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The Amplilitc boom microphone headset has been granted F.A.A. Approval and meets the requirements or the F.A.A. Technical Standing Orders TSO‐C57 and TSO‐C58.

The purpose of this paper is to develop a hierarchical model to rank the challenges faced by the private Malaysian higher education institutions (HEIs) in the provision of quality education and subsequently their corresponding critical success factors (CSFs) to address those challenges.

A sequential mix method was adopted in this study. Semi-structured interviews with 29 participants were initially conducted to identify the challenges and CSFs. This was followed by a questionnaire survey involving 158 respondents to prioritise the identified findings. Thematic analysis was conducted in the qualitative stage, uncovering the challenges and their corresponding CSFs. Data for both stages were accumulated from internal and external stakeholders of Malaysian private HEIs. Finally, the four stages of the analytic hierarchy process (AHP) were applied to rank the challenges and CSFs.

The qualitative stage identified eight challenges, i.e. “academics”, “facilities”, “students”, “programmes and curriculum”, “competition”, “accreditation”, “finance” and “research” together with their corresponding CSFs. The AHP enables the ranking of these challenges. “Finance” has been found to be the most crucial challenge and “high competency in managing the institution’s finance” as the most important CSF to address this challenge.

As the study restricted its focus on Malaysian private HEIs, the results may not be generalised for public HEIs and foreign private HEIs operating in Malaysia.

The hierarchical model developed in this study is deemed important for implementation to resolve the prioritised challenges. It spells out the specific areas in which the resources of Malaysian private HEIs need to be prudently disbursed and properly managed.

The purpose of this study is to investigate the impact of terrorist attacks on the volatility and returns of the stock market in Tunisia.

The employed sample comprises 1250 trading day from the Tunisian stock index (Tunindex) and stock closing prices of 64 firms listed on the Tunisian stock market (TSM) from January 2011 to October 2015. The research opts for the general autoregressive conditional heteroscedasticity (GARCH) and exponential generalized conditional heteroscedasticity (EGARCH) models framework in addition to the event study method to further assess the effect of terrorism on the Tunisian equity market.

The baseline results document a substantive impact of terrorism on the returns and volatility of the TSM index. In more details, the findings of the event study method show negative significant effects on mean abnormal returns with different magnitudes over the events dates. The outcomes propose that terrorism profoundly altered the behavior of the stock market and must receive sufficient attention in order to protect the financial market in Tunisia.

Very few evidence is found on the financial effects of terrorism over transition to democracy cases. This paper determines the salient reaction of the stock market to terrorism during democratic transition. The findings of this study shall have relevant implications for stock market participants and policymakers.

This study aims to investigate the relationship between digital financial inclusion and economic growth in specific Asian countries, emphasizing the exploration of how digital financial inclusion dynamics impact gross domestic per capita income.

The study creates a digital financial inclusion composite index (DFII) by incorporating essential metrics from the Global Findex report. Economic growth is measured using Gross Domestic Product per capita income in its natural logarithmic form (LnPCI), with three control variables– employment-to-population ratio; population growth and inflation. The analysis utilizes a fixed-effect dummy variable model to examine the relationship, considering unobserved country-specific heterogeneity. 30 Asian countries have been selected for the study for the periods 2014, 2017 and 2021 based on their availability, as outlined in Table 4.

The research revealed a robust positive correlation between the Digital Financial Inclusion Index (DFII) and logarithmic GDP per capita income (LnPCI), indicating higher per capita income with enhanced digital financial inclusion. Employment and population exhibited minimal influence, whereas inflation had a notable negative effect on per capita income. Population growth showed a limited impact. The model demonstrated a high explanatory power for the dependent variable (high R-squared), and the residuals displayed low autocorrelation (Durbin–Watson of 1.96).

This study adds to the existing literature by examining the intricate connection between digital financial inclusion (DFI) and economic growth in 30 Asian countries, employing a comprehensive composite index for analysis.

The price clustering behavior is becoming a core part of the market efficiency theory especially with the development of trading strategies and the occurrence of major and unprecedented shocks which have led to severe inquiry regarding asset price dynamics and their distribution. However, research on emerging stock market is scant. The study contributes to the literature on price clustering by investigating an active emerging stock market, the Muscat stock market one of the Arabian Gulf Markets.

This research adopts the artificial intelligence technique and other statistical estimation procedure in understanding the price clustering patterns in Muscat stock market and their main determinants.

The findings reveal that stock prices are marked by clustering behavior as commonly highlighted in the previous studies. However, we found strong evidence of price preferences to cluster on numbers closer to zero than to one. We also show that the nature of firm’s activity matters for price clustering behavior. In addition, firms with traded bonds in Oman market experienced a substantial less stock price clustering than other firms. Clustered stock prices are more likely to have higher prices and higher volatility of price. Finally, clustering raised when the market became highly uncertain during the Covid-19 crisis especially for the financial firms.

This study provides novel results on price clustering literature especially for an active emerging market and during the Covid-19 pandemic crisis.

This paper aims to examine the causation linking financial technology to economic growth in the East African Community states from 1997 to 2019.

Autoregressive distributed lag is used. Gross domestic product per capita proxies economic growth, automated teller machines, point of sale, debit card ownership and mobile banking measure financial technology.

