Showing papers by "Cristi Spulbar published in 2022"

An Investigation of the Link between Major Shareholders’ Behavior and Corporate Governance Performance before and after the COVID-19 Pandemic: A Case Study of the Companies Listed on the Iranian Stock Market

Abstract: One of the basic functions of establishing corporate governance (CG) in companies is improving performance and increasing value for shareholders. Expanding the company’s value will ultimately increase the shareholders’ wealth. Therefore, it is natural for shareholders to seek to improve their performance and increase the company’s value. If CG mechanisms cannot perform this function in companies, they do not have the necessary efficiency and effectiveness and, therefore, cannot improve the efficiency of companies. This article investigated the connection between the power of major shareholders and the modality of CG of companies listed on the Iranian capital market before and after the COVID-19 pandemic. The statistical sample of the research included 120 companies listed on the Tehran Stock Exchange for the selected period from 2011 to 2021. The results showed that the concentration of ownership is harmful to adopting corporate governance (GCG) practices. In particular, the high level of voter ownership concentration weakens the corporate governance system (CGS). The results of this study, which was conducted using panel analysis, revealed that the concentration of ownership impairs the quality of CGS, and major shareholders cannot challenge the power of the main shareholder; it alsonegatively affected the quality of business boards, both during and before the COVID-19 pandemic. The competitiveness and voting rights of the major shareholders negatively affected the quality of board composition before and after the COVID-19 pandemic. The concentration of voter ownership also negatively affected the quality of CGS, both during and before COVID-19, and the competitiveness and voting rights of major shareholders before COVID-19. This concentration positively affected the quality of CGS after the COVID-19 pandemic.

Investigating Industry 5.0 and Its Impact on the Banking Industry: Requirements, Approaches and Communications

Abstract: Technology, along with political and economic factors, is one of the main drivers of the future of banking. Banking managers urgently need to know technological trends to make strategic decisions, know the future accurately, and make the most of existing opportunities. Industry 5.0 is the dream of modern banking, based on strategies for successful entry into the field in a completely different way. Using a complex literature survey, 49 indicators were identified to enter Industry 5.0 and were classified into three categories of insignificant indicators, essential indicators, and very necessary indicators. Then, based on the opinions of 10 experts from ten countries with modern banking in the world, the researchers focused on 14 essential indicators. To analyze the drawn space, structural-interpretive modeling and MICMAC analysis were used and the model was classified into nine levels. The results showed that low-level indices are the most influential (TMBE and HEMS) and higher-level indices are the most influenced (PZM and RNC). Finally, researchers analyzed how to use new technologies in the banking industry with the entry of the Industry 5.0 and revealed what the characteristics of the impact of these indicators on entering Industry 5.0 are.

Ranking performance indicators related to banking by using hybrid multicriteria methods in an uncertain environment: a case study for Iran under COVID-19 conditions

Abstract: This research aims to identify performance indicators and use them to prioritize banks in Iran. Today, the banking industry is severely challenged by decreasing revenues, especially during crisis such as the COVID-19 pandemic. Hence, evaluating banks to find their weaknesses is vital and shows how banks with flaws can be benchmarked from best practice banks. For this work, data is collected from Iranian banks and then evaluated based on the Delphi method. Since the importance of the considered factors is quite diverse, they should be ranked. We use Evaluation by an Area-Based Method of Ranking (EAMR) for this research study. As this method requires factor-specific weights, the Stepwise Weight Assessment Ratio Analysis (SWARA) method is used for determining these weights. This paper looks forward to introducing new hybrid MADM methods in an uncertain environment with high reliability in the results. This new model leads to ensure managers that they can make their decisions accurately. The results reveal the performance of Iranian banks and a respective ranking of them including a model for benchmarking. This empirical research study also provides useful guidance to a better understanding of performance measurement in the banking sector in Iran.

Revisiting CAMELS Rating System and the Performance of ASEAN Banks: A Comprehensive MCDM/Z-Numbers Approach

Abstract: This paper provides a new perspective on the performance of ASEAN member countries’ banks as proxied by CAMELS rating system in the light of information reliability. A comprehensive MCDM approach is developed based on alternative methods to handle expert preference uncertainties regarding banking ideal performance levels and relative CAMELS variable efficiency. While expert preferences are collected using structured interviews, the partial bank rankings are defined upon Fuzzy TOPSIS with the primary relative efficiency weights obtained from SWARA. Z-numbers are utilized to address the inherent fuzziness in how banking performance and financial distress are associated with information reliability of positive-ideal banking performance and CAMELS variables efficiency functions generated from expert preferences or perceptions. The empirical findings demonstrated that employing information reliability methodologies applied to a proxy of the CAMELS rating system, the ambiguous influence of ASEAN banking performance on financial hardship can be adequately handled.

