impact of demonetization in india research paper

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Economic Impact of Demonetization: Critical Analysis on Social Impact in India

This research paper discusses the general effects of demonetisation on the economy with the notion of its influence in growth. In the research study, 3 important independent variables are selected that have influences towards the performance of demonetization in India. Indian economy took a momentous shift of banning high denomination notes calculated as 87 percent of total currency in November 2016. The independent variables are social impact, political impact and economic impact. These selected independent variables are the possible factors that might influence the performance of demonetisation recently happened in India. In this research study, 184 sets of questionnaires were prepared and distributed to the targeted respondents who are affected through this, and one more thing the target population is from the southwest part of India called Andhra Pradesh. After the data were collected, IBM SPSS was used to testing the data in order to generate the final result. In the end, the f...

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Kaustubh Kalyankar

The impact of demonetization is very huge and significantly major in terms of Indian economy. We have tried in this paper to find what are the factors and impacts of demonetization on the Indian economy and its people. For collection and analysis purpose we have collected and compare about 150 samples of people .The people for research were randomly distributed from the Vellore district located in Tamil Nadu state of India. After conducting analysis we came to the analysis that employment was the only key factor that affected the economy and had a severe impact on demonetization and level of qualification was the only factor that have any relationship with the view of people about whether the demonetization was applied correctly or not. It was also found out that the main reason according to the people for conducting demonetization by government were the cause of removing black money after some other causes which were seem to be important were terrorism and the battle against corrup...

IOSR Journals

The subject matter of this paper is a "Descriptive study on the impact of demonetization in India: From the perspective of society, politics and economic fraternity." Three important independent variables have selected for this study that might have influenced the performance of demonetization that had implemented in India in 2016. They are social impact, political impact and economic impact. The objectives of demonetization were linked to a variety of issues such as limitation of black money, removing forged currency and stopping terrorist funding. Quite a few academics have conducted their own analysis of demonetization and its effects. But most of the research works have addressed the partial and biased effects of the demonetization move since they have been carried out in the early months of the move. This paper finds that, in general, the effects of demonetization on the economy can be said to be balanced and unbiased. This is of major importance for stimulating investment in the economy, which in turn, has wider implications for the overall expansion and development. The main objective of this research is to study the relationship between social, political and economic impact towards demonetization. To conduct this research work, 184 sets of questionnaires were prepared and distributed to the targeted respondents who were affected through this process. The target population is from the state of Kerala, the southern part of India. The IBM SPSS was used to test the data collected in order to produce the final result. The final result shows that demonetization has had significant effect on the areas of social, political and economic areas of life.

SKIREC Publication- UGC Approved Journals

The impact of demonetization was felt in the social, political and economic sector. This action worst affected the poor and the common people. It was a step of moving toward cash less economy. This step was taken to curb the black money to a great extent. Salaried class was not still able to withdraw their salaries from the banks and ATMs as a result of cash deficit. Govt. encouraged doing financial transactions using mobile and other electronic means. Present study is focused on finding of social and economic impacts of demonetization.

India has amongst the highest level of currencies in circulation at 12.1% of GDP. Cash on hand is an estimated at around 3.2% of household assets, higher than investment in equities, or roughly around $ 220 billion. Of this cash, 87% is in the form of Rs 500 and Rs 1,000 notes or roughly Rs 14 lakh crore ($190 billion). Demonetization is a process by which a series of currency will not be legal tender. The series of currency will not be acceptable as valid currency. The demonetization was done in Nov 2016 in as an effort to stop counterfeiting of the current currency notes allegedly used for funding terrorism, as well as a crackdown on black money in the country. Demonetization is a generations' memorable experience and is going to be one of the economic events of our time. Its impact is felt by every Indian citizen. Demonetization affects the economy through the liquidity side. Its effect will be a telling one because nearly 86% of currency value in circulation was withdrawn without replacing bulk of it. As a result of the withdrawal of Rs 500 and Rs 1000 notes, there occurred huge gap in the currency composition as after Rs 100; Rs 2000 is the only denomination.

Ashwani Kumar

Withdrawing units of money from circulation is demonetisation; units of money are denied the status of legal tender. Demonetisation is defined as a process by which currency units will not remain legal tender. The currency notes will not be taken as valid currency. Demonetisation is a step taken by the government where currency units are ceased of its status as legal tender. Demonetisation is a basic condition to change national currency. In other words, demonetisation can be said a change of currency where new units of currency replace the old one. It may involve the introduction of new notes or coins of the same denomination or completely new denomination. The currency has been demonetised thrice in India. The first demonetisation was on 12th January 1946 (Saturday), second on 16th January 1978 (Monday) and the third was on 8th November 2016 (Tuesday). The study attempts to understand meaning and reasons of demonetisation, the sector-wise impact of demonetisation. This study also gives an insight into the positive and negative impact of demonetisation on Indian economy. This study is of descriptive nature so all the required and relevant data have been taken up from various journals, magazines for published papers and websites. Books have also been referred for theoretical information on the topic as required.

International Journal of Research and Analytical Reviews

Jyotika Kaur , DIVNEET BAGGA

‘Demonetisation was a good idea but had bad results’- this was the reaction of the Indian public with regards to demonetisation that was implemented in India in November 2016. The NDA Government implemented demonetisation with the objective to attack the evils like black money, corruption and extremism. The public of India welcomed demonetisation with open hearts though many hardships fell on the citizens which made it difficult to even cope with the daily activities. The GDP of the Nation fell instantly affecting the economy as a whole. The cash intensive nature of the Indian economy was hit badly experiencing a forty five year high unemployment rate, bringing the nation to a stand-still. The position of black money was hit initially but the fake notes of the new 2000 rupee currency again brought back the situation to square one. The paper highlights the impact of demonetisation on the Indian economy along with the impact on black money and unemployment. The primary data was collected to better understand and know the real picture with regards to status of black money and unemployment. The analysis of the impact of demonetisation becomes clear as a time frame of year and a half have passed from the time of its implementation, to learn from the same and formulate more effective policies to solve the problem of black money, corruption and extremism for a long duration.

IRA-International Journal of Management & Social Sciences (ISSN 2455-2267)

Farhat Mohsin

Turkish Journal of Computer and Mathematics Education

shyam kapri

Sosanh Albert

International Journal of Engineering and Management Research

Surabhi Srivastava

Demonetisation is an act of cancelling the legal tender status of a currency unit. It is a process when the government pulled out a unit of currency from the total circulation of the economy. The concept of demonetisation is not new, at first French used demonetisation then after most of the countries has adopted demonetisation to clean up the economy from corruption and inflation. India has adopted demonetisation three times: At first in January 1946 when RBI demonetised Rs. 1000 and Rs. 10000 currency notes. and again in 1978 by Moraji Desai of Rs. 1000, 5000, 10000 banknotes were demonetised and both demonetisation were held to eradicate black money. But the term Demonetisation became familiar on 8 November 2016 when P.M. Mr Narendra Damodar Das Modi announced Rs.500 and Rs.1000 currency notes will be no longer as legal tender status from the past midnight to unearth the corruption, black money and terror funding. Therefore this research paper is an attempt to throw the light on...

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Analyzing the Impact of Intellectual Capital on the Financial Performance: A Comparative Study of Indian Public and Private Sector Banks

• Published: 11 April 2024

Cite this article

• Monika Barak   ORCID: orcid.org/0000-0001-6598-3093 1 &
• Rakesh Kumar Sharma 1  

This paper explores the effect of intellectual capital (I.C) on the financial performance (F.P) of 12 public and 17 private sector banks in India. To get a comprehensive viewpoint, the study will try to answer the research question, i.e., Does the intellectual capital affect the financial performance of the Indian banks with respect to their multidimensionality? We used intellectual capital and financial performance as multidimensional constructs (human capital (H.C), capital employed (C.E), structural capital (S.C), and relational capital (R.C) for intellectual capital and return on assets (ROA), return on equity (ROE), return on capital employed (ROCE), and return on sales (ROS) for financial performance). Data were collected over 12 years, specifically from 2010 to 2022 from each bank. This study employed the modified value-added intellectual coefficient (MVAIC) measure as an alternative to the disputed value-added intellectual capital (VAIC) model to address the shortcomings of the previous research. The present study employed advanced longitudinal cointegration techniques to authenticate and validate the results. The fully modified ordinary least squares (FMOLS) method is employed to assess the effectiveness of the intellectual capital. The results suggest a positive relationship between human capital and all financial metrics, except for ROE, in the context of public sector banks. Further efficiency of capital employed and structural capital positively affects public sector bank financial performance indicators like ROA, ROE, ROCE, and ROS. Private sector banks have a negative correlation between relational capital and ROS whereas it demonstrates a positive association with ROCE. Similarly, there is a negative correlation between relational capital and both ROA and ROE in the case of public sector banks. For instance, the MVAIC model improves all financial performance measures except ROA, especially in private sector banking. The findings will assist executives, government officials, and policymakers in quantifying the efficiency and discerning the essential intellectual elements that enhance their effectiveness. Additionally, these findings will aid in devising strategies to foster and enhance their intellectual capacity.

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Data Availability

The datasets used and analyzed during this study are available upon reasonable request from the corresponding author.

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Barak, M., Sharma, R.K. Analyzing the Impact of Intellectual Capital on the Financial Performance: A Comparative Study of Indian Public and Private Sector Banks. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-01901-4

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• Published: 12 April 2024

Evaluating the effectiveness of training of managerial and non-managerial bank employees using Kirkpatrick’s model for evaluation of training

• Kayenaat Bahl 1 ,
• Ravi Kiran 1 &
• Anupam Sharma 1  

Humanities and Social Sciences Communications volume  11 , Article number:  508 ( 2024 ) Cite this article

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This research employs Kirkpatrick’s model, to assess the efficacy of training programmes for managerial and non-managerial employees in the banking sector using all the four components i.e., Reaction, Learning, Behavior and Results. Data were collected from 402 respondents from public, private and foreign sector banks. SEM-PLS was used to determine the relationship between the levels of the Kirkpatrick’s model for the evaluation of the training programs of the baking sector. The results suggest that all the four levels are interlinked and Kirkpatrick model was effective to evaluate the impact of training programs on employee motivation and bank performance. Reactions of employees (stage 1) have enhanced knowledge, and skills and has a positive and significant influence on learning (stage 2) and Behavior through job performance (stage 3) has a positive impact on results (stage 4). The results reflected that the adjusted R 2  = 0.732 of the managerial level is more than that of the non-managerial level ( R 2  = 0.571). This indicates that the training is more effective on the Managerial level than the Non-Managerial level and that the managerial employees are more skilled and experienced in their jobs. The study is novel and one of the initial contributions to apply Kirkpatrick’s model to banking sector in India.

