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The purpose of this paper is to empirically investigate the factors deriving effective tax rate (ETR) for Indian manufacturing firms in different sectors. The study also tries to analyze the sensitiveness of ETR because of shocks on its key determinants.

The study is using Arellano–Bond dynamic panel regression model to identify the key drivers of ETR, and impulse response functions of panel vector auto-regression model to analyze the response of ETR because of one standard deviation (SD) shock to its key determinants.

This study concludes that ETR is significantly explained by firm size, profitability, growth rate and non-debt tax shield in most of the sectors, and debt ratio, asset tangibility and age of the firms are impacting ETR differently across sectors. In case of entire manufacturing sector, firm size, profitability, growth and non-debt tax shield are driving ETR positively and asset tangibility is driving ETR negatively. Interest coverage ratio (ICR) and firm age are not significant drivers of ETR. ETR is positively related with firm size, but responses negatively when there is an immediate shock to firm size. Similarly, ETR is negatively related with asset tangibility, but responds positively following an immediate shock to it. Overall, ETR is more sensitive and responses significantly because of shocks in firm size, profitability, growth, asset tangibility and non-debt tax shield whereas, the response is very marginal following shocks to debt ratio, ICR and age of the firm.

Firm managers may find the study useful to understand the receptiveness of ETRs at each sector level. The empirical findings are not only validating the theoretical developments but also providing a root cause analysis to the firm managers to understand the cause and consequence of ETRs for firms at different sectors.

Empirically investigating the factors driving ETR and analyzing its sensitiveness because of one SD shock on its key determinants for Indian manufacturing firms from different sectors is the originality of this study. Developing a strong theoretical background and empirically validating it through advanced methodology makes the study unique.

The purpose of this paper is to examine the short-term and long-term interdependence among the stock markets of Africa and Middle East region. It also attempts to analyze the pattern of volatility spillover among the regional stock markets.

The study has used Granger causality test, variance decomposition test of vector auto-regression (VAR) model, vector error correction model (VECM), multivariate generalized conditional heteroskedasticity (MGARCH-BEKK) models and Johansen and Juselius multivariate cointegration techniques.

The study finds that the interlinkages of the stock markets are not uniform across all the countries of the region. The stock market of Israel, South Africa and Jordan are found to be highly connected stock market of the region followed by Egypt and Botswana. The study also finds significant spillover of lagged standardized volatility across the stock markets of the region. But the magnitude of the response of volatility spillover and its persistence is very minimum. However, the stock markets are found to be co-integrated and expected to share long-run equilibrium relationships among each other.

The study has the scope to be extended to capture the time-varying integration of market returns with transmission of monetary policy and exchange rate changes within the region. The results obtained from this study may assist the firm managers and international investors to understand the key drivers of market connectedness.

Empirically investigating the pattern of stock market connectedness in Africa and Middle East region with advanced methodology over a long study period is the originality of this study.

The purpose of this paper is to empirically analyze the determinants of capital structure and their long-run equilibrium relationships with firm-specific and macroeconomic indicators for Indian manufacturing firms.

The study is conducted using the panel semi-parametric and non-parametric regression models to identify the key determinants of capital structure. Panel cointegration models are also employed for analyzing the long-run equilibrium association of capital structure with its determinants.

The study finds that each manufacturing sector has unique determinants of capital structure. The debt level is significantly affected by asset tangibility, growth opportunity, effective tax rate, non-debt tax shield, cash flow, profitability, firm size, foreign investment, government borrowing, economic growth, and interest rate. All these firm-specific and macroeconomic variables have strong long-run equilibrium relationship with capital structure as a whole.

The study analyzes the determinants of capital structure for eight manufacturing sectors of India, which helps firm managers and policy-makers to identify appropriate factors that maximize firm value. The sector-specific features of firms may lead to a new path with regard to corporate governance and ownership structure to enhance stakeholder's satisfaction.

The use of semi-parametric and non-parametric panel regression models to analyze the determinants of capital structure, and the use of panel cointegration approach to explore the long-run equilibrium relationship between the determinants and its factors are the unique contributions of the present research.

The purpose of this paper is to track the financial performance of manufacturing firms at different levels of their conditional quantiles. It also analyzes the relevance of revenue and cost channels along with key firm-specific parameters that influence firm’s profitability.

The study analyses a sample of 1,000 manufacturing firms over a study period spanning from 2000 to 2016. It uses both quantile regression and panel ordinary linear square (OLS) models to analyze the financial performance of the firms.

The study finds large scale of heterogeneity among the firms under different quantiles of profitability. Export earnings, firm size, asset turnover and volatility of exchange rate are the decisive determinants of financial performance across all quantiles. Financing assets by current debt is negatively impacting return on assets and return on capital employed of firms from lower quantile whereas profitability is positively impacted if they are financed by long term debt. Debt financing of assets does not make any sense for firms with high quantile of profitability. The study also finds that quantile regression approach is a better method than panel OLS models in the presence of highly heterogeneous and non-normal distributions.