A Study on the Acceptance of Mobile-Banking Applications in India—Unified Theory of Acceptance and Sustainable Use of Technology Model (UTAUT)

Abstract: This research makes an attempt to understand various factors that influence the adoption of mobile applications. Within the context of the “Unified theory of acceptance and use of technology” (UTAUT) modified model, considering the upcoming demand and increase in demand for mobile-banking applications, the researcher tried to explore the theoretical concept between random people of various states in India. The primary data was collected by preparing a questionnaire and circulating it using Google Forms. The collected data was further coded into Smart PLS 4 to understand the model and structural equation with reference to mobile-banking technological adoption and factors that had a significant impact. The conclusions derived from the study is that social influence, “effort expectancy”, and “trust” factors had a very strong influence on the “purchase intention”, whereas “effort” and “risk” factors had a negligible impact on purchase intent. It was also found that the UTAUT model is appropriate for evaluating the technological adoption of mobile-banking applications. With the advent of many players in the market and their unique banking management applications on mobile platforms, consumers are moving towards different third-party app than their origin bank in which they hold account. This has forced banking institutions to up the pace in the competition, introducing a lot of new features. It is also important to understand that, as a customer, there are a lot of attributes that he would be looking into for adoption. This paper is an attempt to understand the advancements in various variables that consumers would look at in the area of mobile-banking applications.

Investigating the nexus between artificial intelligence and machine learning technologies in the case of indian services industry

Abstract: The future in the services industry belongs to Artificial Intelligence (AI) driven machines, which is a major source of worry for the job market in India. Over 50% of India’s GDP constitutes services, and it is a major source of employment for the skilled manpower of India. The research measures the impact of AI on service jobs in India based on qualitative parameters such as logical, natural, physical, and compassion; and finds which aspect serves the jobs better between machines and humans. The jobs taken over by AI are primarily at the task level more than the job level and for the basic tasks predominantly. The replacement starts with the basic tasks involved in providing service and then it grows to perform all the tasks involved in services. The research finds out that the logical aspects of the service will slowly reduce in the coming 5–10 years as AI will perform all the logic-related tasks leaving more emotional tasks such as compassion for humans. Finally, even these emotional-related tasks will be taken over by the AI which provides us with a very interesting combination of man and machine in the Indian scenario still threatening human employment.

Crude oil futures to manage the price risk of textile equities: An empirical evidence from India

Abstract: The textile sector in India is the oldest manufacturing sector. As the raw materials for this sector are sourced from the petrochemical industries, the earnings of Indian textile companies are dependent on the crude oil price. The crude price in the international market has become more volatile and hence, the equity price of Indian textile companies has become more volatile. This study aims to develop two price risk management strategies for Indian textile equities. Using the vector autoregressive (VAR) model, a price forecast model, further the possibility of cross hedge for textile equities with the help of crude futures is examined using the Granger causality test and Pearson correlation statistics. The results of the study showed that crude futures price in India is one of the price determinants of textile industry stock prices.

Using Econometric Models to Manage the Price Risk of Cocoa Beans: A Case from India

Abstract: This study aims at developing econometric models to manage the price risk of Dry and Wet Cocoa beans with the help of ARIMA (Autoregressive Integrated Moving Average) and VAR (Vector Auto Regressive). The monthly price of Cocoa beans is collected for the period starting from April 2009 to March 2020 from the office of CAMPCO Limited, Mangalore, and the ICE Cocoa futures price from the website of investing.com. The augmented dickey fuller test is used to test the stationarity of the series. The ACF and PACF correlograms are used to identify the tentative ARIMA model. Akaike information criterion (AIC) and Schwarz criterion (SBIC), Sigma square, and adjusted R2 are used to decide on the optional AR and MA terms for the models. Durbin–Watson statistics and correlograms of the residuals are used to decide on the model’s goodness of fit. Identified optimal models were ARIMA (1, 1, 0) for the Dry Cocoa beans price series and ARIMA (1, 1, 2) for the Wet Cocoa beans price series. The multivariate VAR (1) model found that the US and London Cocoa futures prices traded on the ICE platform will influence the price of Dry Cocoa in India. This study will be helpful to forecast the price of Cocoa beans to manage the price risk, precisely for Cocoa traders, Chocolate manufacturers, Cocoa growers, and the government for planning and decision-making purposes.

