The University of Buckingham Press Journals
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COVID 2.0 – THE CHANGING PANDEMIC ATTITUDES OF CASINO CUSTOMERS
No full textIn June 2021 we conducted a study to evaluate the potential for casinos to bounce back to pre-pandemic levels by addressing changing perceptions and priorities of their customers. Following this study, we have seen rapid changes in the perception of COVID-19 and, in turn, shifting customer behavior. Therefore, a follow-up national survey of US casino customers was conducted to better understand if pandemic related issues are still a concern, and, if not, what more can and brick and mortar casinos do to get customers back. The results of this follow-up study indicate dramatic changes in COVID related perceptions and shifting priorities of casino customers, offering implications and opportunity for casino operators
Is International Qualified Teacher Status (iQTS) A Solution to the Growing Demand for Teachers Globally?
Get PDFIs International Qualified Teacher Status (iQTS) A Solution to the Growing Demand for Teachers Globally
Using UK Data to Study the Effects of Dividends Announcements on Stock Market Returns
No full textUsing a sample of firms listed on the FTSE-350, this study examines the effects of dividends announcements on the London Stock Exchange (LSE) during the period from 1990 to 2019. We use the dividend-signalling hypothesis to test whether dividends announcements have any effects on stock returns. Our results suggest that dividend increase announcements have a positive effect on stock returns, and dividend decrease announcement reduce stock returns. On average, a dividend increase is estimated to increase stock returns by 6 basis points and a dividend decrease is estimated to reduce stock returns by the same amount. These findings are consistent with the dividend-signalling hypothesis
'I don't feel like I'm learning how to be a doctor': early insights regarding the impact of Covid-19 on UK medical student professional identity
No full textIntroduction
Professional identity formation is a priority of medical training. Covid-19 caused disruption to medical education. We ask how this disruption impacted professional identity formation through the lens of the activities performed – or not performed – by medical students during the first wave of the covid-19 pandemic, and perceptions of conflicts between these activities.
Methods
A pragmatic mixed-methods survey was distributed to medical students in the UK. The survey was active from 2nd May to 15th June 2020, during the height of the first wave of the Covid-19 pandemic in the UK. Operating within the paradigm of constructivism, we conducted a reflexive thematic analysis of qualitative responses to three open questions. Analysis was focused around the disruption to medical education, actions taken by medical students during this disruption, and the tension between student actions (where they existed in conflict).
Results
We analysed 928 responses and constructed three themes:
Status and role as a future doctor
Status and role as a student
Status and role as a member of the wider community
Conflict arose at the intersections between these three themes. Students noted that lack of clinical exposure was detrimental to their education, implicitly recognising that some aspects of professional identity formation require the clinical environment. Participants were keen to volunteer clinically, but struggled to balance this and academic work. Participants worried about risk to their families and the wider community and wanted to ensure their skills added value to the clinical environment. Volunteers felt frustrated when they were unable to perform tasks aligning with their identity as a future doctor. An exception was participants who worked as interim FY1s, aligned with the role of an FY1.
Conclusions
Medical students feel a duty to help during crises. There is conflict when different aspects of their identity demand different actions. Covid-19 heightened issues with developing professional identity for medical students, including lack of a defined role, and perceived undervaluing of their skills by other members of the healthcare team. Care must be taken to nurture professional identity formation even during periods of disruption
Normality Tests and its Power against Alternative Distributions: An Empirical Analysis on Emerging Asian Stock Index Returns.
