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This paper aims to test the empirical validity of the dynamic trade-off theory in its symmetric and asymmetric versions in explaining the capital structure of a panel of publicly listed US industrial firms over the period from 2013 to 2019. It analyzes the existence of an adjustment of leverage toward its target level and whether the speed of this adjustment is influenced by the debt measure, the model specification or/and the fact that the actual debt ratio is higher or lower than its long-term target level.

This paper uses a quantitative research methodology using panel data analysis under the partial adjustment model and the error correction model using the generalized moment method in first differences and in systems to explore the dynamic nature of firms’ capital structure behavior.

The results show that the effects of the conventional determinants of leverage are globally consistent with the trade-off theory predictions. The dynamic versions confirm that firms exhibit leverage-targeting behavior. Although this speed of adjustment (SOA) depends on the debt and model specifications, it is around 60% on average. The estimated SOA is higher for the market leverage measure compared to the book leverage. The asymmetric adjustment model reveals that firms are more sensitive to reducing leverage than increasing it when they are away from their target; overleveraged firms exhibit approximately 5% faster adjustment than underleveraged firms when book leverage is used.

The originality of this research paper lies in its development and test of an asymmetric model to allow the leverage adjustment speed to vary depending on whether the firm’s debt ratio is above or below its target level and the methodological approach as well as the different model specifications used and the insights generated through the application of rigorous econometric techniques.

The purpose of this paper is to examine the role of institutional, macroeconomic, industry, and firm characteristics on the adjustment speed of corporate capital structure within the context of developing countries.

The authors considers a sample of 986 firms drawn from nine developing countries in Africa over a period of ten years (1999-2008). The study develops dynamic partial adjustment models that link capital structure adjustment speed and institutional, macroeconomic, and firm characteristics. The analysis is carried out using system Generalized Method of Moments procedure which is robust to data heterogeneity and endogeneity problems.

The paper finds that firms in developing countries do temporarily deviate from (and partially adjust to) their target capital structures. Our results also indicate that: more profitable firms tend to rapidly adjust their capital structures than less profitable firms; the effects of firm size, growth opportunities, and the gap between observed and target leverage ratios on adjustment speed are functions of how one measures capital structure; and adjustment speed tends to be faster for firms in industries that have relatively higher risk and countries with common law tradition, less developed stock markets, lower income, and weaker creditor rights protection.

Future research should focus on examination of the adjustment speed of debt maturity structure. Identification of industry-specific characteristics that affect the pace with which firms adjust their capital structure to the optimum is another possible avenue for future research.

Our findings have practical implications for corporate managers, governments, legislators, and policymakers.

The study focuses on firms in developing countries for which the literature on adjustment speed of capital structure is virtually non-existent. Furthermore, unlike previous works on capital structure, it explicitly models industry variable as one of the determinants of adjustment speed. Therefore, it contributes to the literature on capital structure and adjustment speed in general and to the literature on developing countries in particular.

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 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.

The purpose of this paper is to investigate whether existing capital structure target adjustment models are able to identify whether companies adjust their capital structures towards an (unobservable) target.

Existing capital structure target adjustment models are applied to a specific dataset by using different regression techniques (ordinary least square, fixed effect, Fama‐MacBeth, least square dummy variable “corrected”, SYS‐GMM).

Existing capital structure target adjustment models are not able to identify whether companies adjust their capital structures towards a target or not. They might indeed indicate target adjustment behaviour when companies' capital structures actually move away from their targets.

As target adjustment behaviour is often used as support for the trade‐off and against the pecking order theory, the “horse race” between both theories seems still to be open.

This paper highlights some of the fallacies of existing capital structure target adjustment models and demonstrates that the results obtained by those models can be highly misleading.

Building upon the framework of the tradeoff model of capital structure and motivated by the equity market timing theory, we examine whether equity misvaluation is a source of adjustment “costs” that will affect a firm’s leverage adjustment speed toward target. We also investigate whether the quality of a firm’s long-term growth options will influence the decisions of managers to exploit the mispriced equity to converge to the optimum. Using a sample of listed Taiwanese firms during 1992–2014 and employing the market-to-book decomposition as developed by Rhodes-Kropf, Robinson, and Viswanathan (2005), we find that overleveraged and overvalued firms demonstrate faster adjustment speed than overleveraged but undervalued firms. Furthermore, controlling for the misvaluation status, high-growth firms converge to target faster than their low-growth counterparts. The effect of growth options on the relation between equity mispricing and adjustment speed does not mirror the effect of financing deficits. With the detailed financial information of the local companies across a rather long time series, this study provides incremental inputs to the literature of capital structure from the determinants of target leverage, the estimation of leverage adjustment speeds, to the identification of the sources of adjustment costs in an emerging market where institutional environment is strikingly different from the US.