An Approach for Supply Chain Management Contract Selection in the Oil and Gas Industry: Combination of Uncertainty and Multi-Criteria Decision-Making Methods

Abstract: The oil and gas industry plays a significant role in the economies of many countries today. Due to various factors, including oil price fluctuations, wars, sanctions, and many other instances, selling and supplying these products at low prices is necessary. As a result, the global economy may suffer as well. Supply chain management is one way to reduce the prices of these products. This study was conducted to identify supply chain management contracts in the oil and gas industry. The paper presents an application of multi-criteria decision-making (MCDM) for coping with uncertainty. We contribute to the literature by proposing a new hybrid MCDM method with gray numbers for ranking supply chain management contracts in the oil and gas industry. The results show that the factors for evaluating supply chain management contracts must be selected, and then according to these factors, the supply chain management contracts must be chosen. As a result, we provide our customers with the best deals and help oil and gas companies minimize their costs.

Digitalization as a Factor in Reducing Poverty and Its Implications in the Context of the COVID-19 Pandemic

Abstract: In the present economic context, one of the most important topics of discussion is that regarding sustainable development. According to the agenda developed by the United Nations, one of the most important objectives for the present decade is represented by the list of the Sustainable Development Goals. The Sustainable Development Goals can be divided into five pillars: people, planet, prosperity, partnership and peace. One of the first stipulated goals of the UN agenda is the eradication of poverty and famine. We consider that a significant influence on the eradication of poverty is represented by the development of technology. In this paper, the authors aim to establish a connection between the rate of technological development and the poverty headcount rate. To measure the digital development of the analyzed countries, we decided to compose an index of digital development by taking into account indicators made available by the International Telecommunication Union and the poverty headcount ratio, as was calculated by the World Bank database. This empirical study is of interest for the implications that it has in shaping governmental policies regarding easing the access to digital technology. The method used to quantify the influence of digital development on poverty was the panel data GMM vector autoregressive model for a dataset composed of 35 countries for the period between 2005 and 2018. The results indicate that an increase in digital development will lead to a reduction in the poverty headcount rate. These results imply that by increasing access to technology, countries could help reduce their level of poverty. In this paper, we will also analyze the way in which adopting digital development leads to better economic performance when faced with the COVID-19 pandemic. The results of the present study are of great interest to the scientific community and the public due to the implications of digital development in the field of economics and the combined effect of this phenomenon and the COVID-19 pandemic. We thus conclude that by encouraging digital development and through adopting new technologies, the government can lead to the eradication of poverty. This seems counterintuitive due to the fact that investment in shelter and primary goods can be seen as one of the primary ways of developing the economy. We conclude that better and more consistent results regarding the reduction of poverty can be obtained by increasing the digital development of a country.

Turning Crisis into a Sustainable Opportunity Regarding Demand for Training and New Skills in Labor Market: An Empirical Analysis of COVID-19 Pandemic and Skills Upgradation

Abstract: TheCOVID-19 pandemic has brought rampant changes in skill needed in the labor market. It has accentuated technological disruption leaving millions in dire need of reskilling and upskilling. In this paper, we empirically analyze the impact of the COVID-19 pandemic related lockdown on the thrust of skills upgradation among people. By analyzing the Google trends data of 13 countries, we test the effect of the lockdown implementations on the urge to upgrade the skills through online searches for skills enhancement. Using difference-in-difference estimation approach, we found a substantial hike in the frequency of search terms related to skills upgradation. Our results suggest that people are utilizing the excess time, made available due to lockdowns, by exploring avenues to enhance their skills to accumulate human capital. The online educational platforms have been proven vital. The findings of this study establish the causal link between use of online education platforms and human capital development.

Investigating the nexus between safety training, safety rules and procedures, safety performance and protection against hazards in Pakistani construction companies considering its impact on textile industry

Abstract: The main aim of this research paper is to examine the linkage between safety training, safety rules and procedures, safety performance and protection against hazards in Pakistani construction companies related to its effects on the textile industry. The primary responsibility of the organization is to provide a safe workplace to the workers where workers do their work safely. The current study examines the relationship between safety training, safety rules & procedures and safety performance. A total of 450 workers from 15 companies participated in the study. A questionnaire survey was used to collect the data. The findings revealed that both safety training and safety rules and procedure were significantly and positively associated with safety compliance. The results propose that construction companies should give proper training to their worker in order to avoid any bad incidents. Similarly, adequate safety rules and procedures are essential for a safer work environment. The textile industry is a very important sector in Pakistan with a significant impact on employment and the labour market.

