The impact of demonetization on Indian firms’ performance: does company’s age make a difference?

The main aim of this paper is to evaluate the impact of demonetization on Indian firm’s quarterly financial performance before and after demonetization period (March-December, 2017), and to find out if companies’ age helps to face financial disruption. Four variables, which are net sales, total income, net profit after tax, and earnings per share, were taken as proxies for analyzing the quarterly financial performance of 2,892 companies listed on Bombay Stock Exchange (BSE), National Stock Exchange (NSE), and Calcutta Stock Exchange (CSE). Nonparametric test, particularly Wilcoxon Matched-Pairs Signed Rank Test and Kruskal-Wallis one-way analysis of variance, were applied in analyzing the data. Results reveal that there is a statistically significant difference between the financial performance before and after demonetization at 5% level of significance. It was also found that the decrease/increase in the financial performance of all the firms was affected by the demonetization process, irrespective of their ages. The findings could be useful for financial managers and financial consultants, as they would be able to focus on the issues that matter most at the time of financial disruption

An analysis of working capital management in India: An urgent need to refocus

The current study aims to evaluate the impact of working capital components on the financial performance of Indian pharmaceutical companies. Moreover, it aims to analyze working capital among small, medium and large firms. The study uses a panel data of 82 pharmaceutical companies for the period from 2008 to 2017. Generalized Method of Moment (GMM) model is used for estimating the results. Findings show that there is a significant difference in managing working capital among small, medium and large firms. Furthermore, it is found that number of days’ collection period, number of days’ payable period and number of days’ inventory holding period positively impact the financial performance of Indian pharmaceutical companies measured by return on assets and net operating margin. Whereas, cash conversion cycle has a negative impact on return on assets, net operating margin and Tobin’s Q

The moderating effect of liquidity on the relationship between sustainability and firms' specifics: Empirical evidence from indian manufacturing sector

The current study attempts to examine the moderating effect of liquidity on the relationship between firms' specific and sustainability expenses. The study is based on secondary data over a period from 2015 to 2021. The results are estimated using panel data with fixed-effect models. The results indicate that liquidity enhances and strengthens the ability of a company to spend more on environmental, social, and employee compensation sustainability expenses. In the same context, the results reveal that there is an insignificant moderation effect of liquidity with the financial performance of a company, indicating that the liquidity of companies with higher financial performance does not enhance and strength their ability to spend more on sustainability expenses. Further, the extent of liquidity in larger companies affects positively and significantly the level of employee compensation but not environmental and social spending. Finally, the findings show that greater leverage with less liquidity negatively affects the levels of sustainability spending. This study provides a unique contribution to the existing literature by introducing the moderating effect of liquidity on the relationship between firms' specific and sustainability expenditures. It highlights the direct effect of firms' specific determinants and the moderating effect of liquidity on three categories of sustainability expenses which are environmental expenses, social expenses, and employee compensations. Therefore, this research has valuable implications for company managers, financial analysts, policymakers, and other stakeholders

Forecasting Indian Goods and Services Tax revenue using TBATS, ETS, Neural Networks, and hybrid time series models

AbstractThis study focuses on the crucial task of forecasting tax revenue for India, specifically the Goods and Services Tax (GST), which plays a pivotal role in fiscal spending and taxation policymaking. Practically, the GST time series datasets exhibit linear and non-linear fluctuations due to the dynamic economic environment, changes in tax rates and tax base, and tax non-compliance, posing challenges for accurate forecasting. Traditional time-series forecasting methods like ARIMA, assuming linearity, often yield inaccurate results. To address this, we explore alternative forecasting models, including Trigonometric Seasonality Box-Cox Transformation ARIMA errors Trend Seasonal components (TBATS) and Neural Networks: Artificial Neural Networks (ANN), Neural Networks for Autoregression (NNAR), which capture both linear and non-linear relationships. First, we test single time series models like Exponential Smoothing (ETS), TBATS, ANN, and NNAR. Second, we also test hybrid models combining linear models, non-linear models, and neural network models. The findings reveal that the Hybrid Theta-TBATS model offers superior forecasting accuracy, challenging recent research favouring neural network models. The study highlights the effectiveness of advanced non-linear models, particularly TBATS and its hybridisations with linear models, in GST revenue forecasting. Our study also found that the single TBATS is the second-best model, which offers better forecasting accuracy. These insights have significant implications for policymakers and researchers in taxation and fiscal planning, emphasising the need to incorporate non-linear dynamics and advanced modelling techniques to enhance the accuracy of GST revenue forecasts