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Currency Derivatives are very important financial instruments for speculation, hedging and arbitrage opportunities, and among them cross-country futures are one of the important types with a huge research gap. Studying them becomes very imperative. This paper studies the volatility of INR based cross country futures (USD, JPY and EUR) and performs forecasting using ML Algorithm and utilizes LSTM for prediction. The study proves to be a first of its kind study involving cross-country futures and is a beacon of hope for all future research on similar subjects. The study will also be helpful to investors and foreign exchange managers along with monetary and fiscal policymakers. The study consists of total of 674 data points of near-month expiry futures expiring on 29th October, 2024. The span of data was 1 year for JPY and EUR and nearly 11 months for USD. The data were downloaded from NSE website. The USD-INR futures were nearly stable and EUR-INR futures were most volatile. The JPY-INR futures had highest rise in price trends. Prediction of USD/INR future outperformed other two with least error. However, LSTM model that was trained, relatively underperformed in case of JPY-INR.

Introduction The social media landscape has radically changed and has revolutionized consumer perspectives, purchasing habits, and behaviors. Amidst this emerging trend is the rise of influencer marketing and its impact on the purchase intentions of followers. The objective is to explore the characteristics of influencers that contribute to their credibility. This research aims to explore the role of consumers’ attitude toward brands on their intention to adopt brands endorsed by influencers. Methods This cross-sectional research was undertaken among GenZ in the urban landscape of India. Data collected was analyzed using SmartPLS4 software. Findings Trust, expertise, and similarity were the significant antecedents of the formation of influencer credibility. Attractiveness did not have a significant influence on influencer credibility. A complementary partial mediation of Attitude towards a brand is observed in the association between influencer credibility and the purchase intention of followers. Attitude towards the video also had a significant positive influence on purchase intention. Conclusion The study found that Gen Z places little importance on an influencer’s attractiveness, as it has no significant impact on credibility. However, attitude toward the brand strongly influenced purchase intention and partially mediated the relationship between influencer credibility and purchase intention.

Introduction The social media landscape has undergone radical changes and has revolutionized consumer perspectives, purchasing habits, and behaviours. Amidst this emerging trend is the rise of influencer marketing and its impact on the purchase intentions of followers. The objective is to explore the characteristics of influencers that contribute to their credibility. This research aims to explore the role of consumers’ attitude toward brands on their intention to adopt brands endorsed by influencers. Methods This cross-sectional research was undertaken among GenZ in the urban landscape of India. Data collected was analyzed using SmartPLS4 software. Findings Trust, expertise, and similarity were the significant antecedents of the formation of influencer credibility. Attractiveness did not have a significant influence on influencer credibility. A complementary partial mediation of Attitude towards a brand is observed in the association between influencer credibility and the purchase intention of followers. Attitude towards the video also had a significant positive influence on purchase intention. Conclusion The study found that Gen Z places little importance on an influencer’s attractiveness, as it has no significant impact on credibility. However, attitude toward the brand strongly influenced purchase intention and partially mediated the relationship between influencer credibility and purchase intention.

The study was conducted on BUX Index volatility for the post-2008 (from 2011) global financial crisis period using advanced GARCH models (GARCH, TGARCH, EGARCH, IGARCH, PARCH, APARCH). Based on parameters and test results appropriate model was chosen (APARCH (1,1) at Student’s t distribution) to study volatility, the presence of asymmetry, leverage effect, volatility clustering, and decay factor. Test results were linked with the macro and microenvironment (Political instability, Geopolitical crisis, Unemployment, Inflation, COVID-19, Slowdown, etc.) of the index and unique features of the index have been discovered (Like a sudden huge dip between 2020-2022). The study through mathematical and econometrics terms establishes causal links among the variables affecting the index. The paper is relevant to both investors and policymakers as BUX is one of the most important indicators as far as stock market in Hungary is concerned.

Introduction The social media landscape has undergone radical changes and has revolutionized consumer perspectives, purchasing habits, and behaviors. Amidst this emerging trend is the rise of influencer marketing and its impact on the purchase intentions of followers. The objective is to explore the characteristics of influencers that contribute to their credibility. This research aims to explore the role of consumers’ attitude toward brands on their intention to adopt brands endorsed by influencers. Methods This cross-sectional research was undertaken among GenZ in the urban landscape of India. Data collected was analyzed using SmartPLS4 software. Findings Trust, expertise, and similarity were the significant antecedents of the formation of influencer credibility. Attractiveness did not have a significant influence on influencer credibility. A complementary partial mediation of Attitude towards a brand is observed in the association between influencer credibility and the purchase intention of followers. Attitude towards the video also had a significant positive influence on purchase intention. Conclusion The study found that Gen Z places little importance on an influencer’s attractiveness, as it has no significant impact on credibility. However, attitude toward the brand strongly influenced purchase intention and partially mediated the relationship between influencer credibility and purchase intention.