The impact of OECD's Development Assistance Committee (DAC) Aid Commitments for Education on Human Development in Asian Countries and its implications for textile industry

Abstract: Education and health are considered a cornerstone for obtaining targeted development in any society. Moreover, both sectors promote prosperity greatly. In this changeable epoch, people are thought out as the real wealth of any nation and this wealth with good human capital serves the economy very efficiently and productively. This research study aims to analyse how Development Assistance Committee (DAC) aid commitment for education along with institutional quality is effective for the human development of selected Asian economies. A panel data set over 2011–2018 is used for this analysis in Asian countries. GMM results show a significant and positive relationship between aid commitment for education and the human development of these economies. A more interesting result is that financial development seems to boost up human deployment in the selected Asian economies. The development of the textile industry is significantly influenced by education, especially considering the effects of OECD's Development Assistance Committee (DAC) Aid Commitments for education on human development in Asian countries. There is a dire need to reconsider more allocation of resources and aid to education and health to utilize these inflows at the maximum level for targeted development.

Risk Allocation Optimization between Owner and Contractor in Construction Projects by Using the UTA-STAR Method

Abstract: Project risk is an uncertain situation or event that, if it occurs, may have a negative or positive effect on one or more project objectives, such as scope, schedule, cost, and quality. Major industrial projects are increasingly facing complexity and uncertainty. The scope of this paper is related to petrochemical projects, in which risks directly affect the approved time, cost, and quality of the project. In such projects, there are risks that neither the owner nor the contractor has the main role in the occurrence or prevention of, and it is not easy to determine who is responsible for them. In such projects, there are risks that neither the owner nor the contractor has the main role in the occurrence or prevention of, and for which it is not easy to determine responsibility. Therefore, predicting, identifying, analyzing, and determining of the optimal allocation of risk responsibility between contracting parties is one of the most important steps before the start of the project. Suppose it is not correctly allocated among project stakeholders, then, in that case, risk responsibility imposes costs on the project that must be paid by the owner, contractor, and partnership, causing, in general, many problems for project management. Therefore, this paper presents a model to calculate the optimal ratio of risk allocation between the project parties in the concluding contract stage, using the UTA-STAR technique to obtain the owner and contractor utility function to create as much of a win-win relationship between them as possible.

Exploring the sustainable effect of mediational role of brand commitment and brand trust on brand loyalty: an empirical study

Abstract: Abstract The study focus on the role of self-expressive branding, brand love, brand trust and brand commitment on brand loyalty. It also identifies the strength of mediating effect of variable brand commitment between brand love and brand trust. Also measures the strength of mediating effect of variable brand commitment between brand trust and brand loyalty. The data is gathered by using a structured questionnaire and a sample size of 101 respondents in a cross-sectional study. Statistical analysis has been done through SMART PLS 3.0 software. In the analysis part, PLS algorithms, bootstrapping, blindfolding, Importance performance matrix, FIMIX, Multi-Group analysis have been undertaken. A reflective model has been developed. The path coefficient value and empirical t-values of all direct relationships of variables above 0.2 and 1.96 respectively and substantiate the hypothesis. The results have shown that brand commitment is partially mediates the association between brand love and brand trust and also between brand trust and brand loyalty. The four-segment solution's FIMIX-PLS path coefficient shows that brand love and brand trust are more relevant in segment 3, followed by segment 2, segment 1 and segment 4, respectively. Companies should focus on improving their brand trust displayed by consumers followed by brand commitment which strengthens brand loyalty in the automobile sector. This industry could consider implementing this creating trustworthiness about the brand, by developing strong psychological connectedness between the customer and brand by the retail outlet by offering the best quality product, and by incorporating strategies to reduce cognitive dissonance among the buyers.

