conduct this review: identification of academic journals, selection and analysis of target papers. This study uses a combination of bibliometric tools and a system thinking approach to assess the current status of publications and emerging themes within the literature.

This study has found a progressive evolution of SoA in capital structure research from 1984 to 2021. Studies largely focus on implementing the dynamic models to analyse the impact of adjustment costs, dynamic economic conditions, corporate governance practices and other variables on the firms’ adjustment speed and financial decisions. The network analysis of citations, keywords and clusters gives further knowledge on the intellectual structure of the data.

This study is highly dependent on the papers available within the SCOPUS database. Studies not included herein are not part of this analysis, which may or may not bear an effect on the study’s findings.

To the best of the authors’ knowledge, the application of systems engineering concept of “system thinking approach” to identify literature gap and suggest directions for forthcoming research is the first of its kind, thus adding a novel and multidisciplinary aspect to this study.

The purpose of this paper is to examine the determinants of capital structure using a dataset of firms in Malaysia.

This paper carries out a panel data analysis of 8,270 observations from 827 listed non-financial firms on the Malaysia stock market over the period 2008–2017. To estimate the model and analyse the data collected from the DataStream and World Bank databases, the authors use static panel estimation techniques as well as two-step difference and system dynamic GMM estimator.

The results show that profitability, growth opportunity, tax-shield, liquidity and cash flow volatility have a negative and significant impact on debt measures. However, the effects of collateral, non-debt tax and earnings volatility on measures of debt are positive and significant. In addition, firm size, firm age, inflation rate and interest rate are important determinants of the present value of debt. The results also show a significant inverse U-shaped relationship between the firm's age and its capital structure. In general, the results support the proposition advocated by the pecking order and trade-off theories.

The results of this study necessitate formulation of various policy measures that can counter the effects of debt on firms.

The present study is among the earliest to use both the book and market value measures of capital structure. It also uses three proxies for each: total debt, long-term debt and short-term debt. It incorporates earning volatility and cash flow volatility as new independent variables in the model. These variables have not previously been used together with both book and market value measures of capital structure. The study also examines the non-monotonic relationship between firm's age and capital structure using a quadratic regression method. It applies both static panel techniques and dynamic GMM estimation techniques to analyse the data.

This study aims to evaluate the impact of firm, industry level determinants and ownership concentration on the dynamic capital structure decision in Indonesia and analyses the governing theories.

This study uses the dynamic panel model of generalized method of moments-System (one-step and two-step) by using a panel data from 2000 to 2014 to examine the relationship between the determinants and leverage. The results are robust to the various definitions of leverage, heterogeneity, autocorrelation, multicollinearity and endogeneity concern.

Growing firms and firms operating in a highly concentrated industry use high level of debt, taking advantage of the tax shield (trade-off theory). However, if the firms are operating in a highly dynamic environment, they take on less debt as to avoid bankruptcy risk. Firms in Indonesia opt for debt financing perhaps to act as a controlling mechanism to mitigate agency conflicts that may exist between the large controlling shareholders and the minority. Aged and highly profitable firms with high tangible and intangible assets and liquidity level operating in a high dynamic environment follow the pecking order theory.

This study does not perform each industry regression individually. All the industries are pooled together, as the main focus of this study is to examine the factors affecting leverage of firms in general without giving particular attention to individual industry.

The insights on the impact of ownership concentration and industry characteristics are novel especially on Indonesia, thus fill the gap in the literature.

The purpose of this paper of this study is to examine the possible factors contributing to the issue of inconclusiveness in capital structure studies. This study also attempts to provide logical explanations to the unresolved issue of inconsistencies in the relationship between factors identified and leverage in capital structure studies. Comparisons are also made between the emerging market and the developed market to see whether the findings are consistent with both market landscapes.

This study employs two models in its methodology which are static and dynamic models to examine the effects of using different models in the study. The fixed effect model and partial adjustment model represent the static and dynamic models, respectively. The dynamic model is estimated using generalized method of moments. This study also uses six definitions of leverage to examine the impact of using different leverage definition in capital structure studies. To test the robustness of the findings comparison were made with past studies done by other researchers on developed markets.

This study found that the use of different models (with the same leverage definition) and different leverage definitions (using the same model) give different results including signs. Inconsistencies were more obvious in the different leverage definitions (using the same model) compared to the use of different models (with the same leverage definition). There was also evidence that the findings were consistent with both the emerging and the developed markets as other studies on developed markets also report inconsistent results when using different models and different leverage definitions.

The sample chosen focussed only on firms in three emerging markets (Malaysia, Thailand and Singapore) thus it may not be sufficient for generalization.

The issue of inconclusive results and findings in capital structure studies keeps recurring but no study has been done to further understand the issue. Using data from the selected countries, this paper attempts to fill this gap in the literature.

Using a 10,709 firm-year sample covering the 1998–2007 period, we investigate the determinants of capital structure among 1,491 ASEAN-4 (Indonesia, Malaysia, the Philippines, and Thailand) emerging market firms. Building on the work of previous authors, we apply the two-step generalized method of moments (Arellano & Bond, 1991) to develop country-specific dynamic models of target leverage decisions. The right-hand variables incorporate a lagged leverage term that controls for the firms' target adjustment process and the following four explanatory variables: firm size, profitability, tangibility, and nondebt tax shields. The sign and significance of each coefficient provides evidence regarding whether the impact of the associated variable is consistent with the trade-off or pecking order theories. We find that size is negatively associated with leverage among Malaysian, Philippine, and Thai firms but positively associated among Indonesian firms. Profitability is negatively associated with leverage among Indonesian and Malaysian firms but positively associated among Philippine firms. Tangibility is negatively associated with leverage among Malaysian firms but positively associated among Philippine firms. While the impacts of size and profitability are consistent with pecking order theory, the impact of tangibility is not supportive of a specific theory. Of the four variables, size is consistently influential, while nondebt tax shields have no significant impact among firms in any country.

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.

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.

The purpose of this paper is to use a dynamic model to investigate capital structure determinants for 178 firms listed on the Johannesburg Stock Exchange for the period 1998‐2008. The sample of firms is also used to examine the cost and speed of adjustment towards a target debt ratio.

A target adjustment model is estimated using a generalized method of moments technique to examine the cost and speed of adjustment towards a target debt ratio. The determinants of target capital structure for South African listed firms are also examined.

The results show that South African firms adjust relatively fast towards a target leverage level. It is also found that asset tangibility, growth, size and risk are positively related to leverage, while profitability and tax are negatively related to leverage. The results also suggest that capital structure decisions of South African listed firms follow both the pecking order and trade‐off theories of capital structure.

The sample chosen focused on listed firms, thus the results cannot credibly be generalized to all South African firms (listed and unlisted). Also, whilst a lot can be gleaned from the results, they may not be readily applicable to firms in other African countries.

The issue of dynamic adjustment towards a target or optimal debt ratio has not received sufficient attention in developing economies. Using data from an emerging economy, this paper attempts to fill this gap in the literature. A target adjustment model is estimated using a generalized method of moments technique.