Introduction

Training is crucial for enhancing employee development by emphasizing productivity optimization to meet customer satisfaction. Banks regard it as the utmost crucial objective. Employee training involves implementing programmes that provide individuals with the requisite knowledge and skills to effectively carry out a particular job or to acquire additional competencies in anticipation of future growth in their professional careers. It is a process that aligns the growth of the employee with the goals and procedures of the organization. The demand for employee training in banks has intensified as a result of the increasing competition in the banking sector. Evaluating the effectiveness of training programmes is a crucial priority for financial institutions. The Indian banking industry is currently facing substantial challenges arising from deregulation, demonetization, digitalization, and bank consolidation. These factors have intensified the need for job enrichment. Irrespective of their qualifications, every bank is required to offer their employees essential training. Induction or orientation is a compulsory training programme for all bank employees worldwide.

Banks are employing supplementary training programmes, in addition to induction programmes, to acquaint their employees with specific skills in anticipation of the introduction of e-banking. The dynamic nature of the environment has required an evaluation of the employee training and development programmes. Rani and Garg ( 2014 ) argue that it is extremely important to create a training culture that is integrated and systematic among bank employees, rather than providing training on an as-needed basis. According to Das ( 2018 ), it is crucial for financial institutions to establish a training system that is comprehensive and includes both quantitative and qualitative elements. In order to accomplish this, it is imperative to create specialized software for skill training and recruit the most highly qualified trainers available to incentivize them to engage in research and cultivate expertize. Furthermore, it is imperative to establish a proficient system for appraising training requirements and assessing performance. The need for enhanced training facilities and evaluation methods in the banking industry highlights the importance of creating a model that can accurately identify the required conditions to reflect the current state of banks.

Kirkpatrick’s Model, developed in 1954, made a substantial contribution to the formulation of strategies aimed at ensuring the acquisition of behavior and skills. The present study assessed the training programmes for banking employees utilizing Kirkpatrick’s four-tiered model. Employing Kirkpatrick’s model for evaluation is crucial for gauging the overall efficacy. This will streamline the process of evaluating whether to continue with the training programme, terminate it, or make any required modifications. The study elucidates the utilization of Kirkpatrick’s model to develop a framework that is effective for the banking sector to evaluate the effectiveness of training for bank employees and to measure the influence of that training on their behavioral results.

The present study is further classified on the basis of the objectives given below:

O1: To examine the reactions of the employees on their learning ability, learning on the behavioral changes of the employees and the impact of the Behavior on the overall Results of the training.

O2: To analyze the impact of training on the Managerial and the Non-Managerial levels of the Banking Sector

O3: To create a Model based on the impact of reactions on learning ability, learning on the behavior of the employees and finally analyzing the results to incorporate changes in the training program separately for both the Managerial and the Non-Managerial levels.

Many researchers employ Kirkpatrick’s Model to evaluate training programmes. As the level of evaluation increases, the process becomes more difficult and time-consuming. The banking industry, being a major contributor to the global gross domestic product, has created employment opportunities for billions of individuals across the globe. Therefore, it is the responsibility of financial institutions to make sure that their staff members receive sufficient training and that the training programmes are regularly evaluated. Training the current staff is a more cost-effective option compared to hiring a high-priced professional. Training is a means by which employees gain organized knowledge to enhance their skills, growth, and cultivate a favorable mindset towards carrying out a particular job. Proficient employees are crucial to a company’s success (Mathur and Pandaya, 2019 ). The significance of offering employees timely and pertinent training is emphasized by the scarcity of resources and the increasing expectations, as elucidated by Benson and Dundis ( 2003 ). Despite assessment being a crucial component of staff training processes, Rajeev et al. ( 2009 ) claim that training programmes are inconsistent and lacking in adequate evaluation. Therefore, drawing from these viewpoints, the present study endeavors to offer a response to the subsequent research question:

Does data from training program implemented at the banking industry support the use of Kirkpatrick’s Model for evaluation (Kirkpatrick and Kirkpatrick, 2006 )?

The following is the outline of the article. In “Introduction”, we build on what has been learned from the previous literature to show how important the training programmes are and to argue that they should be regularized soon so that performance and results can improve. This section also explains the original Kirkpatrick’s model and how the study built upon it to use a conceptual model to evaluate the impact of training programmes for bank employees. “Literature Review” details a review of previous research that focused on how training affected the outcomes for scientists who have used Kirkpatrick’s Model. “Theoretical Underpinning and hypotheses development” then moves on to talk about how theories and hypotheses development. Also included here is a comprehensive rundown of all the steps needed to evaluate the training methods. “Research Design and Methodology” details the study’s population, sample, scale, and methodology. Using the four stages of Kirkpatrick’s Model—Reaction, Learning, Behavior, and Results—this study aims to construct a PLS-SEM-based model to evaluate training programmes. “Data analysis” provides a comprehensive summary of the investigation’s findings. Discussion, limitations, implications, and suggestions for further study make up “Discussion and conclusion”.

Literature review

Evaluation of training entails systematically reviewing descriptive and judgmental data necessary for making good training decisions (Werner and DeSimone, 2012 ). Assessing the impact of training on the behavioral modifications of the organization’s employees is one of the main procedures to determine the efficacy of the training. An in-depth assessment is required for every training programme before its worth can be determined. The training has a significant impact on how employees behave within an organization. Employees’ improved behavior is a direct result of their training’s emphasis on knowledge retention, skill development, and situational awareness. According to Mohamed et al. ( 2012 ), when it comes to assessing training programmes, Kirkpatrick’s Model works well for knowledge transfer from the individual to the workplace. In the 50 years after its inception, Kirkpatrick’s model has remained the gold standard for assessing the efficacy of training (Saxena, 2020 ). Evaluating training in the banking industry should focus more on the training itself, not just the results, to increase the effectiveness of training for employees. As a result, this study used Kirkpatrick’s four-leveled model to evaluate the training procedure’s effectiveness.

The current study utilized the conceptual framework of Kirkpatrick’s Model (Fig. 1 ) to analyse the impact of training on the banking staff. The evaluation process is broken down into four separate steps according to the Kirkpatrick model. Included in this category are Reactions, Behavior, Learning, and Outcome. Training evaluation is important and crucial because if the results aren’t what were hoped for, then the whole training process was a waste of time and money. Overall, the research has used all four levels of Kirkpatrick’s Model to evaluate the benefits of training programmes in the banking industry. Adapted from Kirkpatrick’s model, Fig. 1 shows the stages used to evaluate the effectiveness of training for banking sector employees. Employee reactions to the training itself make up the first phase. The next step is to put all that training into practice by learning new things that are relevant to what you already know. In the third phase, workers adjust their actions in accordance with what they’ve learned and how they’ve handled certain situations so that the bank can run more smoothly. The last step is to evaluate the findings in the context of the overall efficiency and effectiveness of the banking industry and its personnel.

Kirkpatrick’s Model with respect to Banking Staff Training Programs.

In Level 1 the reactions of the employees to the training programs are considered and measured for further imparting in depth information and skills. Level 1 consists of the various training programs i.e., On the Job training, Off the Job training and Special training being imparted to the trainees and to observe their reactions for the same.

Level 2 consists of the Learning and absorbing the information being imparted with the help of BSC (Balanced Score Card) and its four perspectives i.e., Financial, Customer, Internal Business Process and Growth (Social and Environmental) perspectives.

Level 3 consists of the behavioral changes as a result of the factors affecting the Banking environment and job performance w.r.t. Digitalization, Demonetization, Job Enrichment; and Consolidation of banks.

Level 4 concludes with results for assessing the training programmes’ overall effectiveness, as measured by the benefits to employee motivation and bank performance.

Literature support and application of Kirkpatrick model in different sectors is depicted through Table 1 .

Theoretical underpinning and hypotheses development

The purpose of this research is to look at how the four stages of Kirkpatrick’s Model (Reactions, Learning, and Behavior) interact with each other. This research set out to clarify how these phases differ at the managerial and non-managerial echelons.

In order to compare how well the three different training methods employed, we use the first level of Kirkpatrick’s model. Employees’ knowledge, attitudes, behaviors, and performance are all improved through on-the-job training. Creative output, goal attainment, and monetary gain are all profoundly affected. According to Lin and Hsu ( 2017 ), there is a positive relation between on-the-job training and employees’ work performance, personality traits, work behavior, and overall job achievement. Lynch ( 2014 ) found the opposite to be true: that off-the-job training increases the probability of employee turnover, while on-the-job training increases tenure. Since off-the-job training is more targeted and on-the-job training is broad, he argues that women are more inclined to quit an employer when it comes to the former. It is essential to provide staff with specialized training to help them interact with customers effectively, according to Ali and Ratwani ( 2017 ), because employees’ attitudes change and vary in response to customer demands. Workers are empowered to perform to their full potential when they have access to a well-crafted training programme.

H1: There is a difference in reactions to different forms of training (On the Job, Off the Job and Special training) for managerial and non-managerial employees.

H1a: There is a difference in reactions to different forms of training (On the Job, Off the Job and Special training) for managerial employees.

H1b: There is a difference in reactions to different forms of training (On the Job, Off the Job and Special training) for non-managerial employees.

Kirkpatrick’s model associates Level 2 with learning based on training. There can be no greater importance than figuring out how much training has improved knowledge and abilities to positively impact learning. Although there are a variety of models that can be used to evaluate learning, the balanced score card (BSC) is used in this study to examine how reactions impact learning. According to Tuan’s ( 2020 ) research, commercial banks in Vietnam saw substantial gains in performance after adopting the balanced scorecard as an evaluation tool. Organizations can benefit from BSC because it allows for more balanced evaluations of bank and employee performance in addition to overall company performance. Consequently, the BSC and its four perspectives greatly influence employee performance because they make it easier to evaluate their learning capacity.

H2: There is a difference in the learning ability of managerial and non-managerial employees for the four perspectives, viz. financial, internal business process, customer and growth (social and environmental) perspectives.

H3: The reactions to the training (Level 1) has a significant influence on learning (Level 2).

H3a: The reactions to the training (Level 1) has a significant influence on learning (Level 2) for the managerial employees.

H3b: The reactions to the training (Level 1) has a significant influence on learning (Level 1) for the non-managerial employees.

