Purpose – The purpose of this paper is to study the key determinants of capital structure for Indian manufacturing firms and which theory implications, i.e. trade off vs pecking order are more applicable in current Indian manufacturing sector scenario. Design/methodology/approach – A sample size of 422 listed Indian manufacturing companies on Bombay Stock Exchange has been considered to do the empirical evaluation. A ten year period from 2003-2004 to 2012-2013 and annual financial standalone data have been considered for study. Ratio analysis and panel data approach have been applied to perform the empirical evaluation. Total debt to total capital and total debt to total assets are used as the proxy for firm financial leverage. Findings – It was empirically found that size, age, asset tangibility, growth, profitability, non-debt tax shield, business risk, uniqueness and ownership structure are significantly correlated with the firm financial leverage or key determinants of capital structure in Indian manufacturing sector. Also, other variables like dividend payout, liquidity, interest coverage ratio, cash flow coverage ratio (CFCR), India inflation and GDP growth rate are empirically found to be insignificant to determine the capital structure of Indian manufacturing sector. There is no single theory implications, i.e. trade off vs pecking order which can explain the capital structure nature of Indian manufacturing sector and rather it is a mix of both the theories. Originality/value – The findings of the study would enhance the literature on capital structure and is significant for the Indian manufacturing firm’s decisions as it includes the most recent data and covers the period of both pre- and post-recession of 2008-2009.

This paper studies the impact of capital structure or financial leverage on firm financial performance. A sample size of 422 listed Indian manufacturing companies on Bombay Stock Exchange (BSE) has been taken to analyze the relationship between leverage and firm performance. A period of 10 years from 2003–2004 to 2012–2013 and annual financial standalone data have been considered to analyze the leverage effect. Ratio analysis and panel data approach have been applied to perform the empirical study. Return on asset, return on equity and Tobin’s Q are used as the proxy for measuring the firm’s financial performance. It was found that financial leverage has no impact on the firm’s financial performance parameters of return on asset and Tobin’s Q. However, it is negative and significantly correlated with return on equity. Other independent variables like size, age, tangibility, sales growth, asset turnover and ownership structure are significant determinants of a firm’s financial performance in the Indian manufacturing sector. Thus, the findings of the study would enhance the literature on capital structure and is relevant for the Indian manufacturing industry in taking its capital structure decisions as it is based on the most recent data and covers the period of both pre- and post-recession of 2008–2009. There is an adverse effect of recession on the financial performance of the Indian manufacturing firms.

PurposeThe purpose of this paper is to empirically investigate the relationship between working capital management (WCM) efficiency and exogenous variables of the Indian manufacturing sector along with its sub-industries that are involved in export activities.Design/methodology/approachPanel regression (fixed effects) was used on a sample of 563 Indian manufacturing firms involved in export activities, covering a time period from 2008 to 2018.FindingsIndustry-wise results showed a significant relation of leverage, net fixed asset ratio, profitability, asset turnover ratio, total asset growth rate and productivity with cash conversion cycle (CCC).Research limitations/implicationsFirstly, having taken a sample from a developing economy, the results of our study may be generalizable only among developing contexts. Secondly, the time period taken in this study (2008–2018) has witnessed several economic fluctuations such as recession and demonetization which might differ for the firms or countries in normal conditions.Practical implicationsAn improved working capital model could advance the firms' performance by reducing the CCC of the firm, thereby creating efficiency in WCM. In addition, the results of this study could be helpful for many stakeholders such as working capital managers, debt holders, investors, financial consultants and others for monitoring the firms.Originality/valueThis study contributes to the existing literature in the relation between WCM efficiency and exogenous variables of the Indian manufacturing firms engaged in the export activities. Moreover, this study is one of the few research studies to investigate this relationship among Indian export firms in different industries, thus filling the gap in similar work done in other countries.

Operating liquidity and financial leverage are two significant aspects of overall firm management. This paper analyses the impact of financial leverage on various measures of operating liquidity. Further, we examine the effect of both operating liquidity and financial leverage on the firm's performance. We employ a sample of 151 Indian machinery firms and 10 years annual financial standalone data from 2004-05 to 2012-13 was collected using CMIE Prowess database. Ratio analysis and Panel data regression model have been applied to study the relationship. It was found that financial leverage has significant impact on different measures of operating liquidity. Further operating liquidity and financial leverage have considerable impact on performance of the Indian machinery firms. This study provides insights on interrelation between operation management and financial management and their impact on firm's performance.

