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

Impact of Covid-19 on firms' performance: Empirical evidence from India

The current study aims to examine the impact of the pandemic (Covid-19) on the financial performance of some of the selected Indian sectors. The study targets all Indian firms listed on the Bombay stock exchange, which belong to the following sectors (Constructing, tourism and hospitality, food and consumer sectors). The study extracted data of 444 firms from the Prowess database for four sectors. Due to some missing values, the study dropped 73 firms. Therefore, the final sample of this study consists of 371 firms. Results revealed a significant difference in total income, net sales, net profit, earnings per share, and diluted earnings per share before and after the pandemic in tourism, hospitality, and consumer sectors. The result of the study states that there is a significant difference in total income net sales before and after the pandemic in construction. There is a difference in the decline in both sectors' net sales and total income during the pandemic. Conversely, there is no significant difference between net profit, earnings per share, diluted earnings per share before and after the pandemic in constructing and food sectors. Results of the study state that the food sector was not affected by the pandemic, whereas the construction sector reduced its expenses to their minimum. The study also found that the tourism, hospitality and customer sectors were the most effected by the Covid-19 pandemic, followed by the construction sector and food sector, which was a minor sector affected by the pandemic. Most of the prior research on Covid-19 is theoretical, and only a few have conducted an empirical investigation. The study is unique as it evaluates the financial performance of Indian firms before and after the Covid-19 pandemic, which has not been studied yet in the Indian context. Further, this study provides valuable insights to regulators and policymakers about the most affected sectors due to the pandemic by analysing Indian sectors

The impact of artificial intelligence on information audit usage: Evidence from developing countries

The present study aims to explore the factors influencing the utilization of Information audit in the context of Egypt and Jordan, with specific attention given to the role of artificial intelligence (AI). A sample of 443 respondents participated in the study, and data collection was carried out through a non-probability convenience and snowball sampling approach. The findings reveal that internal determinants are positively associated with the intention to adopt Information audit technologies, exhibiting a significant impact with a beta coefficient of +0.45 (P-value < 0.01), and the perceived benefits associated with their implementation. Moreover, the study underscores the critical influence of artificial intelligence, with dimensions such as cloud computing, data mining, and e-commerce enhancing the perceived advantages (β = 0.35, P-value < 0.01) and fostering the intent to use Information audit technologies (β = 0.22, P-value < 0.01). Additionally, there is a robust positive correlation between the intention to use Information audit technologies and their actual usage, where the presence of AI amplifies this association, indicated by a beta value of 0.48 (P-value < 0.01). This study significantly enriches the existing body of knowledge by delineating the determinants of Information audit usage, particularly within the Middle Eastern context, and highlights the pivotal role of artificial intelligence in shaping these dynamics. The study provides empirical evidence on the factors influencing the intention to use Information audit technologies, the perceived benefits associated with their usage, and the actual utilization of Information audit. Its originality lies in its focus on the underexplored Middle East region within the Information audit literature and its investigation of the influence of artificial intelligence on Information audit. The implications of this study are significant for practitioners, auditors, and policymakers operating within the Middle East region. The findings suggest that firms should allocate sufficient support and resources to encourage the adoption of Information audit technologies. Additionally, auditors need to have the necessary skills and knowledge to effectively use these technologies. Policymakers can use the study's findings to develop policies and regulations that promote the adoption of Information audit technologies