In Kirkpatrick’s model, the impact of actions on productivity in the workplace is investigated at the third level. Thanks to this insightful evaluation data, we can improve the training programmes with confidence. We looked at it in this study by analyzing the efforts of the employees to adjust to the constantly shifting business climate, which encompassed digitalization, demonetization, and bank consolidation. Furthermore, it tackles diversity through enhancing job opportunities. The factors that impact performance and the ways in which employees cope with these factors are the causes of behavioral changes. Workers in the banking industry have been able to improve their efficiency, manage customer relations better, offer services that go beyond their physical capabilities, and handle information management more effectively thanks to digitalization. Based on statistical evidence presented by AL-Ahdal et al. ( 2018 ), demonetization had a substantial impact on the firm’s financial performance. Zunzunegui ( 2018 ) asserts that modern banks give their customers more control over their personal data, which they can then use in partnership with tech companies to improve their services and earn their trust. A number of elements affect employee motivation, including job enrichment, pay, and training opportunities. Each company needs to find a good way to pay their employees and provide them opportunities to grow professionally and personally (Tumi et al. 2021 ). When it comes to banking institutions, high-performance work systems are a great way to boost productivity and output.

Since the merged banks are still following all applicable laws and regulations, Prakash et al. ( 2018 ) state that consolidation has had no effect on the long-term viability of the banking industry. Declining production, branch closures, unregulated customer service, and increased employee workload are the main challenges facing India’s baking industry. Just as Kambar ( 2019 ) asserted. The current research looks at how employees’ actions are correlated with their attempts to adjust to ever-changing corporate conditions, including digitalization, demonetization, bank consolidation, and job enrichment, in order to evaluate performance at this level.

H4: There is a difference in the Behavioral changes of managerial and non-managerial employees w.r.t to the factors influencing the functioning of the banks (Digitalization, Demonetization, Job Enrichment and Consolidation of Banks).

H5a: Learning (level 2) has a significant influence on Behavioral changes (level3) of the managerial and non- managerial employees.

H5a: Learning (level 2) has a significant influence on Behavioral changes (level3) of the managerial employees.

H5b: Learning (level2) has a significant influence on Behavioral changes (level 3) of the non-managerial employees.

Training has several positive effects on performance, including but not limited to: employees’ capacity for self-management, technical skill development, cross-cultural competence, innovation, and tacit expertize (Aguinis and Kraiger, 2009 ). Methods used to educate employees have a significant impact on a company’s image (Clady, 2018 ). Benefits include individual and team financial health as well as national financial health (Janev et al., 2018 ).

To get the most out of training, it’s important to pay attention to the following: need assessment, trainees’ pre-training state, training design and delivery, training evaluation, and training transfer. Providing training to employees has many benefits, but only if the evaluation is done correctly. Therefore, it is necessary to investigate the benefits of training in relation to the performance of the bank and the motivation of its employees. A well-organized training programme is essential for all employees, including those in managerial and non-management roles. At the managerial levels, competences and skills matter greatly. Finding qualified candidates for managerial positions at all levels is difficult for most companies. Aslan and Pamukcu ( 2017 ) opine managers are in a position to make choices that might have a major impact on their employees’ emotional and physical well-being. Training that takes place in an organizational context also has an effect on the managers’ mental health and their level of competence. The authors of the study are Yahya and colleagues ( 2018 ). Therefore, it is highly beneficial to have a structured training programme for managers in the banking sector. Research into the impacts of development and training programmes on banking industry managers and non-managers is, therefore, essential, as is an evaluation of the present training programmes’ ability to boost productivity in the banking sector.

H6: Behavioral changes (level 3) has a significant impact on the results, viz. individual and bank performance (level 4) for managerial and non-managerial employees.

H6a: Behavioral changes (level 3) has a significant impact on the results, viz. individual and bank performance (level 4) for managerial employees.

H6b: Behavioral changes (level 3) has a significant impact on the results, viz. individual and bank performance (level 4) for non-managerial employees.

H7: Training has a significant impact on results, viz. individual and bank performance for managerial as well as non-managerial employees.

H7a: Training has a significant impact on results, viz. individual and bank performance for managerial employees.

H7b: Training has a significant impact on results, viz. individual and bank performance for the non-managerial employees.

The next stage is to give details of dataset, research design and methodology. This has been covered in “Data analysis”.

Research design and methodology

Target population and sample size.

Data for the present study were gathered from employees of public, private, and foreign sector banks via a structured questionnaire. The responses pertain to the banks’ contribution to the Indian economy, with public sector banks accounting for 66%, and private sector banks for 28% and foreign banks 6%. A grand total of 402 responses were gathered for the purpose of analysis and interpretation, despite the data collection being impeded by the Covid-19 pandemic. There are 125 banks from the public sector, 55 banks from the private sector, and 15 foreign banks included in the count. The research encompasses the following financial institutions: Axis, Punjab National Bank, SIDBI, IDBI, Yes Bank, J & K bank, Canara, Punjab and Sind Bank, Punjab Gramin Bank, Syndicate Bank, and State Bank of India. Branch heads, assistant managers, regional heads, senior managers, associates, probationary officers, clerks, and chief managers were among the personnel affected. A further division was made into level 1 (managerial personnel) and level 2 (non-managerial personnel). The primary objective of this research is to utilize Kirkpatrick’s Model in order to improve the efficiency of training programmes for managerial and non-managerial personnel in the banking industry.

Research methods

The current investigation employed PLS-SEM model to ascertain the proposed measurement and structural model, which is a widely recognized and utilized technique across multiple research domains. Legate et al. ( 2023 ) found that for many years, Covariance-based Structural Equation Modeling (CB-SEM) has been the method of choice for interpreting intricate interrelationships between latent and observed variables. In 2010, a relatively greater number of published articles incorporated PLS-SEM, as reported by Hair et al. ( 2017 ). Perceptron-Last SEM (PLS-SEM) is a causal predictive methodology within SEM that prioritizes prediction when assessing statistical models designed to provide imaginative explanations (Wold, 1982 ; Sarstedt et al., 2017 ). PLS-SEM is typically applied to data with non-normal distribution and small sample sizes. It facilitates the estimation of values for numerous interdependent relationships among variables and the application of construct measurement (Ittner et al. 1997 ). Given the limited sample size, extensive application, and general acceptance of PLS-SEM, it was deemed appropriate to utilize this method to assess the efficacy of training in the banking industry.

Data analysis

Kirkpatrick’s model for evaluation of effectiveness of training for managerial employees, measurement model, reliability and validity.

Before analyzing Kirkpatrick’s model’s four tiers, construct reliability and validity have been assessed. According to Fornell and Larcker ( 1981 ), factor loadings and average variance extracted (AVE) are used to assess construct convergent validity. Table 2 shows that the AVE ranges from 0.623 to 0.799, meeting Brown and Moore ( 2012 )‘s threshold. Nunnally ( 1978 ) considered composite reliability (CR) values acceptable if they meet 0.70. Cronbach Alpha values range from 0.748 to 0.915, while composite reliability is 0.868 to 0.940. The model has acceptable construct validity and reliability.

Discriminant validity

Discriminant validity was determined by comparing square root of AVE values to inner construct correlations. Table 3 shows that square root of AVE was greater than construct correlations, indicating acceptable discriminant validity. The HTMT criterion aids discriminant validity assessment. As per HTMT criterion, the acceptable level of discriminant validity is suggested to be less than 0.90 between reflective constructs (Hair and Alamer, 2022 ). Kirkpatrick’s Model’s four stages had values below 0.90. Thus, discriminant validity between variables is established. Given that all the stipulations have been fulfilled, it was deemed suitable to proceed with the analysis.

Variance inflation factor

Variance Inflation Factor VIF values greater than 3 reflect the presence of collinearity (Hair, Ringle and Sarstedt 2011 ). As reflected through Table 4 , all VIF values are all lesser than the threshold limit of 3, therefore no indicator was removed.

The study was done with the major objective to examine the effectiveness of training on managerial employees with the help of Kirkpatrick model, basically to examine the impact of training programs on results, viz. employee motivation and bank performance (Table 5 ). Before continuing structural modeling, all factor outer loadings must be checked. All factors had significant values ( p  ≤ 0.01) and values above 0.70 (threshold level). No factor was eliminated.

The four levels of Kirkpatrick model are: reaction to training; learning w.r.t. financial, internal business process, customer and growth (social and environmental) perspectives; behavior; and results were taken to analyze the impact of training on the performance of the employees and bank performance (Fig. 2 ). This study examined how reactions affected on-the-job, off-the-job, and special training programmes. The initial hypothesis: H1: There is a difference in reactions to different forms of training (On the Job, Off the Job and Special training) for managerial and non-managerial employees . Table 6 shows that on-the-job training has 0.914 outer weights, off-the-job training 0.911, and special training 0.747. Each is significant and greater than 0.70. Of these three types of training, off-the-job training loaded more items. The least loadings were for special training. H1a: Managerial employees react differently to on-the-job, off-the-job, and special training has been accepted. Findings highlight that special training, which had lower outer weights requires added focus.

Kirkpatrick’s Model for Evaluation of effectiveness of training for Managerial Employees.

The next objective was to assess how much information was retained and whether the training led to financial, customer, internal business process, and growth (social and environmental) learning. The related hypothesis is H2: There is a difference in the learning ability of managerial and non-managerial employees for the four perspectives, viz. financial, internal business process, customer and growth (social and environmental) perspectives . The outer loadings of all four learning perspectives were greater than 0.70, between 0.837 and 0.920, and significant. Thus, the training taught financial, customer, internal business process, and growth (social and environmental) perspectives. Hence H2 has been empirically supported and there is a difference in the learning ability of managerial employees for the four perspectives, viz. financial, internal business process, customer and growth (social and environmental) perspectives at the managerial level.

The next hypothesis linking stage 1 reactions to training to level 2 learning is H3: The reactions to the training (Level 1) has a significant influence on learning (Level 2). As we can see from path co-efficient given in Table 6 and PLS-SEM Fig. 2 , the Beta value for Reactions to Training -> Learning is 0.663 and is significant (T:14.366; p  ≤ 0.01). Thus, the reactions to the training (Level 1) has a significant influence on learning (level 2) has been empirically supported.