This study is limited to the financial performance of manufacturing firms and does not consider service sector which is also equally competitive. However, a sector wise analysis of firm’s profitability could be more meaningful than comparing all the firms in one basket of manufacturing domain.

The research findings have both practical as well as policy implications. Practically, the study helps the firm managers to identify critical success factors that significantly influence firm’s financial performance at different levels of profitability. It also helps the policy makers to align policy focus to stabilize firms at lower level of profitability and also to manage conducive business environment for all firms at different levels of their profitability.

The study provides a deep theoretical underpinning of literatures on firm’s financial performance and empirically investigates it using advanced methodology. The robust estimates of the study ensure to analyze financial performance under revenue and cost channels at diverse level of their profitability.

The purpose of this paper is to provide empirical evidence about the relationship between working capital financing (WCF) and firm profitability in six key manufacturing sectors of Indian Economy. It also aims to capture the change in the financing of working capital requirement over different scenarios of price-cost margin and financial flexibility.

The study is undertaken on a sample of 1,211 firms from 6 key manufacturing sectors of Indian economy from 2000 to 2016. The non-linear relationship between WCF and profitability is studied using two-step generalized model of moments (GMM) estimator.

The study finds a convex relationship between WCF and profitability among firms in chemical, construction, and consumer goods sectors. Firms in these sectors can finance larger portion of their working capital requirements through short-term debt without negatively impacting profitability. However, a concave pattern of relationship for firms in machinery, metal, and textile industries implies increasing debt financing of working capital requirement would increase profitability for the firms who have financed lower portion of their working capital by short-term bank borrowing. But when a higher proportion of working capital requirements are already financed by short-term debt, a further increase in debt financing may impact profitability negatively. Moreover, the study finds that firms with high financial flexibility and high price-cost margin (except textile) can increase profitability by financing larger portion of working capital requirement through short-term debts and the continuation with risky WCF could increase profitability.

The study contributes to the literature on working capital in a number of ways. First, no previous study has been undertaken to explore the non-linear relationship between WCF and corporate profitability over a large sample of firms from six key manufacturing sectors of Indian economy. Second, the study uses a quadratic function to explore the non-linear relationship between WCF and profitability. Third, the study explores the relationship between WCF and profitability with respect to the price-cost margin and financial flexibility of firms under different manufacturing sectors of Indian economy. Finally, the study uses advanced two-step GMM, the panel data techniques to handle unobservable heterogeneity and issues of endogeneity within the data sample.

The purpose of this paper is to capture the pattern of return volatility and information spillover and the extent of conditional correlation among the stock markets of leading South American economies. It also examines the connectedness of market returns within the region.

The time series properties of weekly stock market returns of benchmark indices spanning from the second week of 1995 to the fourth week of December 2015 are analyzed. Using univariate auto-regressive conditional heteroscedastic, generalized auto-regressive conditional heteroscedastic, and dynamic conditional correlation multivariate GARCH model approaches, the study finds evidence of returns and volatility linkages along with the degree of connectedness among the markets.

The findings of this study are consistent with increasing market connectedness among a group of leading South American economies. Stocks exhibit relatively fewer asymmetries in conditional correlations in addition to conditional volatility; yet, the asymmetry is relatively less apparent in integrated markets. The results demonstrate that co-movements are higher toward the end of the sample period than in the early phase. The stock markets of Argentina, Brazil, Chile, and Peru are closely and strongly connected within the region followed by Colombia, whereas Venezuela is least connected with the group.

The implication is that foreign investors may benefit from the reduction of the risk by adding the stocks to their investment portfolio.

The unique features of the paper include a large sample of national stock returns with updated time series data set that reveals the time series properties and empirical evidence on volatility testing. Unlike other studies, this paper uncovers the relation between the stock markets within the same region facing the same market condition.

The purpose of this study is to examine the evidences of leverage effects on the conditional volatility of exchange rates because of asymmetric innovations and its spillover effects among the exchange rates of selected emerging and growth-leading economies.

The empirical analysis uses the sign bias test and asymmetric generalized autoregressive conditional heteroskedasticity (GARCH) models to capture the leverage effects on conditional volatility of exchange rates and also uses multivariate GARCH (MGARCH) model to address volatility spillovers among the studied exchange rates.

The study finds substantial impact of asymmetric innovations (news) on the conditional volatility of exchange rates, where Russian Ruble is showing significant leverage effect followed by Indian Rupee. The exchange rates depict significant mean spillover effects, where Rupee, Peso and Ruble are strongly connected; Real, Rupiah and Lira are moderately connected; and Yuan is the least connected exchange rate within the sample. The study also finds the assimilation of information in foreign exchanges and increased spillover effects in the post 2008 periods.

