The recent COVID-19 pandemic has forced educational institutions worldwide to adopt e-learning. UAE higher education institutions have implemented e-learning systems and programs to cope with this unprecedented situation. This paper measured the strength of association between key aspects of e-learning systems and programs and students’ motivation to learn in Ajman University (AU). Cronbach’s coefficient alpha was used to test the internal consistency reliability of key aspects of e-learning (EL-8) and students’ motivation to learn (SML-16). Exploratory factor analysis was used to test the validity of, and coherence of patterns in, the data. Parametric and non-parametric methods were used to investigate the strength of association between key aspects of e-learning and students’ motivation to learn in AU. The results indicated that motivation variables were more strongly correlated with both e-teaching materials and e-assessments key aspects relative to others such as e-discussion, and e-grade checking and feedback.

The aim of this study is to investigate if Ichimoku Cloud can serve as a technical analysis indicator to improve stock price prediction for leading US energy companies. The methodology centers on the application of the Ichimoku Cloud as a trading system. The daily stock prices of the top ten constituents of the S&P Composite 1500 Energy Index -spanning the period from 12 th April, 2012 to 31 st July, 2019 -were sourced for experimentation. The performance of the Ichimoku Cloud is measured using both the Sharpe and Sortino ratios to adjust for total and downside risks. The analysis is split into pre and post oil crisis to account for the drop in energy stock prices during the July 2014 -December 2015. The model is also benchmarked against the naïve buy-and-hold strategy. The capacity of the Ichimoku indicator to provide signals during strengthening trends is analyzed. Despite the drop in energy stock prices, number of trades continued to increase along with profit opportunities. The PSX stock ranked first, with the highest Sharpe ratio, Sortino ratio, and Sharpe per number of trade. As expected, a number of buying signals occurred during strengthening bullish periods. Surprisingly, various sell signals also occurred during similar strengthening bullish trends. Most of the buy and sell signals under the Ichimoku indicator occurred outside of strengthening of bullish or bearish trends. The overall findings suggest that speculators can benefit from the use of the Ichimoku Cloud in analyzing energy stock price movements. In addition, it has the potential to reduce susceptibility to changes in energy prices. Last, the strength of the trend in place needs to be captured as it served as an additional layer of information which can improve the decision making process of the trader.

Most technical analysis tools focus traditionally on the simple and exponential moving average technique. This study looks at the performance of an optimized fractal adaptive moving average strategy over different frequency intervals, where the Euro/US Dollar currency pair is analyzed due to the increased correlation between the Euro Index and EUR/USD, and the Dollar Index and EUR/USD over the last year compared to the last 15 years. The optimized strategy is evaluated against a buy-and-hold strategy over the 2000-2015 period, using annualized returns, annualized risk and Sharpe performance measure. Due to the existence of different number of long and short trades in every trading scenario, this paper proposes the use of a new measure called the Sharpe/Total trades ratio which takes into account the number of trades when evaluating the different trading strategies. Findings strongly support the use of the adaptive fractal moving average model over the naïve buy-and-hold strategy where the former yielded higher annualized returns, lower annualized risk, a higher Sharpe value, although it was subject to more trades than the buy-and-hold strategy. The best market timing strategy occurred when using 131 daily fractal data with a Sharpe/Total trades ratio of 0.31%.

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Background: The COVID-19 pandemic has caused major public health and economic disruption. At the same time, a pandemic allows researchers to assess market efficiency; namely, whether, to what extent, and how swiftly stock markets incorporated information related to COVID-19. Soon after the outbreak of the pandemic, research on this front was conducted, with a particular focus on the United States of America (US) market. However, new major events linked to the pandemic have unfolded: a number of vaccines were announced and authorized. The research available in relation to market efficiency relative to vaccine availability is scant. The aim of this study was to assess market efficiency hypotheses with regards to the US and United Kingdom (UK) markets, investigating the impact of the promising news of the vaccines’ successful trials and, subsequently, their authorization. Methods: This work considered data from the S&P500 for the US market and the FTSE100 for the UK market. The time interval considered ranged from the date positive results of vaccines’ trials were first announced, 18 November 2020, until up to two months after the vaccines’ authorization that happened later in December 2020. For both markets, we analyzed the daily returns, cumulative returns, standard deviation and average returns. Results: In both the US and the UK, there was a positive effect of the vaccines’ announcements in terms of increase in the daily returns. However, the standard deviation was not found to increase substantially, notwithstanding the increase in the COVID-19 cases worldwide and the potential lockdown in several countries, as well as the fear from new coronavirus strains that the new vaccines might not be protect against. Conclusions: Whilst both markets displayed an increase in the average return following the vaccines’ announcements, the UK market seemed to reflect vaccines’ announcements faster than the US market.

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Insights from the analysis of views towards energy sources are of paramount importance for the setting of successful energy policies, especially in instances where the public might be reluctant towards certain projects’ implementations. This work presents an analysis of social media comments data given in response to posts around the connection to the grid of a nuclear plant reactor in the United Arab Emirates (UAE). We assessed comments on Facebook posts of local and international media, as well those written in response to a post of a social media influencer. We extracted the main themes and performed sentiment analysis. The results indicate the presence of mixed views towards nuclear energy when focusing on comments on international media’s posts as well as on the social media influencer’s post considered, whilst they were very positive when assessing comments to local media. All in all, nuclear waste and previous nuclear accidents appear to be as the top of the mind; at the same time, solar energy is often suggested in the comments as a viable energy source for the UAE. Implications for the communication of nuclear energy developments in social media are discussed.

This paper investigates if energy block chain based crypto currencies can help diversify equity portfolios consisting primarily of leading energy companies of the US S&P Composite 1500 energy index. Key contributions are in terms of assessing the importance of energy cryptos as alternative investments in portfolio management, and whether different volatility models such as autoregressive moving average -Generalized Autoregressive Heteroskedasticity (ARMA-GARCH) and machine learning (ML) can help investors make better investment decisions. The methodology utilizes the traditional Markowitz mean-variance framework to obtain optimized portfolio combinations. Volatility measures, derived from the Cornish-Fisher adjusted variance, ARMA family classes and ML models are used to compare efficient portfolios. The study also analyses the effect of adding cryptos to equity portfolios with non-positive excess returns. Different models are assessed using the Sharpe performance measure. Daily data is used, spanning from November 21, 2017 to January 31, 2019. Findings suggest that energy based cryptos do not have a significant impact on energy equity portfolios, despite the use of different risk measures. This is attributable to the relatively poor performance of energy cryptos which did not contribute in improving the excess return per unit of risk of efficient portfolios based on the leading US energy stocks.