The third objective of the study was to measure how much training has influenced learning and how learning has influenced employee behavior to evaluate their application of information to deal with factors influencing the banking environment and employee performance, such as digitalization, demonetization, bank consolidation, and job enrichment. This study examined whether behavioral changes helped employees cope with dynamic bank performance factors. The outer weights indicate that employees struggled with demonetization and bank consolidation. These two are recent and may require training. Thus, this must be addressed. Results show that job enrichment and digitalization are more important. New training modules should cover bank consolidation and demonetization. Hence, we can say that hypothesis H4: There is a difference in the Behavioral changes of managerial employees w.r.t to the factors influencing the banks (Digitalization, Demonetization, Job Enrichment and Consolidation of Banks) has been accepted.

The next hypothesis was linking learning with Behavioral changes. As we can see from path co-efficient given in Table 6 and PLS-SEM Fig. 2 , the Beta value for Learning -> Behavioral changes is 0.663 and is significant (T: 21.931; p  ≤ 0.01). Thus, H5a: Learning (level 2) has a significant influence on Behavioral changes (level3) of the managerial employees had been empirically supported .

Lastly, the results were measured and analyzed to observe whether the Behavioral changes through job performance (level 3) have a positive impact on results, viz. employee motivation and bank performance (level 4). Outer weights of benefits in terms of employee motivation and bank performance are quite high. Moreover, the Beta value for Behavioral changes -> Results is 0.856 and is significant (T: 35.409; p  ≤ 0.01). On the basis of these results, we accept H6a: Behavioral changes (level 3) has a significant impact on the results, viz. individual and bank performance (level 4) for managerial employees.

The highlighted paths of bootstrapping results are shown through Fig. 3 . All paths are significant as indicated through the Fig. 3 .

Bootstrapping Model for Kirkpatrick’s Model Evaluation of effectiveness of training for Managerial Employees.

The last hypothesis is H7a: Training has a significant impact on results, viz. individual and bank performance for managerial employees. The effectiveness of training for managerial employees can be gauged from the R 2 value of 0.733 and adj. R 2 value of 0.732 suggesting that this model for managers explains 73.2% of total variation. The total effects are also shown through Table 7 , highlight Reactions to Training -> Learning (0.663); Reactions to Training -> Behavioral Changes (0.526) and Reactions to Training -> Results (0.450) are all significant. Further Learning -> Behavioral Changes (0.793) and Learning -> Results (0.679) are also positive and significant. Lastly, Behavioral Changes -> Results. The beta value is high (0.856) and is also positive and significant. Training-related learning, behavioral changes, and results are all important. Behavioral changes and results from further learning are also positive and significant. Last, Behavioral Changes -> Results is positive and significant. Thus, H7a is empirically supported. The training programmes improved employees’ skills to cope with the dynamic banking sector and factors affecting bank performance, which significantly impacted employee motivation and bank performance.

Kirkpatrick’s model for evaluation of effectiveness of training for non-managerial employees

For the four levels of Kirkpatrick’s model, it is vital to assess the reliability and validity of constructs for non-managerial level. The non-managerial model has AVE between 0.637 and 0.914 (Table 8 ). Composite reliability is 0.873–0.945 and Cronbach Alpha is 0.805–0.923. Model construct validity and reliability are good for non-managerial level too.

As shown through Table 9 , the discriminant validity is also acceptable for non-managerial employees. The square of AVE values is higher than inner construct correlations and HTMT ratios are below 0.90 and are thus acceptable.

Variance Inflation Factor VIF values should not exceed 3. The absence of collinearity is illustrated in Table 10 for the outer VIF are less than 3 (Hair, Ringle, and Sarstedt, 2011 ). Thus, we proceeded with all indicators, for further analysis.

The initial hypothesis associated with the Kirkpatrick model for assessing the impact of training on outcomes is as follows: H1b: Non-managerial employees exhibit distinct responses to various types of training (on-the-job, off-the-job, and special training). The outer weights for On-the-Job training (0.880), Off-the-Job training (0.921), and Special Training (0.724) are detailed in Table 11 . This indicates that employees at the non-managerial level place greater importance on off-the-job training, as it carries the highest loading. This differs from managerial levels, where on-the-job training carries a greater workload. The fact that special training entails reduced workloads for both managerial and non-managerial personnel may suggest that these aspects should be prioritized in future efforts to enhance this form of training. Specialized training is in greater need of attention in light of recent developments in the banking industry. Nonetheless, all of these values exceed 0.70 and were thus significant. As a result, the hypothesis H1b, which states that non-managerial employees respond differently to various types of training (on-the-job, off-the-job, and special training), is supported. At banks, it is time to transition from routine orientation programmes to alternative formats.

The next objective was to investigate the relationship between responses to training (Level 1) and learning (Level 2), specifically from the perspectives of finance, customers, internal business processes, and growth (social and environmental aspects). H2b: There is a difference in the learning ability of non-managerial employees for the four perspectives, viz. financial, internal business process, customer and growth (social and environmental) perspectives. The outer loadings of all the four perspectives of learning were greater than 0.70, in the range of 0.879 and 0.915 with p  ≤ 0.01. Hence, it can be inferred that the training led to learning from financial; customer; internal business process; and growth (social and environmental) perspectives. Hence, this hypothesis has been empirically supported for non-managerial employees. The third hypothesis was whether reactions to training (level) were influencing level 2 learning. As we can see from path co-efficient given in Table 12 and Fig. 4 , the Beta value for Reactions to Training ->Learning is 0.589 and is significant (T: 14.058; p  ≤ 0.01). Therefore, H3b, which states that non-managerial employees’ reactions to the training (Level 1) have a substantial impact on their learning (Level 2), is supported by empirical evidence.

Kirkpatrick’s Model for Evaluation of effectiveness of training for non-managerial Employees.

After analyzing relation between reactions to training to learning, the next step was to relate learning with behavior. It was to examine whether this learning through behavioral changes helped employees to deal effectively with the dynamic factors, viz. Digitalization, Demonetization, Consolidation of banks and behavioral changes related with Job Enrichment. The loadings for behavioral changes related with Job Enrichment had highest loading indicating its supremacy. From behavioral changes related Consolidation of banks, had the lowest loading, and Demonetization to had lesser loading, indicating that non-managerial employees were not dealing with Consolidation of banks and demonetization effectively. Consequently, we can say that hypothesis H4: There is a difference in the Behavioral changes of non-managerial employees w.r.t to the factors influencing the banks (Digitalization, Demonetization, Job Enrichment and Consolidation of Banks) has been accepted.

The next hypothesis was to see whether learning is related with Behavioral changes. As we can see from path co-efficient given in Table 12 and Fig. 4 , the Beta value for Learning -> Behavioral changes is 0.711 and is significant (T:17.485; p  ≤ 0.01). Thus, H5a: Learning (level 2) has a significant influence on Behavioral changes (level3) of the non-managerial employees had been empirically supported.

It is vital to access whether the Behavioral changes through job performance (level 3) have a positive impact on results, viz. employee motivation and bank performance (level 4). Outer loadings of benefits in terms of employee motivation and bank performance are quite high. Moreover, the Beta value for Behavioral changes -> Results is 0.757 and is significant (T:20.302; p  ≤ 0.01). On the basis of these results, we accept H6b: Behavioral changes (level 3) have a significant impact on the results, viz. individual and bank performance (level 4) for non-managerial employees. The highlighted paths of bootstrapping results are shown through Fig. 5 . All paths are significant as indicated through the Fig. 5 .

Bootstrapping Model for Kirkpatrick’s Model for Evaluation of effectiveness of training for non-managerial Employees.

The last hypothesis related with non-managerial employees is H7b: Training has a significant impact on results, viz. individual and bank performance for managerial employees. From R 2 Value of 0.573 and adj. R 2 Value of 0.571, it can be indicated that model for non-managers explains 57.1% of total variation. The total effects are also shown through Table 13 . As reflected, Reactions to Training -> Learning (0.589); Reactions to Training -> Behavioral Changes (0.419) and Reactions to Training -> Results (0.317) are all significant. Further Learning -> Behavioral Changes (0.711) and Learning -> Results (0.539) are also positive and significant. Lastly, Behavioral Changes -> Results. The beta value is high (0.757) and is also positive and significant. Hence hypothesis H7b: Training has a significant impact on results, viz. individual and bank performance for managerial employees has been empirically supported. Thus, on the basis of these results, it can be concluded that the training programs were effective as reflected through all stages of Kirkpatrick model. Till yet, very little research covers gauging effectiveness of training using Kirkpatrick model. This study has set the stage, where the differences at managerial and non-managerial level may be considered to enhance the effectiveness of training.

Overall results highlight that in case of managerial model the R 2  = 0.733 and the adjusted R 2  = 0.732 are higher than that for the non-managerial level with R 2  = 0.573 and adjusted R 2  = 0.571. This indicates that the training is effective for both the managerial level than the non-managerial level, but is more successful for managerial employees.

Discussion and conclusion

The findings provide evidence that responses to training influence learning, learning influences behavior, and behavior influences the overall outcomes (Table 14 ). Training must be linked to determinants such as demonetization, consolidation of banks, and digitization, which have a significant impact on the overall banking environment. Additionally, job enrichment must be taken-into-account, particularly when attempting to influence behavior. Four perspectives are utilized to analyse the employees’ favorable response to receiving training and assimilating the information through learning for application: financial, internal business process, customer, and growth (social and environmental). The efficacy of training programmes as a whole is demonstrated by the outcomes of training benefits. The researchers in this particular domain hold optimistic views regarding the performance of banks; however, as each passing phase in this sector unfolds, evaluation methods must be improved. Kirkpatrick’s model is widely utilized as a standard for assessing programmes across diverse industries, including but not limited to the pharmaceutical, medical, and aviation sectors (Li et al. 2020 ; Firooznia et al. 2020 ; Buriak and Ayars, 2019 ; Mishra et al. 2020 ). Diverse departments have implemented the various phases of Kirkpatrick’s Model of Training Evaluation both collectively and individually. However, the number of studies that have contributed to the evaluation of training in the banking industry is negligible, rendering it a pertinent subject for discourse.

The current investigation aids in the assessment of training in the banking industry and derives its conclusions from the primary data gathered from 402 respondents. The study is predicated on an examination of the determinants of bank performance, namely consolidation of banks, demonetization, and digitization, as well as job enrichment. Additionally, the types of training provided by banks, including on-the-job training, off-the-job training, and special training, are considered. Numerous autonomous investigations have been conducted on the correlation between Kirkpatrick’s Model and the determinants of bank performance. However, this scholarly article capitalizes on the training’s merits and examines the aforementioned factors before employing the Kirkpatrick’s Model for Evaluation of Training Programmes to assess the effectiveness of the training programmes. In addition, the results emphasize the importance of special training in the management of demonetization and bank consolidation. Nonetheless, it does underscore the correlation between bank performance and employee motivation resulting from training programmes. Therefore, it can be deduced that the Kirkpatrick model demonstrated efficacy in assessing the effects of training programmes on outcomes, behavior, and learning.