PurposeThis study develops an integrated approach combining data envelopment analysis (DEA) and structural equation modeling (SEM) for estimating the working capital management (WCM) efficiency and evaluating the effects of diverse exogenous variables on the WCM efficiency and firms' performance.Design/methodology/approachDEA is applied for deriving WCM efficiency for 212 Indian manufacturing firms over a period from 2008 to 2019. Also, the effect of human capital (HC), structural capital (SC), cost of external financing (CEF), interest coverage (IC), leverage (LEV), net fixed asset ratio (NFA), asset turnover ratio (ATR) and productivity (PRD) on the WCM efficiency and firms' performance is examined using SEM.FindingsThe average mean efficiency scores ranging from 0.623 to 0.654 highlight the firms operating at around 60% of WCM efficiency only, which is a major concern for Indian manufacturing firms. Further, IC, LEV, NFA, ATR revealed direct effect on the WCM efficiency as well as indirect effect on firms' performance, whereas CEF had only a direct effect on WCM efficiency. HC, SC and PRD had no effects on WCM efficiency and firms' performance.Practical implicationsThe findings offer vital insights in guiding policy decisions for Indian manufacturing firms.Originality/valueThis study is the first to identify the endogenous nature of the relationship of HC, SC, CEF, IC altogether with firms' performance, compounded by the WCM efficiency, by applying a comprehensive methodology of DEA and SEM and provides an efficiency performance model for better decision-making.

Purpose The purpose of this study is to get insights into working capital management (WCM) practices and the determinants of its efficiency prevailing in the Indian manufacturing sector using firm-specific as well as macro-economic variables by examining three efficiency models, i.e. cash conversion cycle (CCC), cash conversion efficiency (CCE) and net working capital level (NWCL). Design/methodology/approach The study uses panel data techniques on 1,207 firms of the Indian manufacturing sector, as well as on its nine key manufacturing industries from 2008 to 2018 for the analysis. Findings Several firm-specific variables such as net fixed asset ratio, size of the firm, profitability, firm’s growth, asset turnover ratio, age of the firm, interest rate and leverage have significant effect on WCM efficiency, whereas total assets growth rate, gross domestic product growth rate and inflation rate have insignificant effect on WCM efficiency. Research limitations/implications The study provides new empirical evidence on the short-term liquidity management of manufacturing firms prevailing in the developing countries such as India. The findings are particularly relevant in the present scenario when the liquidity levels are decelerating and there is a marked slowdown in private credit flows to the manufacturing sector due to the problem of burgeoning non-performing assets. Originality/value This study examines WCM efficiency exhaustively by incorporating both firm-specific and macro-economic variables using three efficiency measures, i.e. CCC, CCE and NWCL, results of which emerged as an answer to an efficient WCM.

PurposeThis paper evaluates the working capital management (WCM) efficiency of the Indian manufacturing industries through data envelopment analysis (DEA) and empirically investigates the influence of several exogenous variables on the WCM efficiency.Design/methodology/approachWCM efficiency was calculated using BCC input-oriented DEA model. Further, the panel data fixed effect model was used on a sample of 1391 Indian manufacturing firms spread across nine industries, covering the period from 2008 to 2019.FindingsFirstly, the WCM efficiency of Indian manufacturing industries has been stable over the analysis period. Secondly, the capacity to generate internal resources, size, age, productivity, gross domestic product and interest rate significantly influence WCM efficiency.Research limitations/implicationsFirst, the selected study period has observed various economic uncertainties including demonetization and recession, so the scenario might differ in normal conditions or country-wise. Second, the findings might not be generalizable to the developed economies, since the current study sample belongs to a developing economy, which further provides scope for comparative study.Practical implicationsAn efficient model for managing the working capital comprising most vital determinants could enhance the firms' valuation and goodwill. Also, this study would be helpful for financial executives, manufacturers, policymakers, investors, researchers and other stakeholders.Originality/valueThis study estimates the industry-wise WCM efficiency of the Indian manufacturing sector and suggests measures to the concerned parties on areas to focus on and provide evidence on the estimated relationships of firm-level and macroeconomic determinants with WCM efficiency.

The study has been divided into two parts. The first part of the article analyzes the trends of capital structure of selected sample companies. The second part analyzes empirically the impact of leverage on firm’s value of selected sample companies. A sample size of 422 listed Indian manufacturing companies on Bombay Stock Exchange (BSE) has been taken to analyze the trends and leverage effect. A period of 10 years from 2003–2004 to 2012–2013 and annual financial standalone data have been considered to analyze the trends and leverage effect. Ratio analysis and panel data approach have been applied to perform the empirical study. It has been found empirically that there is a substantial debt level in the capital structure of the companies and there is no significant relationship between firm’s value and leverage using panel data fixed effect regression approach applied on four different models. In other words, leverage has no impact on the firm’s value in Indian manufacturing industry. However, variables such as size, age, profitability and growth of the firm are positively and significantly correlated with the firm value in Indian manufacturing industry. In addition, there is a significant relationship between firm value and industry practice of the firm. Business risk has no significant relationship with firm value. Thus, the findings of the study would enhance the literature on capital structure and is relevant for the Indian manufacturing industry in taking its capital structure decisions, as it is based on the most recent data and covers the period of both pre- and post-recession of 2008–2009.