The research study voyage commences with the foundational objective of fitting a suitable Generalized Autoregressive Conditional Heteroscedastic (GARCH) model to assess market volatility, a fundamental pillar of financial analysis. This research embarks on an ambitious quest to predict and understand stock market volatility within the realms of the DJIA and S&P 500 of USA and ATX index of Austria using different sophisticated GARCH models. The dataset used in this study comprises daily stock market data for two key indices: the S&P 500 Index, representing the USA stock market, and the ATX Index, representing the Austria stock market. Additionally, the DJIA Index, another representative of the USA stock market, was included. The dataset consists of 5967 daily observations over the specified time period from January 3, 2000, to September 21, 2023. The observation of results, analysis and discussion depicts that PARCH model shows most promising results and found suitable to model the volatility patterns of the selected indices. The findings and methodologies presented in this paper can be seen as a solid foundation upon which to build future investigations, refining our ability to anticipate market movements and make informed decisions in an uncertain financial landscape. In closing, this research not only contributes to the body of knowledge in financial econometrics but also underscores the importance of modeling long-term stock market behavior with precision and diligence.

The act of modeling and forecasting stock market volatility has become essential to risk management practice; it has become one of the most prevalent subjects in financial econometrics and has been mainly and continuously used in the valuation of financial assets and the Value at Risk, as well as the pricing of options and derivatives. The purpose of this paper is to contrast the FIGARCH model in the prospect of establishing the best algorithm to forecast PSI 20 index (PSI stands for Portuguese Stock Index). This study analyzed PSI 20 index to identify the behavior of the Portugal stock market in terms of volatility and then evaluated the forecasting ability of GARCH family models using Portugal PSI 20 index sample data for the long period from May 2010 - August 2023 (which is more than 13 years in daily data). There are 3396 daily observations. The results indicated that the PSI 20 index’s volatility is asymmetric. The present empirical investigation also seeks to illustrate the potential for investment returns as well as the associated risk. Our findings may have implications for risk management in Portugal, as well as a deeper understanding of the PSI-20 Portugal stock market volatility dynamics, given the scarcity of prior such studies.

Asian countries are facing environmental degradation issue which is the result of non-renewable energy usage for growth however this issue cannot be unnoticed. This study is an effort to highlight the causes for an emerging issue of environmental degradation in selected Asian economies. The research findings demonstrate that urban population, energy consumption and economic growth increases carbon emission CO2 (i.e., environmental degradation) in Asian countries. It is suggested that strategy formulation in Asian economies should implement and indorse renewable energy sources in order to meet up the high energy demand and to reduce CO2 emissions for certain sustainable economic development in Asian countries.

The adoption of financial technology (fintech) has the potential to make banking and financial services more accessible and convenient for all, but there are significant barriers preventing the adoption of fintech by street vendors and hawkers in India. This study aims to identify and analyse the barriers to fintech adoption using interpretive structural modelling (ISM). The research identified nine key barriers to fintech adoption, including perceived risk, lack of trust, lack of perceived benefit, social influence, lack of awareness, cash culture, lack of literacy, perceived ease of use, and lack of financial literacy. The study utilizes ISM to develop a hierarchy of these barriers and their interrelationships. The findings suggest that to promote the adoption of fintech, it is essential to build trust and awareness of fintech services through education and out-reach programs. This research provides a comprehensive understanding of the barriers to fintech adoption among street vendors and hawkers in India and offers insights into how these barriers can be overcomed. The study contributes to the development of strategies that can help promote the adoption of fintech among marginalized populations and advance financial inclusion in India.