Practical implications

The results of the study indicate that there is an urgent need to train banking sector employees in accordance with their managerial and non-managerial positions, and that the training they are currently receiving is producing significant results. The training initiatives implemented within the banking industry exert a substantial influence on the behavior of personnel. In response to change drivers, the managerial level is subject to a greater number of changes than the non-managerial level. In light of the findings of this study, it is imperative that special training be emphasized in order to facilitate a more effective response to the dynamic changes occurring in the banking sector, particularly in regards to demonetization, consolidation of banks, and digitalization (which are change drivers). The study suggests that there is a necessity to concentrate on comprehending the potential efficacy of training through behavioral modifications and learning in order to improve job performance and the overall performance of the bank for both managerial and non-managerial staff.

Limitations and future scope

This study is based on a sample of 402 respondents. An expansion of the sample size could be considered in order to assess the comprehensive efficacy of the training. An expansion of the research could involve conducting a bank-by-bank examination of training programmes and assessing their influence on performance. Korde and Laghate ( 2015 ) assert that training and development programmes have a significant influence on the performance of banks. However, further research is required to compare the effects of training programmes in private and public sector banks and to offer recommendations. Based on an analysis of training responses and performance indicators across various banking sectors, the present study concludes that while training is advantageous for all sectors, managerial level employees in the banking industry necessitate particular attention in terms of training needs and self-improvement. Subsequent research may additionally account for the variations in training programmes, construct an industry-specific model, and derive conclusions. Drawing distinctions and developing a model based on an analysis of the current training environment in the banking industry are facilitated by the present study.

Data availability

The data supporting the findings of the study are available from the corresponding authors upon reasonable request.

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Acknowledgements

We thank all the staff of the Banking Institutions of different sectors for their participation in fulfilling the requirements of the questionnaire.

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Bahl, K., Kiran, R. & Sharma, A. Evaluating the effectiveness of training of managerial and non-managerial bank employees using Kirkpatrick’s model for evaluation of training. Humanit Soc Sci Commun 11 , 508 (2024). https://doi.org/10.1057/s41599-024-02973-y

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Impact of India’s Demonetization Episode on its Equity Markets

Goutam sutar.

1 Thiagarajar School of Management (TSM) Madurai, Madurai, Tamil Nadu 625005 India

Krantiraditya Dhalmahapatra

2 Indian Institute of Management Shillong, Shillong, Meghalaya 793018 India

Sayan Chakraborty

3 ICFAI Business School Hyderabad, ICFAI Foundation for Higher Education, Hyderabad, 501203 India

Demonetization is an act of divesting a currency unit from its legal tender. In a developing country, the act of demonetization will have a direct influence on various sectors. An event study is an empirical analysis to investigate the effect of such unforeseen events. In this study, we investigate the impact of demonetization on the Indian stock market. For the analysis, daily data from the NIFTY 50 Index during demonetization have been analyzed within the observation of different event windows. These event windows are framed as 0–7 days, 0–14 days, and 0–30 days to understand the impact of demonetization during the analysis period 8th November 2016–21st December 2016. The study concludes that the impact of events on the Indian stock market lasted for a short time-period and the market recovered within 1 month. More precisely, in the case of demonetization, though the stock market initially viewed the event as disruptive, Cumulative Abnormal Returns bounce back to indicate that the negative financial impact was not as severe as the industry perceived.

Introduction

Demonetization is the process of taking away a currency unit from its legal tender status. It happens as national currency changes: the current form or money forms are taken out of circulation and withdrawn, often to be replaced by new notes or coins. It will create confusion or a severe economic slowdown if it goes wrong. Demonetization has been seen as a mechanism for stabilizing the currency and fighting inflation, promoting trade and consumer access, and moving informal economic activity towards greater openness and away from black and grey markets. One of the earliest known instances of demonetization was in the United States of America where the implementation of the Coinage Act of 1873 mandated the replacement of silver by adopting gold standards that led to an economic slowdown for 5 years (Reti, 1998 ). Further, in 1969, the United States of America went for demonetizing all bills above $100 which was the stepping stone for the development of the American banking system (Friedman, 1992 ). Reduction of market liquidity and tax evasion was the prime concern of Ghana in 1982. The government then took initiative by demonetizing its 50-cedi currency note which resulted in the public’s shift of interest towards physical assets and foreign currency (Kapasi, 2019 ). In 1984, the Nigerian government under the leadership of Muhammadu Buhari proposed the change of existing currency notes with new colored currency notes. This decision failed to achieve its aim of resolving the inflated and debt-ridden economy (Miyan, 2017 ). In 1987, Myanmar announced the demonetization of nearly 80% of the money in circulation to limit the black economy which led to the government crackdown the following year (Vashishat & Tyagi, 2017 ). In order to counter the parallel economy, the Soviet Union under the leadership of Mikhail Gorbachev removed 100 and 50-ruble currency notes from the market in 1991 which subsequently affected the economy of Soviet republics such as Kazakhstan and Ukraine (Dharanipriya & Karthikeyan, 2020 ). Small countries like Zaire also considered removing the obsolete currency from circulation in 1993. It caused several economic disruptions (Muthulakshmi & Kalaimani, 2017 ). In 1996, the Australian government replaced its paper-based currency with new long-lasting currency notes made up of polymer. This step successfully helped in mitigating the black money and increasing the security features (Singh, 2017 ). In 2002, the European Union demonetized its existing currencies in all 12 nations and introduced the new unified currency ‘Euro’. It turned out to be a successful implementation as the public was well aware of the unprecedented event beforehand (Kaur, 2017 ). In Zimbabwe in 2015, the government in order to stabilize its hyper-inflated economy replaced the Zimbabwe dollar with the American dollar and was unsuccessful as it led to the receding of accumulated savings of wealth holders (Warburton, 2017 ).

Several studies have been conducted in the past, examining the impact of a disruptive event on market response and shareholder wealth through a numerous methodologies and the event study is one of them. The event study was invented by Ball and Brown and can capture the impact of any event on the volatility and direction of the price fluctuations (Kim et al., 2020 ). An event study is commonly used in several fields such as finance, economics, marketing, and supply chain management. It is usually carried out by collecting the financial market data and analyzing it to find out the impact of any event. It is known that the security prices immediately reflect the impact of the event, as per its significance. Hence, by observing the security prices, the economic impact of an event could be measured (Kowalewski & Śpiewanowski, 2020 ).

In the Indian context, the demonetization episodes are not new. The first demonetization recorded was in 1946 during the British colonial government and the second was in 1978 in independent India to remove the higher order currency denominations to eliminate the spread of black money but the impacts of such currency bans were less significant (Fouillet et al., 2021 ). This was due to the reason that people during the demonetization were rarely using very high denominations of currency or a very small proportion of the money supply got affected. On 8th November 2016, the Indian government brought demonetization into effect unprecedentedly where ₹500 and ₹1000 banknotes were scraped off from the market (Fouillet et al., 2021 ). Further, new ₹500 and ₹2000 banknotes were issued in exchange for the old ones. Prime Minister Narendra Modi justified the move as a bold step of the government to uproot the financial scams in the form of black money and corruption (Sam et al., 2021 ) in the country by eliminating the circulation of illegal and counterfeit currency and tax evasion (Dharmapala & Khanna, 2019 ; Zhu et al., 2018 ). The impact of this demonetization attracted a mixed reaction in the literature. On the negative side, this unannounced demonetization resulted in cash shortages in subsequent weeks triggering a short-term structural break in the nation (Singh & Ghosh, 2021 ). The authors highlighted that the pre-demonetization period witnessed an increased economic activity with increased banking transaction which was halted temporarily post-demonetization period.

The impact of demonetization completely relies on the factors of demand and supply. Demonetization affects cash-dependent industries and consumers the most, rather than the cash-independent ones especially the heterogeneous consumers by creating a bigger liquidity shock to India’s macroeconomically stable output and welfare (Bajaj & Damodaran, 2022 ). Similarly, India’s large informal sectors that comprise of 80% of the labour force were affected adversely by the demonetization due to a shortage of cash (Karmakar & Narayanan, 2020 ). However, the findings by Fouillet et al ( 2021 ) is quite interesting, where the authors claim that the demonetization was executed mostly to promote the digital economy in the country rather than contending the financial corruption. It has also been stated by the researchers that the impact of demonetization is temporary; it can be reinstated upon the arrival of normal conditions (Zhu et al., 2018 ). On the positive side, researchers argued that demonetization promoted financial inclusion (Singh & Ghosh, 2021 ) through a cashless economy such as digital transactions, electronics payment, point of sales (POS), etc. (Fouillet et al., 2021 ; Sam et al., 2021 ; Singh & Ghosh, 2021 ; Zhu et al., 2018 ).

In this study, the impact of demonetization on the Indian stock market is set as the research objective. We have extended as well as differentiate the works of Dharmapala and Khanna ( 2019 ) by considering NIFTY 50 indices from National Stock Exchange (NSE) as our sample. Risk-Adjusted Return Model, Market Adjusted Return Model, and Mean Adjusted Return Model are employed as solution approaches. It is believed that the stock market is a good example of how shareholder expectations over this disruptive event remain grounded across various industries. The ideal indicator for analyzing this impact would be the financial performance of companies in any industry as it would be possible to interpret changes in sales and profits regarding how sectors were affected (Masood & Sergi, 2008 ). The current study helps to understand the impact of the announcement of demonetization on the stock market as a whole and the impact of demonetization on different sectors of the stocks that constitute the NIFTY index. For this study, the Nifty 50 index has been taken as a sample and used for event study methodology due to its large representation of the Indian economy. As per the report on the methodology of NIFTY 50 indices (NSE Indices Limited, 2019 , 2022 ), there are more than 1600 companies trading routinely on the National Stock Exchange of India (NSE). Out of them, NIFTY 50 constituted those 50 blue-chip companies that approximately hold 66% (NSE Indices Limited, 2022 ) of float-adjusted market capitalization. Also, the NIFTY 50 indices cover major Indian economic sectors and offer helpful insights to investment bankers for benchmarking purposes through a single efficient portfolio. In addition, the speed of information adjustment (Prasanna & Menon, 2013 ) is well maintained in NIFTY 50 indices in the form of addition and deletion of companies, share changes, stock splits, etc. For these reasons NIFTY 50 indices are considered a strong representative of the Indian economy and hence we have used the data from NIFTY 50 for our study proposed in this paper. The date of the announcement, 8th November 2016 is considered as the event date (0) and the impact of the demonetization is analysed over the three-time period (event study windows) being 0–7, 0–14, and 0–30 days. The results of the above-mentioned models were observed at the end of the concerned event windows.

