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The purpose of this study is to explain the adjustment rate toward the target capital structure of Chinese nonfinancial listed firms and to investigate the impacts of the split-share reforms (2005–2006) on the capital structure adjustment rate.

The authors control for the unobserved heterogeneity and the fractional nature of the adjustment rate by applying an unbiased dynamic panel fractional estimator on the unbalanced panel data of 27,545 firm-year observations of Chinese nonfinancial firms listed during 1998–2015.

The authors find that Chinese firms adjust at an annual rate of 19–27% to reach their capital structure targets. The authors also find a positive impact of the split-share reforms on the adjustment rates of Chinese nonfinancial firms toward their target capital structure. Split-share reforms also helped Chinese firms to increase the use of equity financing in their capital structure.

The authors argue that the government should strengthen capital markets to enable easy access to more financing options so that Chinese firms can acquire cheaper external financing.

To the best of authors' knowledge, this is the first study that applies an unbiased dynamic panel fractional estimator on an extended data set of 27,545 firm-year observations of Chinese nonfinancial firms listed during 1998–2015.

The behavioural theory adds a new paradigm of analysis with the assumptions of the decision maker’s cognitive biases and their repercussions on financing decisions. The aim of the study is to analyse the repercussions of these biases on the adjustment speed of firm’s capital structure toward the optimal level.

Based on a partial adjustment model, the study uses the Dynamic Panel Fractional estimator to analyse panel data from 4,990 Portuguese entrepreneurial firms.

The results show that the cognitive overconfidence bias impacts the entrepreneurial firm’s capital structure. In fact, the firms run by overconfident managers adjust more slowly than their counterparts. Furthermore, the findings suggest that entrepreneurial firms make relatively fast adjustments toward the optimal debt level and follow a hierarchical financing order in the funding process.

The results of this paper are not only interesting to the academia, but also contain practical implications for corporate, institutional and business policy and governance. First, the paper introduces a new measure of cognitive bias in optimistic managers, which is useful for current and future academic research. Also, in practical terms, the findings of the paper reveal that when a company is contemplating hiring a manager, it should consider whether they need an optimistic or non-optimistic manager based on the company's present life cycle or situation.

The current analysis extends the existing literature. The study suggests that financial classical and behavioural paradigms should not be separated, which can provide evidence to help narrow the gap between these two major perspectives.

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.

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.

This paper aims to study the leverage adjustment behavior of firms distinguished based on financial flexibility. Financial flexibility is one of the key strengths of the companies to borrow funds for long-term capital investment. The lack of extensive studies in this domain motivates the authors to delve into the significance of financial flexibility in making corporate capital structure decisions.

The data comprise a combination of firm-specific and macroeconomic variables for firms in eight manufacturing sectors from 2009 to 2020. The authors employ an advance estimator, dynamic panel fraction, on the partial adjustment model to investigate the diverse impact on capital structure's speed of adjustment (SoA) between the financially flexible and financially inflexible firms. Furthermore, the authors utilize the generalized method of moments and panel-corrected standard errors to establish the robustness.

The empirical analysis reveals that the SoA of financially flexible firms lies between 19.75% and 35.38% and the SoA of financially inflexible firms lies between 11.66% and 25.81%. Due to their conserved debt capabilities, financially flexible firms can rely on leverage to stay near the target whenever they move away from it. Furthermore, financially inflexible firms exhibit a low adjustment speed due to their incompetence to borrow funds to benefit from new growth opportunities. The existence of a target ratio among the studied firms is identified from the positive coefficient of lagged dependent variable, and the relevance of trade-off theory is proved by the quick adjustment speeds in most sectors.

The sectoral distinction in the backdrop of the financial flexibility component adds to the research novelty and managerial implications.

We extend Vuong’s (1989) model-selection statistic to allow for complex survey samples. As a further extension, we use an M-estimation setting so that the tests apply to general estimation problems – such as linear and nonlinear least squares, Poisson regression and fractional response models, to name just a few – and not only to maximum likelihood settings. With stratified sampling, we show how the difference in objective functions should be weighted in order to obtain a suitable test statistic. Interestingly, the weights are needed in computing the model-selection statistic even in cases where stratification is appropriately exogenous, in which case the usual unweighted estimators for the parameters are consistent. With cluster samples and panel data, we show how to combine the weighted objective function with a cluster-robust variance estimator in order to expand the scope of the model-selection tests. A small simulation study shows that the weighted test is promising.

The purpose of this paper is to examine the existence of rational speculative bubbles in the Middle East and North African (MENA) stock markets.

To complement shortcomings of the traditional bubble tests, such as unit root tests and cointegration tests, mainly relying on expectations of future steams of dividends, the authors employ fractional integration tests and duration dependence tests.

Despite recent extreme fluctuations of MENA stock markets, fractional integration tests built on autoregressive fractionally integrated moving average models do not support the possibility of bubbles in the MENA stock markets. Similarly, duration dependence tests based on nonparametric Nelson‐Aalen hazard functions not only reject the existence of bubbles but also support equality of hazard functions between domestic and the US‐based investors without regard to the rapid financial liberalization and integration in the MENA stock markets.

The reliable results of bubble tests of the MENA stock markets provide domestic and international investors as well as policy makers with invaluable benchmark to better understand the irregular and highly fluctuating stock market behaviors of the MENA stock markets compared to other developed and emerging stock markets. For domestic and international investors, the formal analysis of MENA stock markets behavior including rational speculative bubbles will help them in their portfolio decisions and hedging purposes. Similarly, the empirical results of bubble tests in the paper will be also helpful to policymakers in MENA countries to take actions to improve the functioning of these dynamic markets.

The oil-growth nexus is studied in a panel of Organization of the Petroleum Exporting Countries (OPECs), for a long time span (1960-2011), controlling for the specific context of oil production. Their membership in the cartel put them under a common guidance, which originates phenomena of cross-section dependence/contemporaneous correlation in the panel.

Recent panel data estimators and co-integration analyses are both pursued and discussed, namely, dealing with the heterogeneity of panels and the countries’ specific effects. The Driscoll–Kraay estimator proves to be appropriate in handling the panel properties.

Full understanding of the oil-growth nexus requires the short- and long-run effects to be broken down. The growth hypothesis was found only in the short run. The results suggest the presence of the resource curse phenomenon and prove that the cartel’s long-run growth goal could not being fully accomplished. Actually, both oil production and prices are not promoting economic growth in OPEC countries.

The focus is on a group of countries which, besides being oil exporters, have an institutional connection between them, i.e. the OPEC cartel. The paper also contributes by framing the relationship between oil consumption and economic growth within a context of countries that are primary energy producers. Additionally, the paper uses a novel econometric approach and a long time span (52 years) not tested.

A large literature over several decades reveals both extensive concern with the question of time-varying betas and an emerging consensus that betas are in fact time-varying, leading to the prominence of the conditional CAPM. Set against that background, we assess the dynamics in realized betas, vis-à-vis the dynamics in the underlying realized market variance and individual equity covariances with the market. Working in the recently popularized framework of realized volatility, we are led to a framework of nonlinear fractional cointegration: although realized variances and covariances are very highly persistent and well approximated as fractionally integrated, realized betas, which are simple nonlinear functions of those realized variances and covariances, are less persistent and arguably best modeled as stationary I(0) processes. We conclude by drawing implications for asset pricing and portfolio management.