The results unveil a significant relationship between financial technology and economic growth. The findings show bidirectional causality between automated teller machine and economic growth, with unidirectional causation from economic growth to point of sales and internet banking, mobile banking and government effectiveness to economic growth. The error correction term is negatively significant, demonstrating a long-term convergence between Fintech measures and economic growth.

The governments should effectively enact and implement policies that protect investments in financial technologies to boost economic growth in the East African Community countries. The government should reduce taxes on financial technology equipment and related services. The use of automated teller machine, debit card ownership and internet banking should be encouraged through cashless transactions. Financial institutions should adopt cashless operation policies to encourage the use of financial technologies.

Research results on the bond between financial technology and economic growth are not conclusive. These studies demonstrate that technological innovations are double edged-swords, with both positive and negative sides. The results are conflicting; some reveal positive relationships, while others show negative links. Hence, research is required to fill the lacuna.

Terrorist events are unforeseen and have the potential to shake and rattle markets and investors. The purpose of this study is to examine whether major terrorist incidents have affected the Economic Sentiment Indicator (ESI) in four European countries.

An index is constructed that weights the severity of each event and then used to evaluate through the use of vector autoregressive and impulse response analysis estimation techniques whether or not and to what extent the ESI has been affected.

Effects were more pronounced and evident in the case of France and Germany while the ESI in Spain and Great Britain did not appear to be particularly affected by terrorist incidents.

The effects of terrorism on economic sentiment in other countries will provide additional evidence that will allow more robust and conclusive statistical inferences.

The impact of terrorist activity on the ESI for the four European countries studied here has not been examined before using VAR and impulse response analysis.

The aim of this study is to compare volatility persistence with daily volatility and to analyze the asymmetry effect of volatilities in stock markets of emerging economies. Using daily observations of stock market indices of selected major emerging countries during the period of January 1, 2002 to December 31, 2018, the authors estimate the persistence, the half-life measure of volatility and the daily volatility of the return series using the GARCH model application. The authors also examine the leverage effect on stock market returns using the EGARCH model estimation. In addition, the authors investigate the impact of the 2008 global financial crisis on various volatility measures and the leverage effect of emerging stock market returns. The authors then examine and compare the different speeds of mean reversion, volatility persistence and leverage effects in the national stock market indices during the pre-crisis, crisis, and post-crisis periods. The authors hereby present evidence that the effects of negative shocks are significantly larger than those of positive shocks in emerging stock markets throughout their different sample periods.

One important characteristic of cryptocurrencies has been their high and erratic volatility. To represent this complicated behavior, recent studies have emphasized the use of autoregressive models frequently concluding that generalized autoregressive conditional heteroskedasticity (GARCH) models are the most adequate to overcome the limitations of conventional standard deviation estimates. Some studies have expanded this approach including jumps into the modeling. Following this line of research, and extending previous research, our study analyzes the volatility of Bitcoin employing and comparing some symmetric and asymmetric GARCH model extensions (threshold ARCH (TARCH), exponential GARCH (EGARCH), asymmetric power ARCH (APARCH), component GARCH (CGARCH), and asymmetric component GARCH (ACGARCH)), under two distributions (normal and generalized error). Additionally, because linear GARCH models can produce biased results if the series exhibit structural changes, once the conditional volatility has been modeled, we identify the best fitting GARCH model applying a Markov switching model to test whether Bitcoin volatility evolves according to two different regimes: high volatility and low volatility. The period of study includes daily series from July 16, 2010 (the earliest date available) to January 24, 2019. Findings reveal that EGARCH model under generalized error distribution provides the best fit to model Bitcoin conditional volatility. According to the Markov switching autoregressive (MS-AR) Bitcoin’s conditional volatility displays two regimes: high volatility and low volatility.

Purpose: The present study aims to test the Quadratic Programming model for Optimal Portfolio selection empirically.

Need for the Study: All the investors who buy financial products are motivated to obtain higher profits or, in other words, to maximise their returns. However, the high returns are often accompanied by higher risks, and avoiding such risks has become the primary concern for all investors. There is a great need for such a model to maximise profits and minimise risk, which can help design an investment portfolio with minimum risk and maximum return. The Quadratic Programming model is one such model which can be applied for selected shares to build an optimised portfolio.

Methodology: This study optimises the stock samples using a two-level screening of correlation coefficient and coefficient of variation. The monthly closing prices of the NSE-listed Indian pharmaceutical stocks from December 2019 to January 2022 have been used as sample data. The Lagrange Multiplier method is used to apply the model to achieve the optimal portfolio solution. Based on the market reality, the transaction costs have also been considered. The Quadratic programming model is further optimised to achieve the optimal portfolio for the select stocks.

Findings: The traditional portfolio theory and the modified quadratic model gives similar and consistent results. In other words, the modified quadratic model asserts the accuracy of the conventional portfolio model. The portfolio constructed in the present study gives a return much higher than the return of the benchmark portfolio of Nifty Fifty, indicating the usefulness of applying the Quadratic Programming model.

Practical Implications: The construction of an optimal portfolio using the traditional or modified Quadratic model can help investors make rational investment decisions for better returns with lower risks.