The structure of the article is as follows; Sect.  2 describes previous work in this domain, Sect.  3 highlights the methodology adopted in the study, Sect.  4 describes the results, and finally, Sect.  5 concludes the study along with the discussion on future directions.

Literature Review

In the literature, several studies assessed the impact of an event on market response and shareholder wealth. As the primary aim of our present study orients the impact of the demonetization on the Indian stock market, we present a brief review of research work conducted on stock markets that cause noteworthy reactions. Therefore, we have categorised the literature we have considered into three subsections namely (1) Methods to study stock markets, (2) Event Study Approach (3) Event study on Indian demonetization episode in 2016.

Methods to Study Stock Markets

From the pool of literature, it is identified that the volatility of the Indian stock market exhibits characteristics similar to those found in most of the major developed and emerging stock markets (Kaur, 2004 ) and the exchange rate in emerging markets fluctuates due to the local factors and co-movements from developed nations such as US and China (Liu et al., 2019 ). Also, several factors like economic policy uncertainty, geopolitical risk, and financial stress are heterogeneous in nature across different emerging markets and not the same across emerging markets (Das et al., 2019 ). For countries like India, market volatility is also very significant and is proportional to share price (Ma et al., 2018 ). As the volatility of the bull market is comparatively lower than the bear market negative news causes higher volatility than positive news (Sakthivel et al., 2014 ). The predictability of stock return volatility is also significantly affected by the stock market implied volatility (Dai et al., 2020 ). In the literature, it has been observed that several methods have been adopted by researchers to analyze the impact of an event on the stock market. Some of the popular methods are Artificial Neural Network (Vijh et al., 2020 ) , Sentiment analysis (Bhardwaj et al., 2015 ) , GARCH model (Fang et al., 2018 ) , Hierarchical Structure (Zhang et al., 2020 ) Logistic Regression (Nayak et al., 2016 ) and Clustering Technique (Nanda et al., 2010 ). Besides the aforesaid literature, event-study analysis is also found to be an efficient methodology for the aforesaid purpose due to its simplicity and ability to reveal greater market trends or patterns.

Event Study Approach

In the literature of finance and economics, the event study is a widely used statistical method that studies the impact of a noticeable event on a firm’s value (Brown & Warner, 1980 ; Mackinlay, 1997 ). Among the plethora of literature on event study analysis, several studies are central to our study. To discuss a few, Chen et al. ( 2007 ) used an event study to highlight the adverse effect of the SARS outbreak on the hospitality industries in Taiwan. Their study inferred that this outbreak has a significant impact on the hospitality industry because of a significant negative Cumulative Mean Abnormal Return (CMAR) even after 10 days of the outbreak. This outbreak showed the fragility of the hotel business toward an epidemic and a new epidemic could likely depress stock markets in Taiwan and South-East Asia. Duso et al. ( 2010 ) considered the ability of event study analysis to capture mergers’ ex-post profitability in Austria. They showed that abnormal returns and the ex-post profitability of mergers are positively and significantly correlated for merging firms. Seo et al. ( 2013 ) adopted an event study to examine the impact of food safety events on the value of food-related firms in the United States of America. They concluded that it took nearly a year for the firms to recover from the impact of events. Also, firm-specific and situational factors were the most significant determinants of the impact of the event. To investigate the accident severity, Makino, ( 2016 ) used an event-study mechanism to examine whether severity is getting affected by variation in the Japanese chemical industry. The findings show that the accident risk has a significant negative return after severe accidents had occurred and it has the potential to motivate the TMT of the firms to reduce their firms’ accident risk because of monitoring by the investors in stock markets. Dutta et al., ( 2018 ) mentioned a standardized novel mechanism that shows abnormal returns in long-horizon event studies in Finland. Further, they also identified that Initial Public Offering (IPO), as well as Seasoned Equity Offering (SEO), returns successfully outperforms benchmarking organization. Tao et al. ( 2019 ) measured the short-term impact of the 2011 Tohoku earthquake on the whole market and individual stock for China through an event study. They concluded that the market turned positive in a relatively short span, despite the initial negative reaction. Lalwani et al. ( 2019 ) investigated the presence of post-event, over-or under-reaction in the stock markets for the top 10 countries by market capitalization. They indicated that the study shows that the UK and Japanese markets efficiently react toward all kinds of price information. Australia, Hong Kong, and USA markets have shown significant overreaction bias in negative price shocks while there is no such bias in positive price events. The Swiss and Indian markets have underreaction toward positive information and overreaction towards negative reactions. Canadian market under-reacts to negative information and the German market under-reacts toward positive information. An interesting study was conducted by Buigut and Kapar ( 2020 ), where a comparative study is carried out to analyze the impact of the Qatar blockade on seven stock markets in GCC countries through event study mechanisms. Their study helped in obtaining critical findings such as the Qatar market exhibits negative effects over shorter event windows, but it gradually gets eliminated as Qatar introduced new supply routes. Oman index shows a negative impact over longer event windows. This concludes that Qatar should continue its economic diversification so as not to be dependent on a single source. A similar interesting study was conducted by Bash and Alsaifi ( 2019 ) on the impact of Jamal Khashoggi’s disappearance on the stock market. This uncertain event had a strong negative impact on the stock market returns. Through further study, later it was found that the negative impact was mainly due to the local investors. A similar significant negative impact was reported by Law et al. ( 2020 ) when manufacturers were to be taxed according to the volume of products with added sugar they produced or imported by the UK Soft Drinks Industry. Kowalewski and Śpiewanowski ( 2020 ) examined the reaction of the stock market towards the disasters in potash mines. They observed a significant reaction only to accidents that may result in considerable economic losses, whereas we found that other factors do not influence the event effect. Kim et al. ( 2020 ) examined the influence of macroscopic and infectious epidemic disease outbreaks on the financial performance of the restaurant industry during 2004–2016. They show that the epidemic disease outbreaks had a negative impact on the firm's value.

Event Study on Indian Demonetization Episode in 2016

To our knowledge, the most comprehensive study on the impact of unprecedented and disruptive economic policy like demonetization on a country’s financial market has been conducted by Dharmapala and Khanna ( 2019 ) where the authors considered India’s demonetization episode of 2016 and evaluated the reactions from the Indian stock market through event study methodology. Interestingly the authors found no significant impact of demonetization to curb corruption and tax evasion as shown as a rationale by the Indian government behind such move. Instead, they argued that the demonetization positively affects the state-owned enterprises and largely the banks. However, they cautioned against any conclusions on the success and failure of the demonetization move as the stock market gives mixed reaction that are influenced by the other micro and macro-economic factors. Similarly, Jawed et al. ( 2019 ) also used the event study method by taking daily adjusted stock returns as their sample and found varying sectoral effects of demonetization on the Indian economy. Their findings show that the banking and other financial sectors were affected for a short period while sectors like IT, pharma, and consumer durables witnessed significant gain.

Based on the literature, we develop the relationship among the parameters such as the source of an event, the sector of study, and the impact of the event and risk factors. The relationship is shown in Table ​ Table1 1 .

Relationship mapping among source of an event, the sector of study, and its impact on stock market

ARDL autoregressive distributed lag, ARMA auto regressive moving average, MM market model, RA regression analysis

Methodology

The event study methodology is selected to analyze the data. To find out the impact of demonetization on the stock market the daily data of 50 companies on the NIFTY 50 index is collected. This index is selected as it represents the 50 largest Indian companies that are listed on the National Stock Exchange. The daily data is adopted instead of monthly data as the latter comprises of a lot of changes (updates) that may occur during the month. Therefore, measuring the impact of the announcement using the smallest feasible period is important for measuring market effectiveness as the market reactions and sentiments are very sensitive. For our study, the daily data is collected over the period of 10th May 2016–21st December 2016. The data is collected from the website of NSE India ( https://www.nseindia.com/market-data/live-market-indices ). The estimation period for the event is taken from 10th May 2016 to 27th October 2016 (excluding market holidays) (− 121 to − 8) to specify the normal return of the security. The estimation window is the period over which we calculate the normal returns of security, which start from day − 121 and end on day − 8. The estimation window is mutually exclusive with the event window to avoid possible influence that occurs due to the estimation of the normal return of the event of interest. The event window is the period over which we study the market response to the event and starts on 8 November 2016. Apart from the date of the event, the event window includes days after the event to avoid the potential bias due to delays in the market reaction to the new information. It is also a common practice to include days before the event to account for the possibility that the market may anticipate the event. However, the chances of biases due to any confounding events cannot be ruled out and that may increase with the length of the event window. So, it is crucial that the event window should be considered for a shorter duration to minimize the effect of possible confounding events. In addition, there are many stocks available that are very thinly traded, and their impacts will have less significant if a long event window is considered. Therefore, the event window for our study is restricted to up to 30 days from the day of the announcement of the demonetization. Three event windows such as 0–7 days, 0–14 days, and 0–30 days are considered to understand the impact of the event over different time periods, where “0” represents the event date. Figure  1 indicates the estimation window, event window with dates and the closing indices of NIFTY 50.

Pictorial representation of event study timeline

Event Study

In the proposed study we have followed a similar event study methodology as proposed by Brown and Warner ( 1980 , 1985 ), Mackinlay ( 1997 ). Event studies have numerous applications that can uncover significant insights about how security is probably going to respond to a given disruptive occasion. One of the major tools to carry out event study method is through computation of the abnormal returns that can estimate the impact of an event. The abnormal return is calculated by finding the difference between the actual return and the expected return of a security. It is the return generated by security or a portfolio that doesn’t match its benchmark or the return predicted by an equilibrium model. This deviation of the return of the security from its expected benchmark makes it to be called abnormal returns. An abnormal return can be either positive or negative. Abnormal return helps us in understanding the impact of an event (Lalwani et al., 2019 ; Mackinlay, 1997 ). While the actual returns can be empirically observed, the normal returns need to be estimated (Buigut & Kapar, 2020 ). For this, the event study methodology makes use of three expected return models, which are also common to other areas of Finance research. The notations of the models are given in Table ​ Table2 2 .