The purpose of this paper is to investigate the asymmetric adjustment of cash holdings in Pakistani firms for above and below target firms.

The study employs generalized method of moments (GMM) to investigate the adjustment of cash holdings.

The study found that the firms which hold cash above the optimal level of cash holdings have higher speed of adjustment than the firms which hold cash below the optimal level. Financially constrained (FC) firms also adjust their cash holdings faster than financially unconstrained (FUC) firms but high speed of downward adjustment does not remain persistent after financial constraints are controlled. Findings of this study reveal this asymmetric adjustment in above and below target firms and extend these results in FC and FUC Pakistani listed firms, respectively.

The conclusion of this study has been derived under certain limitations. There is a vast space to extend this study in different dimensions. Firms operating in capital-intensive industries may provide different results for financial constraints because their policy designing would be quite different from other firms.

This study contributes to cash holdings research in Pakistan by exploring the adjustment behavior of cash holdings across Pakistani non-financial firms using econometric modeling. Downward adjustment rate is supposed to be higher than upward adjustment rate and this rate is tested using dynamic panel data model. Similarly, it is inferred that this relationship holds for above target firms even after including the financial constraints in the presented model.

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.

The purpose of this paper is to investigate how the timing behavior and the adjustment toward the target of capital structure interact with each other in the capital structure decisions. Past literature finds that both timing and targeting are significant in determining the leverage ratio which is inconsistent with any standalone framework. This study argues that the preference of the firm for timing behavior or targeting behavior depends on the cost of deviation from the target. Since the cost of deviation from the target is likely to be asymmetric between overleveraged and underleveraged firms, the direction of the deviation from the target leverage is expected to alter the preference toward timing or targeting in the capital structure decision.

This study used the GMM system estimators with the Malaysian data for the period of 1992-2009 to fit a standard partial adjustment model and to estimate the speed of adjustment (SOA) of capital structure.

This study finds that Malaysian firms, on average, adjust their leverage at a slow speed of 12.7 percent annually and this rate increased to 14.2 percent when the timing variable is accounted for. Moreover, the SOA is found to be significantly higher and the timing role is lower for overleveraged firms compared with underleveraged firms. Overleveraged firms seem to find less flexibility to time the market as more pressure is exerted on them to return to the target regardless the timing opportunities because of the higher costs of deviation from the target leverage. Underleveraged firms place lower priority to rebalance toward the target compared with overleveraged firms as the costs of being underleveraged are lower and hence, these firms have more flexibility to time the market.

The findings of this study support that firms consider both targeting and timing in their financing decisions. No standalone theory can interpret the full spectrum of empirical results. The empirical work is based on partial adjustment model of leverage; however, this model has been criticized by inability to distinguish between active adjustment behavior and mechanical mean reversion. This is an avenue for future research.

This study investigates if targeting and timing behaviors are mutually exclusive as theoretically expected or they can coexist. A theoretical explanation and an empirical investigation support the conclusion that firms consider both targeting and timing in their financing decisions. This study provides evidence from Malaysian firms that are characterized by concentrated ownership structure and separation of cash flow rights and control rights of the firm due to pyramid ownership structure. Therefore, it provides evidence on how environmental characteristics may affect the capital structure determinants of the firm.

This paper aims to investigate the non-homogeneity in the speed of adjustment (SoA) of the capital structure of manufacturing companies. It also attempts to study the key determinants that accelerate the speed of adjustment towards the target leverage level.

Using the dynamic panel fraction (DPF) estimator on the partial adjustment model, the study captures the heterogeneous SoA of 2,866 firms across eight prominent sectors of the Indian manufacturing industry from 2009 to 2020. To ensure robustness, the empirical inferences of DPF are cross-verified with the estimates of panel-corrected standard errors (PCSE).

The authors find a combination of the capital structure's slow, moderate and rapid adjustment speed along with the relevance of trade-off theory. Interestingly, the lowest and fastest SoA is recorded by the dwindling textile sector and expanding food and agro sector, respectively. Profitability, firm size, asset tangibility and non-debt tax shields are the key firm-specific parameters that impact the SoA towards the target.

Availing the rarely employed estimator ‘DPF’ and the objective of documenting diverse and non-uniform adjustment speeds across the Indian manufacturing sectors marks a novel addition to capital structure literature.