The solar energy industry's positive impact on India's GDP is perceptible through increased investments, innovation, and enhanced energy security. As the nation continues to prioritize clean energy solutions, the solar sector stands as a key player driving both economic prosperity and environmental sustainability, aligning India with world wide determinations to battle climate change and encourage a greener future. As the Indian government continues to champion initiatives promoting renewable energy, Solar Energy Companies have seen unprecedented growth and have become increasingly attractive to investors seeking long-term, sustainable returns. This influx of interest, however, brings with it the challenge of navigating the volatile and dynamic nature of the stock market. In this context, forecasting the stock prices of solar energy corporations in India becomes a pivotal aspect of investment strategy for both institutional and retail investors. This paper targets to add to the prevailing body of knowledge by evaluating the efficacy of the Random Forest model, a machine learning technique known for its versatility and robustness, in forecasting the stock prices of top four Solar Energy Companies in India on the basis of market capitalization, by using the daily opening, high, low and closing stock prices ranging from 1st October, 2019 to 30th September, 2023 i.e. 4 years. The findings reveal that high Coefficient of Determination (R2) values for all companies, ranging from 0.9928 to 0.9939 is a clear indication of the model's ability to predict a substantial portion of the variance in each company's stock prices. But in case of Adani Green Energy Ltd a notably higher MSE and RMSE are exhibited, implying a greater degree of fluctuation in prediction accuracy compared to the other companies. On the other hand, all the selected solar energy companies display lower MAE values, indicating tightly clustered predictions around actual values.

The fintech segment is currently one of the most rapidly growing industries, attracting numerous investors who anticipate substantial returns in the future. Notably, not only individual retail investors but also mutual fund agencies are actively engaged in predicting stock prices within this sector to maximize their trading gains. The purpose of the study is to formulate stock forecasting models for top three Fintech Companies of India i.e., Policy Bazar, One 97 Communications Paytm Ltd., and Niyogin Ltd. Using Random Forest model with high-frequency data in Python. The literature review section also proves that this study is a novel piece of work as none of the existing research study focused on predicting stock prices of Fintech Companies of India using Random Forest model. The data is extracted from www.moneycontrol.com and www.kotaksecurities.com, for the period from 1st October, 2022–30 th September, 2023. The study deals about 293,280 data points i.e., 3 companies @ 97,760 each. It has been found that the forecasting model of random forest provides very successful results for prediction as the co-efficient of determination of all the selected companies is more than 95%.

Each country’s banking system plays a crucial role in its economic development. Without a sound banking system, no economy can exist. In the current banking environment, due to the increasing complexity of the banking system financial performance evaluation is becoming increasingly important. Thus, the financial health of banks has been regularly evaluated. The present study focuses on the performance appraisal of five public sector banks i.e., SBI, Bank of Baroda, Canara Bank, PNB and Union Bank. Regression Analysis, WACC, and CAMEL Model are some of the tools used to measure bank’s performance via., CAMEL parameters this evaluation has been done. This is a well-established method of comparing the performance of financial institutions as well as banks. This way of evaluating banks' performance is widely recognized. This model evaluates a bank's performance based on several factors, including capital sufficiency, asset quality, management, earnings, and liquidity. To determine capital adequacy in the current research, we took into account: Capital Adequacy Ratio and Government Securities to Total Investments Ratio. To measure asset quality we considered: Total Investment to Total Assets and Net NPA Ratio. To measure Management efficiency we considered: Business per employee and Profit per employee. To measure earnings quality we considered: EPS (Earnings per share); Operating Profit to Average Working Funds and Dividend payout ratio. To measure liquidity we considered: Investment to total deposits and Government securities to total deposits. The study has considered a period of six years i.e., from 2017 to 2022. The primary objective of the present study is to focus on the performance appraisal of five Indian banks all the banks are public sector banks viz., SBI, Bank of Baroda, Canara Bank, PNB and Union Bank. Apart from that, this study also gives a ranking with the help of the overall performance of banks. This study has used secondary data sourced from annual reports of Banks and also taken help from few websites. Various financial ratios and averages have been used to analyze the information collected.

High frequency data is a recent entrant to the world of statistics as they relate to the markets. This study measures the volatility of S& P / Toronto index by utilizing the GARCH family models (EGARCH, TGARCH, MGARCH and PGARCH models) using a daily database counted 7636 observations. The empirical results for the selected index for the whole-time frame show that it is volatile, and the PGARCH is a better-fitted model with the student's t distribution. Positive shocks have a greater impact on conditional volatility than negative shocks, according to the data. These extra analyses can be supplied to further corroborate their results and provide useful information for economic thespians interested in adding Toronto stock market index to their investment portfolios.

AbstractThis research paper investigates the enablers of Sustainable Development Goals (SDG) leadership among Small and Medium Enterprises (SMEs) in South Asian nations using Interpretive Structural Modelling (ISM). The study focuses on identifying the factors that facilitate SDG leadership in SMEs, including shared vision, long-term perspective, co-creation, shared responsibility, empowering people, eco-centric values, creativity and innovation, bonding and trust, knowledge sharing, strategic adaption, and progressive culture. The research findings show that these factors are interrelated, and their integration can help SMEs to achieve sustainable development and become SDG leaders in their respective industries. The study contributes to the existing literature by providing insights into the enablers of SDG leadership among SMEs in South Asian nations and proposing a framework for their successful implementation. The proposed framework can be used as a guide for policymakers, managers, and entrepreneurs to promote sustainable development in their organizations and the region.

