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Connectivity, sport events, and tourism development of Mandalika’s special economic zone: A perspective from big data cognitive analytics
Evaluating Efficacy of Statutory Disclaimers of Mutual Funds on Novice and Seasoned Investors
Business Perspectives and Research, Ahead of Print.
The purpose of the present study was to empirically examine the efficacy of statutory disclosures in the Indian mutual fund industry. Whether the disclaimers aid investors in their decision-making process was investigated. The study made a distinction between type of investors (novice and seasoned investors) as disclaimers affect differently on investor’s belief, attitude and ability to take informed decision. Survey was conducted using a structured questionnaire to evaluate the responses of 388 investors, consisting of 243 novice and 145 seasoned investors. Data was analyzed using mean comparison, independent t-test, and logistic regression model. Results revealed that statutory disclaimers were less effective on seasoned investors compared to novice investors. This suggests seasoned investors process the disclaimer information differently. Novice investors systematically process the disclaimers of mutual fund advertising, and their investment decision was meaningfully affected by the disclaimers. The study offers specific suggestions for stakeholders working in the area of behavioral finance, highlighting the importance of considering the dual process theory of information processing. To the best of authors knowledge, this study is the first of its kind to evaluate the efficacy of mandatory disclaimers in the Indian mutual fund industry, providing unique insights for future research in the field.
The purpose of the present study was to empirically examine the efficacy of statutory disclosures in the Indian mutual fund industry. Whether the disclaimers aid investors in their decision-making process was investigated. The study made a distinction between type of investors (novice and seasoned investors) as disclaimers affect differently on investor’s belief, attitude and ability to take informed decision. Survey was conducted using a structured questionnaire to evaluate the responses of 388 investors, consisting of 243 novice and 145 seasoned investors. Data was analyzed using mean comparison, independent t-test, and logistic regression model. Results revealed that statutory disclaimers were less effective on seasoned investors compared to novice investors. This suggests seasoned investors process the disclaimer information differently. Novice investors systematically process the disclaimers of mutual fund advertising, and their investment decision was meaningfully affected by the disclaimers. The study offers specific suggestions for stakeholders working in the area of behavioral finance, highlighting the importance of considering the dual process theory of information processing. To the best of authors knowledge, this study is the first of its kind to evaluate the efficacy of mandatory disclaimers in the Indian mutual fund industry, providing unique insights for future research in the field.
The Determinants of Inbound Tourism Demand in India: New Evidence from ARDL Co-Integration Approach
Business Perspectives and Research, Ahead of Print.
The research used the Autoregressive Distributive Lag (ARDL) model to examine the long- and short-term impact of changes in currency rates and global income on tourist demand in India employing monthly data from 2003 (1) to 2020 (12). We find that exchange rate volatility, global income, and tourism demand are all significantly interrelated. A 15% convergence to the long-run equilibrium path of tourism demand occurs in line with the pace of adjustment through the channel of global income and currency rate. Positive and substantial effects of rising global income are shown over the short and long terms. There is, nevertheless, a positive short-term relationship between currency depreciation and visitor numbers. Additionally, the Toda–Yamamoto method is used for Granger non-causality. The results point to a one-way causal relationship between the currency exchange rate and the number of visitors. It has also been shown that there is a causal relationship in both directions between the demand for tourism and global GDP. The nation is in a special position due to its location, physical characteristics, cultural heritage, and other comparative advantages. According to the findings, a stable currency rate and global income are the two most important factors in increasing tourist interest in India.
The research used the Autoregressive Distributive Lag (ARDL) model to examine the long- and short-term impact of changes in currency rates and global income on tourist demand in India employing monthly data from 2003 (1) to 2020 (12). We find that exchange rate volatility, global income, and tourism demand are all significantly interrelated. A 15% convergence to the long-run equilibrium path of tourism demand occurs in line with the pace of adjustment through the channel of global income and currency rate. Positive and substantial effects of rising global income are shown over the short and long terms. There is, nevertheless, a positive short-term relationship between currency depreciation and visitor numbers. Additionally, the Toda–Yamamoto method is used for Granger non-causality. The results point to a one-way causal relationship between the currency exchange rate and the number of visitors. It has also been shown that there is a causal relationship in both directions between the demand for tourism and global GDP. The nation is in a special position due to its location, physical characteristics, cultural heritage, and other comparative advantages. According to the findings, a stable currency rate and global income are the two most important factors in increasing tourist interest in India.
Cross-listing flows under uncertainty: an international perspective
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Determinants of choice of credit source among clients of microfinance systems in the Upper West Region of Ghana
The mediating impact of airlines’ image in relation of corporate social responsibility and customers’ loyalty: An empirical in Vietnam
Intraday Risk Management of Cryptocurrency Returns During 2020–2021 Upsurge: A Conditional EVT Approach
Business Perspectives and Research, Ahead of Print.
The cryptocurrency market is characterized by extremely high volatility. In the present study, we show the predictive ability of conditional EVT models in the cryptocurrency market during the price upsurge of 2020–2021. Taking high-frequency intraday data of four popular cryptocurrencies, Bitcoin, Ethereum, Litecoin, and Binance coin, we compare the accuracy of different competing models in estimating intraday value at risk (VaR) and expected shortfall (ES). The present study focuses on the extreme value theory (EVT) for modeling the tail of the distribution to forecast the measures of intraday VaR and ES. The study confirms the fat-tailed behavior of intraday returns of all four cryptocurrencies. Further, the study shows the magnitudes of high negative shocks are more than the positive ones for the returns of all four cryptocurrencies. The study uses suitable GARCH-family models such as apARCH, EGARCH, and CGARCH in the ARMA-GARCH framework. Using a two-stage approach the study shows how GARCH-EVT models with skewed student’s—t distribution outperform the predictability of conditional EVT with standard normal distribution as well as the unconditional EVT models in predicting intraday VaR and ES. The result of the study is useful for risk managers, day traders, and also for machine-based algorithmic trading.
The cryptocurrency market is characterized by extremely high volatility. In the present study, we show the predictive ability of conditional EVT models in the cryptocurrency market during the price upsurge of 2020–2021. Taking high-frequency intraday data of four popular cryptocurrencies, Bitcoin, Ethereum, Litecoin, and Binance coin, we compare the accuracy of different competing models in estimating intraday value at risk (VaR) and expected shortfall (ES). The present study focuses on the extreme value theory (EVT) for modeling the tail of the distribution to forecast the measures of intraday VaR and ES. The study confirms the fat-tailed behavior of intraday returns of all four cryptocurrencies. Further, the study shows the magnitudes of high negative shocks are more than the positive ones for the returns of all four cryptocurrencies. The study uses suitable GARCH-family models such as apARCH, EGARCH, and CGARCH in the ARMA-GARCH framework. Using a two-stage approach the study shows how GARCH-EVT models with skewed student’s—t distribution outperform the predictability of conditional EVT with standard normal distribution as well as the unconditional EVT models in predicting intraday VaR and ES. The result of the study is useful for risk managers, day traders, and also for machine-based algorithmic trading.
The impact of activity type and use of health and safety protocols for destination recovery following a health crisis
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Are the informal economy and cryptocurrency substitutes or complements?
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