ABSTRACT
The COVID-19 pandemic has inflicted the global economy and caused substantial financial losses. The energy sector was heavily affected and resulted in energy prices massively tumbling. The Russian invasion of Ukraine has fueled the energy maker more volatile. In such uncertain contexts, an Early Warning System (EWS) would efficiently contribute to stabilizing market swings. It will leverage the ability to control operating costs and pave the way for smooth economic recovery. Within this framework, we deploy Machine Learning (ML) models to forecast energy equity prices by employing uncertainty indices as a proxy for predicting energy market volatility. We empirically examine the comparative effectiveness of prevalent ML models and conventional approaches (regression) to forecast the energy equity prices by utilizing the daily data from 1/6/2011 to 18/1/2022 for four US uncertainty and eight energy equity indices. Results show that the Nonlinear Autoregressive with External (Exogenous) parameters (NARX) of Neural Networks (NN) scored significantly better accuracy than all other (25) ML models and conventional approaches. The study outcomes are beneficial for policymakers, governments, market regulators, investors, hedge and mutual funds, and corporations. They improve stakeholders' resilience to exogenous shocks, blaze the recovery path, and provide evidence-based for assets allocation strategies.
ABSTRACT
Mucormycosis, a secondary fungal infection, gained much attention in the ongoing COVID-19 pandemic. This deadly infection has a high all-cause mortality rate and imposes a significant economic, epidemiological, and humanistic burden on the patients and healthcare system. Evidence from the published epidemiological studies showed the varying prevalence of COVID-19-associated mucormycosis (CAM). This study aims to compute the pooled prevalence of CAM and other associated clinical outcomes. MEDLINE, Embase, Cochrane COVID-19 Study Register, and WHO COVID-19 databases were scanned to retrieve the relevant articles until August 2021. All studies reporting the prevalence of mucormycosis among COVID-19 patients were eligible for inclusion. Two investigators independently screened the articles against the selection criteria, extracted the data, and performed the quality assessment using the JBI tool. The pooled prevalence of CAM was the primary outcome, and the pooled prevalence of diabetes, steroid exposure, and the mortality rate were the secondary outcomes of interest. Comprehensive Meta-Analysis software version 2 was used for performing the meta-analysis. This meta-analysis comprised six studies with a pooled sample size of 52,916 COVID-19 patients with a mean age of 62.12 ± 9.69 years. The mean duration of mucormycosis onset was 14.59 ± 6.88 days after the COVID-19 diagnosis. The pooled prevalence of CAM (seven cases per 1000 patients) was 50 times higher than the highest recorded background of mucormycosis (0.14 cases per 1000 patients). A high mortality rate was found among CAM patients with a pooled prevalence rate of 29.6% (95% CI: 17.2-45.9%). Optimal glycemic control and the judicious use of steroids should be the approach for tackling rising CAM cases.
ABSTRACT
PurposeThis paper aims to investigate how digital financial inclusion (DFI) can be a potential factor to maintain banking stability in Association of Southeast Asian Nations (ASEAN) countries and whether the relationship could bring a possible implication for the post-Covid-19 pandemic era.Design/methodology/approachUsing an unbalanced panel data of 213 banks of 4 ASEAN countries, the study has deployed principal component analysis, ordinary least square, two-step dynamic system generalised method of moments and panel corrected standard errors techniques.FindingsThe empirical study finds that the full-fledged application of DFI accelerates the ASEAN banking stability which not only decreases the default risk of the banks but also upturns the financial mobility in the region. The results also suggest that ASEAN banks are, with the implementation of DFI, likely to uphold the banking sector stability by reducing liquidity crisis and non-performing loans during and in the post-Covid-19 era. Therefore, accelerating digital finance in ASEAN countries is considered as one of the significant means for the banking sector stability that subsequently leads to economic and financial resilience even in the face of any crises.Originality/valuePrevailing studies have mostly investigated the association between financial inclusion and banking stability in different contexts. However, this study is unique to empirically investigate the association between DFI and the ASEAN banking stability.