AbstractPromoting women’s economic participation and empowerment is essential for sustainable development in South Asian Countries. Studies have established that women entrepreneurs in South Asia face a noteworthy challenge with many lacking formal education, lack of entrepreneurship education, capital, skilled and trained manpower, management skills, networking, infrastructural support, fear of failure, gender discrimination, and loaded with family responsibilities. This research aims to evaluate the contextual relationships among identified key challenges and develop a hierarchical framework of key challenges for addressing barriers to promote women entrepreneurship in South Asian Countries. The primary study was conducted using Interpretive Structural Modelling on 31 women entrepreneurs from India, Nepal, Sri Lanka, Pakistan, and Bangladesh. The study proposed that multi-dimensional efforts are needed to ensure that women have equal access to economic opportunities and can fully participate in and contribute to the region’s economic development.

The role of youth in investment is huge when we compare that to the old generation and their perceived attitudes about cryptocurrency investment is getting increased these days in India. This study's primary goal was to assess people's attitudes among young people regarding cryptocurrency awareness and investment. Most of the youth have not yet purchased bitcoin, were just familiar with cryptocurrencies, and lack a comprehensive overview of potential risks. The study’s data was gathered from primary and secondary sources of data. The fundamental information obtained by a questionnaire sent to more than 200 active and passive investors. The secondary sources of data used for the completion of this assignment include journals, magazines, internet websites, textbooks, and a review of literature. Several hypotheses were generated and evaluated with the intention of providing youths with useful suggestions.

The main aim of this research paper is to conduct a comparative empirical study on the behavior of the stock markets in Italy and Poland. In this sense, it is examined the presence of volatility patterns using GARCH family models for the sample period from December 2008 to December 2022. The selected period covers a long-time interval, so that the effects of certain extreme events can be implicitly evaluated. Moreover, the selected stock markets are both included in the category of developed markets. Considering that Italy and Poland are both member states of the European Union it is relevant to also analyze the impact of events such as BREXIT or the conflict between Russia and Ukraine.

The process of building information modeling is one of the most effective advances in the construction industry in the last decade, which is developing rapidly around the world. On the other hand, the construction industry of many developing countries is a vital industry and a driver of development, but it is inefficient and underdeveloped. Although building information modeling can have a great impact on the efficiency of this industry, but there are many obstacles in its way and the flow of its application in the construction industry of these countries has slowed down. Identifying these barriers in different fields and considering the local conditions of the construction industry in developing countries has been one of the main objectives of this research. Some of these barriers are rooted in the technical shortcomings of the Beam system, but many also stem from the nature and characteristics of the construction industry and its relationships and functions. In order to successfully implement this advanced and efficient system and enjoy its advantages, these obstacles must be identified and examined according to their origin, and appropriate solutions must be designed to make this process run more quickly. In this research, first BIM technology and the benefits of its implementation are introduced and then the obstacles and problems during the implementation of BIM in some developing countries in this field are investigated and finally, according to the theory of self-efficacy, solutions to eliminate these barriers are presented.

This study examines changes in volatility clusters and volatility patterns using GARCH class models in the Netherlands stock market in the context of the COVID-19 pandemic and global financial crisis (GFC) pandemic. The movement pattern of the AEX stock market index during the sample period from January 3, 2000 to December 2, 2022 a daily closing adjusted prices considered for the empirical investigation. The global financial crisis and the COVID-19 pandemic's effects are both included in the sample period. GARCH (1,1), GJR (1,1), and EGARCH (1,1) models are part of the econometric framework. By adding further empirical data on the long-term behavior of the Netherlands stock market, this empirical study adds to the body of current literature. We find changes in volatility after the COVID – 19 pandemic period, sharp rise in the index levels and presence of leverage effect in returns.

The recent global pandemic impacted stock markets worldwide, including developed and emerging markets. This paper investigates changes in volatility from a sample of daily returns ofFTSE100, DAX and CAC for the UK, Germany, and France, respectively. We test the fitness of GARCH (1, 1) to model the volatility, measure the interrelationship between selected samples, and abstract the changes in volatility before and during the pandemic period. Used and analysed daily closing returns from 2000-01-01 to 2022-31-01 with Generalised Autoregressive Conditional Heteroskedasticity (GARCH 1, 1) and Value-at-Risk (VaR) with Normal and Mills approach. Data has been divided into three phasesbefore, during, and after the Covid 19 pandemic. The finding confirms persistent volatility for selected samples, the strong interrelationship among the German stock market and UK stock market than in France and German markets, dynamic changes in volatility patterns before, during and after the pandemic. The study results confirm the increase in normal volatility patterns after the pandemic. Further, finding exhibits the dynamics of volatility and response during the different four-phases, changing the degree of risk and prospective returns.