The results probably have the implications for international investment and asset management. Portfolio managers could use this research to optimize their international portfolio. Policymakers such as central banks may find the study useful to monitor and design interventions strategies in foreign exchange markets keeping an eye on the nature of movements among these exchange rates.

This is one of the few empirical research studies that aim to explore the leverage effects on exchange rates and their volatility spillovers among seven emerging and growth-leading economies using advanced econometric methodologies.

The present study examines the short term dynamism and long term equilibrium relationship between the stock markets of South and Central America. It also aims to capture the dynamic conditional correlations between the stock markets using weekly returns of market benchmark indices of the respective countries spanning from 2nd week of 1995 to 4th week of December 2015 are analyzed.

The Johansen and Juselius multivariate cointegration test, Granger causality test based vector error correction model (VECM) approach, and variance decomposition analysis were used to investigate the dynamic linkages between markets. GARCH-DCC is used to investigate the Correlation Dynamics.

This study identifies long run co-movements between the stock markets. Chile, Peru and Venezuela are the most dynamically interlinked. The empirical results VECM reveal that Argentina, Brazil, Chile and Venezuela stock market returns are significantly influenced by each other, suggesting a stronger linkages between national stock markets. Cointegration test confirms long-run equilibrium relationship. among the major stock markets of the region. The findings from GARCH-DCC provide evidence consistent with increasing market integration. Stocks exhibit asymmetries in conditional correlations. The results demonstrate that correlations are higher toward the end of the sample period than in the early phase.

On the basis of the results produced by the study, we conclude that there exist opportunities for diversification and investors will benefit from reduction of diversifiable risk among the South and Central American countries in general, but in particular Chile, Peru and Venezuela have not shown the same outcome.

This study has been conducted for a longer period of time and also uses various tools to investigate the dynamic linkages between markets.VAR, VECM, Cointegration and GARCH-DCC altogether in a single study is a rare piece of work.

The purpose of this paper is to examine the impact of changes in the exchange rate on long-term investment decisions of Indian manufacturing firms at the sector level.

The study is undertaken on a sample of 1,222 firms from six key manufacturing sectors of Indian economy during the period 2000-2016. The non-linear relationship between real exchange rate and long-term investment is studied using the two-step generalized model of moments estimator.

The study finds a concave (i.e. inverted U-shaped) relationship between the long-term investment and real exchange rate, particularly in case of chemical, construction, machinery and textile sector, in particular, and Indian manufacturing industry as a whole. It implies that investments in these sectors increase with depreciation of real exchange rate up to a point of inflection and subsequent to which it starts decreasing if exchange rate continues to depreciate further. But consumer goods and metal product sectors ensure a convex pattern, which demonstrates that investment is decreasing at the initial stage of depreciation of the exchange rate. The study moves one-step forward in validating this nexus between investment and exchange rate with respect to the price-cost margin and the extent of financial flexibility of firms. It is found that high price cost margin and financial flexibility moderates the adverse impact of exchange rate depreciation and immunizes the long-term investments in the scenario of a weak domestic currency and induce long-term investments.

The study measures the impact of exchange rate changes, but the impact of exchange rate volatility on investment has not been studied, which is absolutely different with different implications.

The study provides a clear guideline to firm managers for using the exchange rate movements in a favorable manner. The findings can be used to ensure sustainable long-term investments with respect to the core competence of firms in terms of price cost margin and financial flexibility at sector level of Indian manufacturing firms.

The study analyzes the non-linear relationship between exchange rate changes and long-term investment behavior of manufacturing firms from six key sectors of India. Further, the study moves one step forward to analyze this nexus under different scenarios of financial flexibility and price cost margin using dynamic panel models.

This paper aims to empirically investigate the evidence of the transmission of monetary policy impulses to firm profitability via manufacturing firms’ short-term and long-term corporate financing decisions.

This study decomposes the receptiveness of firm profitability to monetary policy shock under circumstances of financial flexibility. Additionally, the study extends its scope to undertake a sector-wise analysis of manufacturing firms from 2008 to 2020. Generalized methods of moments (GMM) and quantile regression models are employed.

The profitability of firms in the chemical, food and machinery sector are positively impacted by short-term financing, whereas the metal sector is positively impacted. But during the tight monetary policy, short-term financing does not appear to be a significant parameter while explaining the firms’ profitability. Secondly, the profitability of firms in the consumer goods and metal sector is positively impacted by long-term financing. Therefore, debt financing of assets could be more appropriate to maximize profitability in these sectors.

Analyzing the transmission of monetary policy impulses to firm profitability by clustering firms with financial flexibility across six key manufacturing sectors makes the study unique.