No full textIn this paper, we investigate the power of various normality tests against alternative distributions using Monte Carlo simulation experiments. We use seven different normality tests classified as moments tests, correlation and regression tests, and empirical distribution functional tests against six symmetric and four asymmetric alternative distributions. We also perform the rank analysis for the power of the normality tests. Furthermore, we conduct an empirical analysis of five emerging Asian stock indices (India, Indonesia, Malaysia, Singapore, and Taiwan) to understand whether the returns follow a normal distribution or not during the period from January 2000 to January 2020. We find that emerging Asian stock index returns do not follow normal distribution irrespective of the different frequencies of the data
Predictive Power of An Ensemble Model for Cryptocurrency Forecasting: Cryptocurrency Forecasting using Ensemble Modeling
No full textCryptocurrencies have received much attention amongst investors and policymakers due to the innovative features and simplicity. However, prices of the cryptocurrencies are nonlinear and volatile, which creates challenges for the investors to forecast the cryptocurrency prices. The present study takes the price data of two important cryptocurrencies, i.e., Bitcoin and Ripple, for 2013 to 2020. The study presents the forecasting accuracy of statistical models such as random walk (RW) and autoregressive integrated moving average (ARIMA), and machine learning models such as artificial neural network (ANN) and ensemble model. The study develops the ensemble of RW, ARIMA, and ANN. The study compares the predictive power of all the models and demonstrates that the forecasting accuracy of the ensemble model is better than all the component models, i.e., RW, ARIMA, and ANN. The results of the study have several implications for investors, traders, and policymakers
The role of sustainability education within dermatological surgery in the United Kingdom
Get PDFSustainability recognises the limited planetary resources and is characterised by being able to sufficiently meet the needs of the current population without compromising future generations. The most recent iteration of the General Medical Council (United Kingdom) ‘Outcome for Graduates’ document required newly qualified doctors to have knowledge of sustainable healthcare (General Medical Council, 2018). Medical education is a key pillar in empowering the medical workforce to recognise the sustainability of the health services they provide and build competencies to reconfigure services and care pathways that will be resilient to the effects of climate change. This correspondence discusses the role of sustainability education within dermatological surgery in the United Kingdom
Bankruptcy Prediction using Machine Learning Techniques: Evidence on Indian companies under Insolvency and Bankruptcy Code: BANKRPUTCY PREDICTION OF INDIAN COMPANIES UNDER IBC
No full textThis paper attempts to evaluate the predictive ability of four machine learning models: logit, decision tree, random forest and 2-class support vector machine and to identify the key predictors of default. The models are applied on a dataset of 57 companies under the Insolvency and Bankruptcy Code (IBC) in India and a matched sample of 55 solvent companies spanning over ten years from FY06-FY 2016. The solvent companies are matched on size (log of total assets) and sector and are rated ‘AAA’ and ‘AA’. 31 explanatory variables are identified by us for the study which include (i) financial ratios (ii) size and age of the company, (iii) ownership pattern and (iv) market ratios. The empirical findings reveal that random forest strongly outperforms all other models in their predictive ability, followed by SVM, DT and logit model. The findings also confirm relevance of size and age of the firm, market ratios and ownership pattern as predictors of default in addition to financial ratios. We conclude that both parametric (logit) and non-parametric models are useful in the study of default prediction as reflected in the robustness of all models with accuracy of over 75 percent. These models can help banks in strategizing their lending decisions based on credit quality of borrower firms. Our contribution is that to the best of our knowledge this is the first paper that is using the database of companies that are legally defined as insolvent and bankrupt and also taking a balanced sample to avoid biasness and inaccuracy from data imbalance. Also, this study has gone beyond traditional financial statements in identifying key default drivers
Quitting while you're ahead: Evidence for individual gambling thresholds from a survey of Massachusetts Gamblers
Get PDFAlthough key stakeholders have discussed responsible gambling tools and protective behavioral strategies for years, evaluations of their effectiveness are still limited. Among protective behavioral strategies are individual gambling thresholds, typically on monetary losses or time spent gambling, after which a person stops gambling. A novel, counter-intuitive alternative, a monetary win threshold, also might hold value. Simulations have shown that, like monetary loss or time thresholds, win thresholds reduce the amount of time spent gambling and therefore also limit average expected gambling losses. Yet, little is known about gamblers’ use of gambling thresholds. In this paper, we examine data from an internet panel survey of past-year gamblers in Massachusetts to better understand the characteristics of those individuals who are more likely to use and adhere to loss and win thresholds. We observed that individuals who had engaged in recreational drug use were less likely to adopt gambling thresholds. Individuals who had previously received a positive screen for depression, and who travelled to out-of-state casinos were more likely to use gambling thresholds. In analyzing the adherence to gambling thresholds, we found that individuals who adhered to their loss thresholds were less likely to use ATMs during gambling sessions. Finally, individuals who engaged in hazardous drinking were less likely to adhere to their own win thresholds. This study adds to the literature by providing evidence related to the characteristics of gambling threshold users and contributes some of the only evidence in the literature on the actual use of monetary win thresholds