Provide a framework for problem statement in the organization's research: an AHP, ISM, and MICMAC approach

Abstract: The aim is to provide a framework for statement organizational research problems. State the problem is the most important reason for the researcher to choose the subject. Although research has been done on the characteristics of expression of research problems, but in organizational research, no framework for expressing the problem has been provided. The method used in this research is grounded theory followed by the ISM-MICMAC approach for modelling. The findings of this study identify the components of problem statement in organizational research and modelling them. Due to the large number of indicators, first nine indicators of higher importance were identified, which had much more weight than other indicators. These indicators were then compared and prioritized again by other experts. Based on the level of importance of these indicators, a model is presented and it is determined what position each of these indicators is in terms of Degree of dependence and Influence rate. The researcher can easily use this information to provide an acceptable problem statement and the editors will have a good assessment tool.

Predicting future community intrusions using a novel type and encryption mechanism architecture for attack node mitigation

Abstract: The recent exponential rise in the number of cyber-attacks has demanded intensive study into community intrusion detection, prediction, and mitigation systems. Even though there are a variety of intrusion detection technologies available, predicting future community intrusions is still a work in progress. Existing approaches rely on statistical and/or superficial device mastery techniques to solve the problem, and as a result, feature selection and engineering are required. The truth is that no single classifier can provide the highest level of accuracy for all five types of training data set. Cyber-attack detection is a technique for detecting cyber-attacks as they emerge on a laptop or network device, intending to compromise the gadget's security. As a result, using a novel type and encryption mechanism, this paper offered a unique architecture for attack node mitigation. The input UNSW-NB15 dataset is first acquired and divided into training and testing statistics. First and foremost, the information is pre-processed and capabilities are retrieved in the training section. The Taxicab Woodpecker Mating Algorithm (TWMA) is then used to select the critical characteristics. The attacked and non-attacked information are then classified using the BRELU-ResNet (Bernoulli's Leaky Rectified Linear Unit - Residual Neural Community) classifier. The encrypted at Ease Hash Probability-Based Elliptic-Curve Cryptography (ESHP-ECC) technique is used to encrypt the ordinary facts, which are subsequently kept in the security log report. Following that, using Euclidean distance, the shortest course distance is estimated. Finally, the records are decrypted using a set of principles known as Decrypted Relaxed Hash Probability-Based Elliptic-Curve Cryptography (DSHP-ECC). If the input appears in the log file during testing, it is regarded as attacked data and is prevented from being transmitted. If it isn't found, the procedure of detecting cyber-attacks continues.

Evaluation of country risks on foreign direct investment in middle east countries using data envelopment analysis (dea): a case study

Abstract: : THIS RESEARCH PAPER AIMS TO PROVIDE AN ASSESSMENT OF COUNTRY RISKS ON FOREIGN DIRECT INVESTMENT IN MIDDLE EAST COUNTRIES USING DATA ENVELOPMENT ANALYSIS (DEA).NOWADAYS, FORIEGN DIRECT INVESTMENT (FDI) HAS STRONG AFFECT TO RISK COUNTRY. MANY COUNTRIES CHECK THE COUNTRY RISK OF THAT TO UNDERSTAND THEY CAN PERFORM INVESTMENTSIN THAT COUNTRY OR NOT. THE AIM OF THIS RESEARCH STUDY IS FINDING ACCORDING TO COUNTRY RISK, HOW MUCH THEY CAN GET FDI AND EVALUATED THEY PERFORMANCE ABOUT IT IN THE MIDDLE EAST COUNTRIES. FIVE COUNTRIES HAVE BEEN SELECTED AND THEN THEY EVALUATED BY DATA ENVELOPMENT ANALYSIS (DEA) FOR FINDING THE BEST PERFORMANCE OF COUNTRIES FROM 2005 TO 2020. THE RESULT INDICATES THAT AMONG THESE COUNTRIES, EXCLUSIVELY TURKEY AND THE UNITED ARAB EMIRATES HAD OBTAINED BEST PERFORMANCE REGARDING ABSORBING FDI ACCORDING TO THEIR COUNTRY RISK.