Notations for the models

Model Description

In this study, three general models namely (1) Risk-Adjusted Market Model, (2) Market Adjusted Return Model, and (3) Mean Adjusted Return Model are considered to analyze cumulative abnormal returns caused by demonetization in the Indian equity Market. These models are very frequently used in the event study literature and the main rationale behind adopting these models lies in the assumptions and explanations given in Brown and Warner ( 1980 ) and Dyckman et al. ( 1984 ). The Mean Adjusted Return Model assumes that the predicted expected return for security is equal to a constant and varies across the security while Market Adjusted Return Model is equal across the security and need not be constant always. Under the assumption of constant systematic risk both the Market Adjusted Return Model and Mean Adjusted Return Model agree with the Asset Pricing Model. Similarly, the Risk-Adjusted Market Model establishes a crucial relationship between realised returns of security as well as the market. Also, it can be anticipated that the abnormal performance of given security can be conditional to an event like demonetization and therefore, the Risk-Adjusted Market model can bring more insights into the event study. Therefore, incorporating all three models in our study will explore more information on the impact of the demonetization event on the Indian equity market. The models are described as follows.

Risk-Adjusted Market Model

This model works on the principle of variance reduction during the abnormal security return excluding the specific part of the return caused by variation in market return (Kowalewski and Śpiewanowski ( 2020 )

R mt  = market ( m ) return calculated from Nifty 50 index between day t and day t - 1 .

Following Brown and Warner ( 1980 ) and Mackinlay ( 1997 ), the terms a and b are parameters for stock ( i ) are estimated using ordinary least squares (OLS) in the event window from the following equation.

where ε it is the disturbance term with zero mean and constant variance i.e., E ε it = 0 and v a r ε it = σ ε t 2 with σ ε t 2 as parameters of the market model. The term ε it is also known as the abnormal return.

Market-Adjusted Return Model

Market-Adjusted Return Model excludes the impact caused by the variance in market return. The model is explained as follows.

Mean-Adjusted Return Model

This model excludes both the market influence and variance in market return while computing the abnormal return of the stock. It takes the mean of stock (i) as the expected return.

The CAR is given as the sum of abnormal returns for each day. This event window considers all the stocks and is calculated for all three models. This has been purposefully done so that the influence of any abnormal return on any specific stock can be rejected.

Therefore, C A R i t 1 , t 2 can be defined as the cumulative abnormal return of the stock from a particular day (time period) t 1 to the day t 2 , where t 1 ≤ t ≤ t 2 .

(CAAR) which represents the average of the CAR of various stocks within a Further, in the current study, we expanded it by calculating the Cumulative Average Annual Return given period of time.

Hypothesis Development

The following null hypotheses are formulated to investigate the aim of the study.

The means of the CAR before and after the implementation of demonetization of all INR 500 and INR 1000 notes are equal.

The variances of the CAR before and after demonetization of all INR 500 and INR 1000 notes are equal.

There is no significant CAAR during the event window caused by demonetization.

Acceptance of the null hypothesis shows that the impact of the announcement of demonetization is seized within the prices of the security immediately and accurately so that the investor doesn’t get any chance of receiving any abnormal returns from the security. This indicates a semi-strong level presence of efficient market pricing in the stock market.

Test Statistics

In this study, we use t-test statistics to know the significance level of CAAR. The results are calculated during the event window caused by the announcement of demonetization. The t-statistics of CAAR t are computed as:

where S CAAR is the standard deviation of the cumulative abnormal returns

Results and Discussions

In this section, we have stated the results in two phases. First, the statistical inferences have been established through testing three research hypotheses given in Sect.  3.3 by analyzing overall NIFTY 50 indices. Second, we have attempted to explain the possible differential effects of demonetization while grouping NIFTY 50 indices into different sectors and then computing the sector-wise CAR.

Statistical Inference from Research Hypotheses

In the present study, the impact of demonetization on CAR has been examined in three event window horizons namely (0–7) days; (0–14) days, and (0–30) days for those 50 companies present in the NIFTY 50. The test statistics used for research hypotheses H 1 , H 2 , and H 3 are t -test for CAR, F -test for CAR, and t -test with CAAR, and the result summaries are shown in Tables ​ Tables3, 3 , ​ ,4 4 and ​ and5 5 respectively. The test statistic t-test is considered for hypotheses H 1 and H 3 to verify the presence of abnormal return and F -test is conducted for the hypothesis H 2 to investigate possible variability in pre- and post-demonetization periods.

t-test values of CAR

F-test values of CAR

CAAR of event window and computed t-statistics

All CAAR values are observed at the end of the event windows i.e., on 7th, 14th, and 30th days from the event date

Table ​ Table3 3 reports the results of the t-test conducted to inspect the similarities of the means of the CAR of the demonetization announcement dating November 8, 2016. We could see a significant value in all event windows of 0–7-days of CAR (in all models) with results in rejecting the null hypothesis of the same mean before and after the demonetization. This explains that demonetization had an immediate impact on all NIFTY 50 indices during the first 7 days of implementation of the same. But 0–30 days event window CAR doesn’t continue the same. The t-test value of 30-day CAR in Risk-Adjusted and Market Adjusted return models lie below the critical value, thus resulting in the non-rejection of the null hypothesis ( H 1 ). This leads to the conclusion that a significant impact of the announcement of demonetization in a short period (during the first 7 days) is observed but the impact doesn’t last till 30 days from the event. But interestingly we can see a mixed signal from all the three models even when the windows are chosen for 0–14 days. The Risk-Adjusted Return model shows sufficient evidence for not rejecting the null hypothesis while the Mean Adjusted Return model shows strong support to reject the same hypothesis. However, the Market Adjusted Return model weakly rejects the null hypothesis. We can perceive that the results from the Risk-Adjusted, and Market Adjusted return model show that the market is slowly recovering from the impact of the event during the cited event window. In this scenario, the Risk-Adjusted return model can be more relatable given the fact that the model measures the relationship between realized security returns and realized market returns as predicted by the ex-ante model. The Mean Adjusted Return Model gives a consistent result for all event windows. The reason may be because the model averages the difference between observed return and predicted return.

Again, the simultaneous and overall reaction of the market due to economic disruptive events like demonetization is hard to evaluate as there are other possible factors to influence the market (Dharmapala & Khanna, 2019 ). Therefore, we have segregated indices of the firms listed in NIFTY 50 into various sectors and made an effort to describe the possible differential factors through Table ​ Table6 6 and Fig.  3 .

Computed average CAR of all sectors

All average CAR values are observed at the end of the event windows i.e., on the 7th, 14th, and 30th days from the event date

Graphical representation of average CAR obtained from three models of all sectors

Table ​ Table4 4 reports the F-test conducted to inspect the similarities between the variance of the CAR. All the values of the F-test results are closely near zero and well under the critical value. The null hypothesis is rejected ( H 2 ). Hence, it concludes that the variance of the CARs before and after the announcement of demonetization is not equal. This indicates that there is sufficient evidence available to consider that the equity market behavior during the estimation window (pre demonetization) and the event window (post demonetization) periods are not the same and significant changes are observed.

Table ​ Table5 5 and Fig.  2 represent the results of the event study to inspect the impact of demonetization on the value of securities. The CAAR values were observed at the end of the event windows from the event declaration day. For example, during the 0–7 days event window, the CAAR value reported in Table ​ Table5 5 as well as in the Fig.  2 is for the 7th day from the event declaration date. Similarly, for the event windows 0–14 days and 0–30 days, the CAAR values were observed on the 14th and 30th day respectively from the event declaration date. Thus, the horizontal axis in Fig.  2 represents the postevent observation timeline starting from the event date i.e., 08 November 2016 as the origin. The respective t-statistics conducted for H 3 of all the three models used for three different event windows are also represented. The t-statistics value is found to be significant for 0–7 days window in all three models, for the 0–14 days window in two models (Risk Adjusted and Market Adjusted Return Model), and for 0–30 days window in one model (Mean Adjusted Return Model). This indicates a significant negative abnormal return for most of the models during the initial event window (0–7 days) reflecting a strong negative impact due to the demonetization on the value of the securities. The CAAR provides us an overall view of the impact of the announcement and the observation period of its presence in the market (shown in Fig.  2 ) over three event windows. The 0–7 days event window had a significant negative value and it turned positive in the next window. This shows the fast recovery of the market from the announcement of demonetization. From this, we could interpret that the impacts due to the announcement of demonetization have a significant negative impact on the short period. But the impact didn’t last for a long period of time and no significant impact could be detected over a period of 1 month.

Graphical representation of all three model values

Inference from Sector-Wise Average CAR

It is a fact that the differential impact of demonetization on NIFTY 50 indices may not be possible or not sufficient to explain through the tools such as Mean Adjusted, Risk Adjusted and Market Adjusted return models as the NIFTY 50 indices can be influenced by many other socio-economic factors. Therefore, we have computed the average CAAR with all three return models discussed in this study by classifying 18 different sectors that consist of indices of all 50 firms listed in NIFTY 50. Table ​ Table6 6 reflects the results computed through the average CAAR during the event windows. It is done by segregating the CARs of individual securities to analyze their consolidated reactions. For example, the automobile sector consists of firms namely Eicher, Maruti, Tata Motors, Mahindra and Mahindra, Hero Moto Cop, and Bajaj Auto. Similarly, banks namely Bank of Baroda, Kotak Bank, State Bank of India, Yes Bank, ICICI Bank, IndusInd Bank, HDFC Bank, Axis Bank, and HDFC are grouped in the financial service sector and so on. The detailed grouping is provided as an appendix. We found that the overall reaction of the market to demonetization reflects similar trends in various sectors. Then for a better understanding of the impact of the event, the values of all the three models are calculated and plotted in a graph for all sectors individually over three different time periods (Fig.  3 ). Also, a simultaneous pictorial representation of all sectors produced in Fig.  4 by averaging the return from all three models is presented.