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.

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 (

The purpose of this article is to concentrate on the stylized data in the financial series of the major index DAX of the German stock market. Moreover, we investigated the effects of positive and negative news on the volatility of the stock market of Germany, such as DAX index. One of the most fascinating topics for investor research is the financial market volatility of an emerging financial market. Because of this, factorial risks and the likelihood of larger returns are increased. We take into account daily OBS (observations) in the number of 4037 for the sample period January 2007 to November 2022. The study used the GJR-GARCH, or Generalized Autoregressive Conditional Heteroskedisticity type model. We discovered that the DAX index financial series feature a dynamic volatility scale. The GJR-GARCH model was fitted and the stronger impact of innovations was discovered.

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.

The current research was conducted with the topic of investigating the effect of cultural, economic and technological factors on the risk management of construction companies in Iran. This research was applied in terms of purpose and descriptive-survey in terms of method. The statistical population of this research is: senior managers and engineers of grade 1 construction companies in Iran, of which 120 people were selected as a sample through sampling in available was selected and a researcher-made questionnaire was distributed among them and they were asked to rate each item according to its importance from one (lowest) to five (highest). To analyze the data, partial least squares technique was used with the help of SmartPLS software, and the results showed that cultural and economic factors do not affect the risk management of Iran construction companies, but technological factors have an effect on the risk management of Iran construction companies.

The trading of natural rubber derivatives in the Indian commodity exchanges was banned several times in the past. Hence, in India, the derivatives on natural rubber are not traded actively and regularly. We have examined the possibility of a forecast model and a cross hedge tool for the natural rubber price by using crude oil futures in India. Results of the Johansen cointegration test proved that there is no cointegration equation in the model; hence, there is no scope to develop long-run models or error correction models. We have developed a vector autoregressive [VAR(2)] model to forecast the rubber price, and we examined the possibility of a cross hedge for natural rubber further by using the Pearson correlation coefficient and Granger causality test. We have extended our research to a structural VAR analysis to examine the effect of crude futures and exchange rate shocks on the natural rubber price. Our results showed that there is a short-term relationship between the crude oil futures price, the exchange rates of the US dollar to the Indian rupee, the Malaysian ringgit to the Indian rupee and the Thai baht to the Indian rupee; and the natural rubber price in India. The effort of policymakers to cause the Indian rupee to appreciate against the Thai baht and Malaysian ringgit may increase the natural rubber price in India. Natural rubber traders, growers and consumers can use crude futures to hedge the price risk. The Indian Rubber Board can suggest the VAR(2) model to predict the short-run price for natural rubber.

The main objective of this empirical study is to investigate the impact of tax revenue on GDP dynamics at EU-28 level based on structural equation modeling (SEM). We applied structural equation modeling (SEM) which represents a multivariate statistical analysis technique used especially for analyzing structural relationships between measured variables and latent structures. Selected taxes are the following: environmental taxes, indirect taxes, social contributions, taxes on capital, taxes on labour, taxes on property, and direct taxes. The sample period includes a long time horizon during 2005-2017 for each member states of EU-28. Our empirical findings revealed the level of taxation exhibits an increased influence on GDP dynamics in case of EU-28 member states. JEL Codes - C3; E62; O47; O52

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.

India is the major producer of Areca nut in the world. Volatile demand and price are the major challenges for the Areca nut growers in India. The use of time series models to manage the price risk has become the interest of academicians today. This paper deals with developing an appropriate model to predict the prices of a new variety of Areca nut in Karnataka using monthly price data for the period January 2009 to December 2018. Box Jenkins ARIMA methodology is used to develop the model. Along with ARIMA estimates, log-likelihood, Akaike’s information criterion (AIC) and Bayesian (BIC) information criterion statistics are also estimated to decide on the appropriate model. ACF and PACF correlograms for residuals of ARIMA are used to do the diagnostic check of the selected ARIMA model. Appropriate model to forecast the new variety Areca nut price is ARIMA (3, 1, 3)