Investigating the impact of COVID-19 pandemic on volatility patterns and its global implication for textile industry: An empirical case study for Shanghai Stock Exchange of China

Abstract: This research paper aims to examine the impact of the COVID-19 pandemic on volatility patterns and its global implication for the textile industry in China. The COVID-19 pandemic has generated a global health crisis with profound economic, social and financial implications, but also has triggered a ruthless global recession. The global economic recovery as a result of the COVID-19 pandemic can also generate significant investment opportunities for the textile industry in China. In this paper, the application of empirical methods could explain historical prices, the movement dynamics of financial assets, and investigate various important characteristics of asset pricing that explore details of the Chinese stock market. The econometric framework includes the following: symmetric Generalize Autoregressive Conditional Heteroscedastic GARCH (1, 1) model, asymmetric GARCH models such as EGARCH and GJR models. The main aim is to identify the asymmetric volatility effect, and impact of news on the SSE Composite Index and investigate long memory properties in volatility using daily data for the sample period from 19th December 1990 to 31st December 2020. This empirical study contributes to the existing literature on the impact of the COVID-19 pandemic on international stock markets, by investigating symmetric and asymmetric volatility patterns in the case of the Shanghai Stock Exchange from China

Investigating the impact of normal and abnormal loss factors in garment industry: A case study based on a jeans manufacturer in India

Abstract: This study aimed to analyse the normal and abnormal loss of a jeans manufacturing company in India. Personal interview and observation method are used in this study. Abnormal loss in quantity and rupee value is computed for 40 days of production based on the observed data. Mean abnormal losses are computed and one sample t-test is applied to test the hypotheses that the mean abnormal loss is not equal to zero. The study revealed that a normal loss of 3 to 5% is expected in any garment manufacturing company due to loss during the cutting and shrinkage process. The p-values of one sample t-test were less than 0.05 for all the tested hypotheses, hence, all the null hypotheses (H01 to H05 mean abnormal losses equal to zero) were rejected. Further, it was found that fabric is the big contributor in terms of abnormal loss. Hence, proper training for workers and recruiting of trained workers are advised to reduce abnormal losses

The impact of hazards control practices on injuries and accidents in textile industry

Abstract: Workplace injuries and accidents have an adverse effect on the lives of textile workers. The research study investigates the moderating effect of ethical climate on the relationship between safety management practices and safety behaviour. A simple random sampling technique was employed to collect data from 12 textile companies. A total of 384 textile workers participated in this study. Results revealed that management commitment, safety training, workers involvement, safety communication & feedback and safety promotion policies have a significant and positive influence on safety behaviour. While safety rules and procedure are failed to predict safety behaviour. Moreover, ethical climate positively moderate the relationship among management commitment, safety training, workers’ involvement, safety communication & feedback and safety behaviour. Whereas, the ethical climate failed to moderate safety rules and procedure, safety promotion policies and safety behaviour. It is recommended that Textile companies.

Testing volatility spillovers using GARCH models in the Japanese stock market during COVID-19

Abstract: This paper investigates volatility spillovers in the stock market in Japan during the COVID-19 pandemic by using GARCH family models. The empirical analysis is focused on the dynamics of the NIKKEI 225 stock market index during the sample period from July 30, 1998, to January 24, 2022. In other words, the sample period covers both the period of the global financial crisis (GFC) and the COVID-19 pandemic. The econometrics includes GARCH (1,1), GJR (1,1), and EGARCH (1,1) models. By applying GARCH family models, this empirical study also examines the long-term behavior of the Japanese stock market.The Japanese stock market is much more stable and efficient than emerging or frontier markets characterized by higher volatility and lower liquidity. The paper establishes that NIKKEI 225 index dynamics is different in intensity in the case of the two most recent extreme events analyzed, namely the global financial crisis (GFC)of 2007–2008 and the COVID-19 pandemic. The findings confirmed the presence of the leverage effect during the sample period. Moreover, the empirical results identified the presence of high volatility in the sample returns of the selected stock market. Nevertheless, the econometric framework showed that the negative implications of the GFC were much more severe and caused more significant contractions compared to the COVID-19 pandemic for the Japanese stock market. This study contributes to the existing literature by providing additional empirical evidence on the long-term behavior of the stock market in Japan, especially in the context of extreme events.

Do European, Middle-East and Asian Stock Markets Impact on Indian Stock Market? A Case Study Based on NIFTY Stock Index Forecasting

Abstract: This paper estimates NIFTY index from Indian stock market by considering a cluster of MSCI European, Middle East and Asian stock market indices. In the forecasting process, we obtain group of independent variables to test its relative impact over dependent variable (NIFTY) considering a sample size of daily observations from January 2000 to December 2021 abstracted from Bloomberg. We run OLS regression, Quantile estimations with additional parameter of VIF and BKW. We found significant impact association with China (Asian index) and Saudi Arabia (Middle East index) during the forecasting process compared to rest of sample indices that exceed unexpectedly out of VIF limits. Further, we recorded strong association of independent variables despite of statistical significance (<1%) in OLS regression estimation.