Graphical representation of average CAR of all sectors by consolidating the three models

Automobile and Auto Ancillaries Sector

It can be observed that the auto ancillaries’ and automobile sectors are strongly related and the Fig.  3 a, b show that both the sectors follow similar patterns in the Risk-Adjusted and the Market Adjusted return models with significant negative impact due to the event during the first event window. It is understood that the money transaction slowed down during the first event period. But in the second event period, the market shows some improvement in CAAR. As it is very difficult at this point to analyze the differential impact of these improvements, there might be many possibilities. As per the reports of HDFC Bank Investment advisory group in December 2016, it can be assumed that the above positive spike in CAAR value could be due to positive information such as growth opportunity, FDI initiatives from the Government, and implementation of scrappage policy, etc. In addition to this, firm-wise positive performance in the auto sector should not be overlooked. Factors such as Tata Motor’s strong stock valuation and international presence, Bajaj Auto’s new lunches based on premium, price, and super sports segments, and the company’s increased stake in KTM Austria, surged demand in tractor segments on the back of labor shortage and normal monsoon could have possible positive impacts on the stock market in second event windows i.e., 0–14 days. However, there is a negative impact again observed during third event windows as CAAR values declined. We assume that it is due to the restricted money supply, the two-wheeler demands that mostly rural based decreased during the whole event duration as transactions were mainly done in physical currencies. Also, the huge inventory of BS-III vehicles by many companies has a negative contribution to CAAR value in the third event window. We observe that the negative shocks in all the above cases are short-term.

Financial Services Sector

It is interesting to observe that no significant negative impact of demonetization on the financial services sector in Fig.  3 f. The financial sector in NIFTY 50 indices mostly consists of leading banks of India. During the first two event windows (0–7 and 0–14 days window) both Risk-Adjusted and Market Adjusted models report a positive average CAR that reflects the positive impact of demonetization on the financial services sector. The influencing factors that might be instrumental in such a reaction can be many. Some of them are a surge in bank deposits and equity, increased digital retail and banking transactions, positive public anticipation on curbing black money and tax invasion, vigilant on fake currency circulation, greater financial inclusions such as Pradhan Mantri Jandhan Yojna (PMJY), and increased mutual fund investments (Dharmapala & Khanna, 2019 ; Singh, 2017 ), etc. However, all three models outlined a slightly negative impact during the third event window. We assume that a stiff decline in currency circulation and withdrawal restriction were possible reasons for this decreased CAR during the third event window. In addition, some other possible reasons may include factors like excess liquidity in banks, weak credit demand, and low-cost Current Account and Saving Account (CASA) that forced banks to a large cut in their marginal cost of funds-based lending rates (MCLR). In all the models, the Mean Adjusted Return Model exhibited a negative impact on the event for all three windows though there was an improvement in the second event window due its naiveness towards accounting for various market and risk factors.

IT and Software Sector

It is noteworthy to state that the IT and Software sectors witnessed a significantly positive impact from demonetization during the event period. Figure  3 j shows a steadily increasing average CAR value over the event windows in all three models. Although, Indian IT and Software sectors are mostly influenced by confounding factors like U.S geopolitical environments (Dharmapala & Khanna, 2019 ), economical disruptions like demonetization created a huge opportunity for IT sectors to boost the digital economy (Business Standard Report, 2016 ) . Therefore, a strong positive impact of the demonetization as reported in this study is not uncommon under the strong establishment of the Indian technology sector.

Construction Sector

The construction sector listed only one firm from NIFTY 50 which is Larson and Turbo Ltd (L & T) which reflects a different impact pattern (Fig.  3 d) than the previous sectors. The first event period has a positive impact and from there the CAAR tends to be negative. As the company construction business is largely migrant labor intensive and were mostly paid by cash, the CAAR might have been negatively affected due to a decline in high-value currency circulation. Apart from that low investment momentum, order backlogs, and slower outflow of funding from the banks might be other reasons for these negative CAAR values during the first to second event windows. The marginal improvements in the third event window are also observed which may be due to the company’s proactive approach towards reduction in working capital, cost optimization through supply chain effectiveness, and other initiatives as reported by Hindustan Times (Press Trust of India, 2017 ). The Market Adjusted and Risk Adjusted return models show similar patterns while the Mean Adjusted Return model is mostly in the negative CAAR range.

Pharmaceuticals-Drugs and Power Sectors

The pharmaceutical and power sectors exhibit similar trends in their CAAR values as shown in Fig.  3 n, o. The pharmaceutical and drugs sector had no impact in the first event period. In the second event period, the CAAR turned positive which indicates a positive impact on the sector followed by a no significant impact in the third event period. Along the lines of arguments presented by Dharmapala and Khanna ( 2019 ), we also assume that this positive impact was owing to the high dependency on external finances which were sufficiently available in banks during the demonetization.

Similarly, the power sector (Fig.  3 o) had no significant impact in the first event period. The CAAR turned positive in the second event period and slightly declined in value in the following event period. This shows the insignificant impact of demonetization on the power sector. As per the report published in The Economics Times (PTI, 2016 ), it is interesting to note that the power sector got benefitted in terms of collecting huge pending bills as the Government of India authorized paying the pending utility bills in demonetized currencies. Also, a high inflow in funds to banks during the event windows resulted in lending the fund to the power sector for better infrastructure could be another reason for this positive reaction.

Industry Gas and Fuel Sector

The impact on the industry gas and fuel sector could not be specified as the three models provide values different from each other (Fig.  3 i). There is no similarity among the models. Also, financial news sources highlighted many differential factors such as declined two-wheeler sales, the overall decrease in oil demand, a rise in demands for domestic liquified petroleum gas (LPG) and increasing aviation demand in this sector. Hence, the impact of demonetization could not be analyzed for this sector properly.

Telecom and Metal sectors

Figure  3 p shows that the telecom sector had no significant impact in the first event window though it started with a low CAAR value followed by a positive impact in the next window. During the third event window, there is a slight decline in CAAR, but no negative impact was observed for all three models. Relatively low CAAR is expected due to the withdrawal of denominations of Indian Rupees 500 and 1000 currency. It may be because of the increased online transactions, different and flexible packages, and wide adoption of online transactions that have been adopted by telecom industries before the demonetization episode. Similarly, the metal sector recorded no significant impact on their business during the three event windows in Risk-Adjusted and Market Adjusted Models. The metal sector is not directly related to household spending patterns which may be an explanation behind this non-significant reaction due to demonetization.

Other Sectors with Similar CAAR Trends

The sectors like cement, entertainment, consumer product, industrial equipment, mining & mineral, and paint have undergone similar impacts from the demonetization as their respective CAAR values have started with a significant negative impact in the first event window followed by marginal improvements in the second event window and after which further significant decrease in CAAR values in all three models is seen. Most of these sectors are hard cash-dependent industries with voluminous retail outlets throughout the country. With restrictions in withdrawal of higher order denominations, these sectors were affected heavily.

The textile and transport sectors have similar CAAR trends (Fig.  3 q, r) as they face negative impact during the first event windows with almost no impact in the second event windows followed by significant negative impact during the third event window. The textile sectors are mostly labor-intensive and due to sudden demonetization, the inevitable situations like workforce layoff, closure or reduction of various operational activities, inability to pay daily wages of labors, and lack of sufficient working capital due to cash crouch (Choudhery et al., 2021 ) could be the main cause behind this negative impact. The transportation sector from the NIFTY 50 list considered for this study is Adani Ports and Special Economic Zone limited. The freight industries coming under this are mostly substantially transacted through hard cash for various activities like payment and expenses of truckers, fuel, local tolls, and taxes. These transactions were badly affected due to cash crouch and ban in high-value currencies and could be a differential factor for negative impact of demonetization transportation sector.

As the individual sectors are tedious to interpret separately, we consolidate the results by calculating their event-window-wise average and arrive at a mean value of all the three models in Fig.  4 . From Fig.  4 , we can see that most sectors have similar reactions due to demonetization. The sectors such as cement, paint, auto ancillaries, entertainment, textile, and transport had a significant negative impact much higher than other sectors due to the announcement in the shorter period. The sectors that had less impact are pharmaceutical and drugs, power, and telecom. The financial sector and the IT & Software sector remain advantageous during the event windows.

Conclusions

The study examines the impact of the announcement of demonetization on the Indian stock market. To evaluate the consequences of such a significant economic disruptive policy, an event study methodology is adopted, and the impact has been observed for a short period of 30 days from the announcement of the event. Using capital market data sampled from NIFTY 50 indices, the adopted methodology analyzes the financial impacts of such unforeseen events on 18 leading industrial sectors. The effectiveness of such a study comes from the idea that the outcome of an event would be reflected instantly in security prices, provided by the sagacity of the marketplace. This makes it possible the use abnormal returns in research on the day of the event.

The announcement of demonetization, which was intended to reduce the black money or counterfeit notes used for illegal and anti-social activities, invited numerous arguments and counterarguments from different sections of society. But this study intuitively deduces that this monetary policy has directly affected the liquidity in the system. It is because, in a country like India, where most of the transactions are hard cash-dependent, the shortage of cash flow within the country adversely affected many sectors. The sectors like manufacturing of goods, agriculture, and several other services were negatively impacted by demonetization, which led to a short-term economic slowdown and a decrease in the GDP growth rate.

From the findings of the study, we observe that the stock market initially viewed the announcement of demonetization to be disruptive to the entire market. However, CARs ‘bounce-back’ evidenced that the negative financial impact may not be as severe as the industries perceived for most of the cases. Therefore, the impact lasted only for a short period for most of the sectors, and the market recovered largely within 1 month. It is also noticed that sectors like financial services, IT, and Software gained from this event while Pharmaceuticals, Power, Telecom, and Metal sectors were least affected. We also found that most cash-dependent sectors like cement, entertainment, industrial equipment, mining & mineral sectors were negatively impacted by the demonetization. Methodologically, both the Risk-Adjusted return Model and Market Adjusted Return Model show similar inferences, while Mean Adjusted Return Model underestimated the positive impacts of the event.

The insight and values from this study could be utilized by managers and policymakers in the future under the circumstances of disruptive monetary policy or similar events that create an economic imbalance. The event study methodology is not limited to any sector or field and can be used in any such event. However, the current study has several limitations such as scenarios like extended event windows, larger NIFTY indices, the impact of possible differential and confounding factors, etc., which can invite the attention of future researchers on this topic. Further, for future studies, different methodologies, such as longitudinal analysis that includes frequent measurements of the same variables over several periods, can be utilized to analyze the financial impact of demonetization and other events that creates perturbative situation in a country’s economy.

Studied organizations along with their sectors.

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Contributor Information

Goutam Sutar, Email: ni.ca.mst@ratusmatuog .

Krantiraditya Dhalmahapatra, Email: [email protected] .

Sayan Chakraborty, Email: gro.aidnisbi@nayas , Email: moc.liamg@85ytrobarkahcnayas .

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