Purpose – This article examines volatility spillovers, cross-market correlation, and comovements between selected developed and former communist emerging stock markets in the European Union. Modelling the behavioural dynamics of European stock markets represents a vital topic in a fascinating context, but also a current challenge of great interest. Research Methodology – We propose to estimate and model volatility using GARCH family models for selected European markets. We aim to explore volatility movement, presence of leverage effect/ asymmetry in selected financial markets. Findings – The econometric approach includes GARCH (1, 1) models for the sample period from 1, January 2000 to 12, July 2018. The empirical results revealed that exists a significant presence of volatility clustering in all selected financial markets except Poland and Croatia. The empirical analysis also indicates that both recent and past news generate a considerable impact on present volatility. Research limitations – Our empirical study has certain limitations regarding the relatively small number of only eight stock markets. Practical implications – It can provide a useful perspective for researchers, academics, investors, investment managers, decision-makers, and scientists. Originality/Value – The empirical analysis is focused on 8 European stock markets, which are classified as developed (Spain, UK, Germany, and France) and emerging (Poland, Hungary, Croatia, and Romania).

The main aim of this empirical study is to examine short and long run volatility movements based on a case study for Norwegian Stock Market, i.e. Oslo Stock Exchange. The econometric framework includes a series of statistical tests, ARIMA models and GARCH family models for the sample period from March 2013 to October 2021. The empirical results were influenced by the impact of COVID19 pandemic. This research paper also contributes to the existing literature regarding the influence of extreme events, such as COVID-19 pandemic on the behavior of developed stock markets, like Norwegian Stock Market.

The fundamental objective of our research study is to provide a critical analysis on Efficient Market Hypothesis (EMH), Adaptive Market Hypothesis (AMH) and Fractal Markets Hypothesis (FMH) considering their impact on stock markets behavior. Efficient Market Hypothesis is one of the pillars of modern finance and it is built on the paradigm that any publicly information can be considered as available for all financial investors, stock market participants or other actors in financial markets, and consequently asset prices always integrate and reflect all relevant information. Adaptive Market Hypothesis is based is a more recent theory whose theoretical architecture includes evolutionary principles. On the other hand, the Fractal Market Hypothesis is focused on the concept of the stock market liquidity, considering the fact that Efficient Market Hypothesis completely ignores this major aspect. Moreover, a liquid stock market represents a stable market which has significant implications at the investment level. Past financial evidence has shown that short-term price changes exhibit the obvious tendency to be more volatile compared to longterm price trends.

This study evaluates performance of Indian index considering NIFTY MIDCAP 50 index daily series returns. Autoregressive model EGARCH forecasts the volatility predictability and empirically analyze volatility pattern considering daily returns from NIFTY 50 index. The study tests presence of asymmetry in volatility transmitting patterns, Movement of higher positive and negative magnitude of shocks and fitness of the model. For this purpose data series considered from October 2007 to April 2021 consisting 3321 daily observations. This empirical study also attempts to capture the opportunity for investment returns and involvement of risk. Findings provide financial series movement, volatility sketches, summary of statistics and property of EGARCH model and fitness of series returns in EGARCH model.

Many investors in order to predict stock prices use various techniques like fundamental analysis and technical analysis and sometimes rely on the discussions provided by various stock market analysts. ARIMA is a part of time-series analysis under prediction algorithms, and this paper attempts to predict the share prices of selected pharmaceutical companies in India, listed under NIFTY100, using the ARIMA model. A sample size of 782 time-series observations from January 1, 2017 to December 31, 2019 for each selected pharmaceutical firm has been considered to frame the ARIMA model. ADF test is used to verify whether the data are stationary or not. For ARIMA model estimation, significant spikes in the correlogram of ACF and PACF have been observed, and many models have been framed taking different AR and MA terms for each selected company. After that, 5 best models have been selected, and necessary inculcation of various AR and MA terms has been made to adjust the models and choose the best adjusted ARIMA model for each firm based on Volatility, adjusted R-squared, and Akaike Information Criterion. The results could be used to analyze the stock prices and their prediction in-depth in future research efforts.

The main aim of this paper is to investigate volatility spillover effects, the impact of past volatility on present market movements, the reaction to positive and negative news, among selected financial markets. The sample stock markets are geographically dispersed on different continents, respectively North America, Europe and Asia. We also investigate whether selected emerging stock markets capture the volatility patterns of developed stock markets located in the same region. The empirical analysis is focused on seven developed stock market indices, i.e. IBEX35 (Spain), DJIA (USA), FTSE100 (UK), TSX Composite (Canada), NIKKEI225 (Japan), DAX (Germany), CAC40 (France) and five emerging stock market indices, i.e. BET (Romania), WIG20 (Poland), BSE (India), SSE Composite (China) and BUX (Hungary) from January 2000 to June 2018. The econometric framework includes symmetric and asymmetric GARCH models i.e. EGARCH and GJR which are performed in order to capture asymmetric volatility clustering, interdependence, correlations, financial integration and leptokurtosis. Symmetric and asymmetric GARCH models revealed that all selected financial markets are highly volatile, including the presence of leverage effect. The stock markets in Hungary, USA, Germany, India and Canada exhibit high positive volatility after global financial crisis.

There are two kinds of strategies to control Non Performing Assets i.e. curative and preventive. The paper is an attempt to focus on the effectiveness of various preventive strategies in controlling NPA in future.For this study primary data have been collected from 82 branches out of 138 branches of Sagar District in Madhya Pradesh of India. The respondents are the branch managers or recovery officer of each branch.The primary data are related to the causes of NPA, actual usage of preventive measures and effectiveness of each preventive strategy. It highlights few new causes which are barely covered by the earlier researches. The study also represents the actual usage of various preventive measures along with the effectiveness of preventive measures in averting NPA to be occurred in future. The outcome of this study could provide a valuable insight about which strategy is more effective to prevent these stressed assets. Besides that, it could aware the banking authorities regarding the problems faced by the managers in using the preventive measures.

This research attempts to integrate the Virtual Try-On technology, an application of image integrative technology, and the technology acceptance framework to examine the influence of interactivity and customer involvement on the online shopping experience. It attempts to empirically test the conceptual model to establish how the virtual trial of apparel attributes influences conative responses towards an apparel retail website. The antecedents of the conative response (purchase intention) have been empirically validated for a sample size of 410 internet users in India specifically of the millennial cluster. Result reveals the importance of confidence in apparel fit, utilitarian value, and hedonic value in strengthening the purchase intention of an online shopper.Body esteem is also a significant antecedent to purchase intention, while customers shop online. This research endeavor affirms the role of a virtual trial in strengthening the purchase intention in the virtual market space. The outcomes of this study add to the body of knowledge about the efficacy of Image Integrative Technology.

Liberalization of the economy has cemented the open road for many to become entrepreneurs. The possibilities created by today’s comprehensive awareness together with the unfettering of indigenous ventures, has laid a fertile ground for entrepreneurship to thrive on in India. There are several challenges faced by entrepreneurs today and study reveals that most of challenges are because of ethical issues in growing entrepreneurship. The main objective of this research paper is to investigate the entrepreneurship and business developments and their intrinsic relationship with the concept of ethics. Currently, the linkage between entrepreneurship and business ethics is a growing challenge with multidimensional implications.

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.

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.

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.

Purpose: The purpose of this paper is to formulate a multiple regression model by considering those factors which are positively affecting the growth of Micro, Small and Medium Enterprises (MSMEs) in India. Methodology: The data used in this study are primary in nature which are collected through questionnaire method. The questionnaire involves the nature and size of business and opinion of the owners and managers of 454 MSMEs in Katihar district, a semi-urban area of Bihar in India, regarding the various favourable factors of digital banking on a Likert scale of 1 to 10. Findings: The findings of the study suggest the various favourable factors of digital banking with significant coefficients i.e. Level of Easiness in accepting payments, Level of Easiness in making payments, Level of Easiness in Managing the expenditure of Business, Level of Time Saving, and Level of Check on Misappropriation or Theft of Cash are contributing towards the growth of MSMEs in India. Research Implications: This research will be helpful to the bank managers and policy makers to encourage the MSMEs of semi-urban areas to use digital banking by stressing on only significant favourable factors and also to take necessary steps so that the MSMEs could reap the full benefits of digital banking. The study could give a new insight regarding the extent of contribution of digital banking towards the growth of MSMEs in rural and semi-urban area.

Existence of short-term momentum effect is a widely disputed topic. This paper aims to solve the dispute and examine the presence of short-term momentum effect in the stock market of Hong Kong. Empirical results of 16 monthly price momentum investment strategies suggest that short term momentum effect has been found in Hong Kong’s stock market and it is overwhelmingly strong. Future research should focus on investigating momentum effect using weekly and daily price momentum strategies.

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.

The purpose of the present paper is to provide an advanced overview of the practical applications of Banking 4.0 in Industry 4.0. This paper examines the technology trends in the Fourth Industrial Revolution and identifies the key indicators behind the creation of a strategic map for the fourth-generation banks and their readiness to enter Industry 4.0. This paper examines a systematic review of fully integrated Banking 4.0 and the application of the technologies of Industry 4.0 and illustrates a distinct pattern of integration of Banking 4.0 and Industry 4.0. One of the prominent features of this article is the performance of successful global banks in applying these technologies. The results showed that Banking 4.0 in Industry 4.0 is an integrative value creation system consisting of six design principles and 14 technology trends. The roadmap designed for banks to enter Industry 4.0 and how they work with industrial companies will be a key and important guide.

This research paper examines in a comparative manner the long-term behavior of certain developed economies, such as USA, Canada, France and UK. The applied financial econometrics approach includes relevant research methods such as descriptive statistics, Unit Root Test, Hodrick-Prescott (HP) filter, Augmented Dickey-Fuller stationary test, BDS test, Granger causality test/Vector AutoRegression (VAR) model and GARCH (1, 1) model. The empirical results provide additional evidence on volatility spillovers, dynamic causal linkages and international contagion patterns between developed stock markets considering international portofolio diversification benefits. The sample financial data series are based on daily returns of selected stock markets major indices, ie during the period from January 2000 until June 2018

Introduction The social media landscape has radically changed and has revolutionized consumer perspectives, purchasing habits, and behaviors. Amidst this emerging trend is the rise of influencer marketing and its impact on the purchase intentions of followers. The objective is to explore the characteristics of influencers that contribute to their credibility. This research aims to explore the role of consumers’ attitude toward brands on their intention to adopt brands endorsed by influencers. Methods This cross-sectional research was undertaken among GenZ in the urban landscape of India. Data collected was analyzed using SmartPLS4 software. Findings Trust, expertise, and similarity were the significant antecedents of the formation of influencer credibility. Attractiveness did not have a significant influence on influencer credibility. A complementary partial mediation of Attitude towards a brand is observed in the association between influencer credibility and the purchase intention of followers. Attitude towards the video also had a significant positive influence on purchase intention. Conclusion The study found that Gen Z places little importance on an influencer’s attractiveness, as it has no significant impact on credibility. However, attitude toward the brand strongly influenced purchase intention and partially mediated the relationship between influencer credibility and purchase intention.

Introduction The social media landscape has undergone radical changes and has revolutionized consumer perspectives, purchasing habits, and behaviours. Amidst this emerging trend is the rise of influencer marketing and its impact on the purchase intentions of followers. The objective is to explore the characteristics of influencers that contribute to their credibility. This research aims to explore the role of consumers’ attitude toward brands on their intention to adopt brands endorsed by influencers. Methods This cross-sectional research was undertaken among GenZ in the urban landscape of India. Data collected was analyzed using SmartPLS4 software. Findings Trust, expertise, and similarity were the significant antecedents of the formation of influencer credibility. Attractiveness did not have a significant influence on influencer credibility. A complementary partial mediation of Attitude towards a brand is observed in the association between influencer credibility and the purchase intention of followers. Attitude towards the video also had a significant positive influence on purchase intention. Conclusion The study found that Gen Z places little importance on an influencer’s attractiveness, as it has no significant impact on credibility. However, attitude toward the brand strongly influenced purchase intention and partially mediated the relationship between influencer credibility and purchase intention.

Introduction The social media landscape has undergone radical changes and has revolutionized consumer perspectives, purchasing habits, and behaviors. Amidst this emerging trend is the rise of influencer marketing and its impact on the purchase intentions of followers. The objective is to explore the characteristics of influencers that contribute to their credibility. This research aims to explore the role of consumers’ attitude toward brands on their intention to adopt brands endorsed by influencers. Methods This cross-sectional research was undertaken among GenZ in the urban landscape of India. Data collected was analyzed using SmartPLS4 software. Findings Trust, expertise, and similarity were the significant antecedents of the formation of influencer credibility. Attractiveness did not have a significant influence on influencer credibility. A complementary partial mediation of Attitude towards a brand is observed in the association between influencer credibility and the purchase intention of followers. Attitude towards the video also had a significant positive influence on purchase intention. Conclusion The study found that Gen Z places little importance on an influencer’s attractiveness, as it has no significant impact on credibility. However, attitude toward the brand strongly influenced purchase intention and partially mediated the relationship between influencer credibility and purchase intention.

Artificial Intelligence (AI) can enhance the efficient use of green energy and increase the scalability of renewable energy. The global shift towards green energy has to managed and optimized which is a major challenge. Deep learning can play a vital role in predictive maintenance and storage. The data available from renewable energy systems can be analyzed by the artificial intelligence (AI) in real time to predict and manage energy generation and demand response which drastically reduces wastage of energy and distribution of energy can be more efficient. In this context, the integration of AI with renewable energy systems represents a paradigm shift in addressing the global energy crisis. Currently, an increased importance is gained by green technologies in order to obtain a sustainable economic wealth. [ABSTRACT FROM AUTHOR]