How does split announcement affect stock liquidity? Evidence from Bursa Malaysia

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This study examines the impact of stock splits on stock liquidity in Bursa Malaysia from 2004–2018. The study uses event study methodology and investigates liquidity changes, the role of liquidity, and the relationship between abnormal returns and liquidity as well. We found a significant liquidity improvement on the splits announcement, announcement of book closing date and split execution date (Ex-date), while it declined after the split Ex-date. The findings also indicate that firms with a low-level liquidity prior to split announcements experienced an increase in liquidity after Ex-date. Using panel data analysis, we find that the fixed effect model is more appropriate than the pooled OLS, and the abnormal announcement returns are driven by stock liquidity

Global, regional, and national burden of colorectal cancer and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Funding: F Carvalho and E Fernandes acknowledge support from Fundação para a Ciência e a Tecnologia, I.P. (FCT), in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy i4HB; FCT/MCTES through the project UIDB/50006/2020. J Conde acknowledges the European Research Council Starting Grant (ERC-StG-2019-848325). V M Costa acknowledges the grant SFRH/BHD/110001/2015, received by Portuguese national funds through Fundação para a Ciência e Tecnologia (FCT), IP, under the Norma Transitória DL57/2016/CP1334/CT0006.proofepub_ahead_of_prin

EXTENDING BOOTSTRAP AGGREGATION OF NEURAL NETWORKS FOR PREDICTION WITH AN APPLICATION TO COVID-19 FORECASTING

The aim of this study is to improve the forecasting accuracy of artificial neural networks (ANNs) and construct prediction bands for ANN models. The focus is on forecasting for epidemiological purposes, and in particular, the problem of predicting new case and death counts from seven to h days into the future for spatially contiguous regions. The task poses several challenges: datasets are quite small, and both spatially and temporally correlated. To overcome these, the methods attempt to exploit information induced by spatial and temporal dependencies. More importantly, we have developed a fusion of ANNs and bootstrap methods. Bootstrap aggregation (bagging) is an ensemble technique used for reducing the prediction variance and concurrently improving predictive accuracy and constructing prediction bands. Random forests extend bagging by sampling predictors in addition to observations with the result of often dramatic improvement in accuracy. The method developed herein resembles random forests to improve predictive accuracy and to construct prediction bands. We refer to this new approach as extended-bagging (EBagging). Covid-19 is a highly contagious virus that has disrupted life around the world. Accurate predictions of disease trajectory in the near term are critical. Recurrent neural networks based on gated recurrent units (GRU) are a subclass of ANNs that exploits temporal data structures; however, they are problematic to use and remain poorly understood by researchers. Hence, we propose a simple alternative referred to as weighted neural networks and use this with E-Bagging. To investigate and compare these innovations with standard ANN approaches, we apply the methods to Covid-19 datasets using four counties as the spatial units. The predictive functions forecast the number of deaths for 14 days ahead using four of the most populous US counties. The performance of models is quantified by the mean absolute error. The E-Bagging of GRU models yields highly informative predictions and outperformed the other prediction models. The assessment of constructed prediction bands is measured by coverage probability and the GRU model with the E-Bagging technique performed best. These methods can be applied to a wide variety of other situations from Ebola outbreak mitigation to intra and inter-day stock price forecasting

Injury burden in individuals aged 50 years or older in the Eastern Mediterranean region, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019

Background: Injury poses a major threat to health and longevity in adults aged 50 years or older. The increased life expectancy in the Eastern Mediterranean region warrants a further understanding of the ageing population's inevitable changing health demands and challenges. We aimed to examine injury-related morbidity and mortality among adults aged 50 years or older in 22 Eastern Mediterranean countries. Methods: Drawing on data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we categorised the population into adults aged 50–69 years and adults aged 70 years and older. We examined estimates for transport injuries, self-harm injuries, and unintentional injuries for both age groups, with sex differences reported, and analysed the percentage changes from 1990 to 2019. We reported injury-related mortality rates and disability-adjusted life-years (DALYs). The Socio-demographic Index (SDI) and the Healthcare Access and Quality (HAQ) Index were used to better understand the association of socioeconomic factors and health-care system performance, respectively, with injuries and health status in older people. Healthy life expectancy (HALE) was compared with injury-related deaths and DALYs and to the SDI and HAQ Index to understand the effect of injuries on healthy ageing. Finally, risk factors for injury deaths between 1990 and 2019 were assessed. 95% uncertainty intervals (UIs) are given for all estimates. Findings: Estimated injury mortality rates in the Eastern Mediterranean region exceeded the global rates in 2019, with higher injury mortality rates in males than in females for both age groups. Transport injuries were the leading cause of deaths in adults aged 50–69 years (43·0 [95% UI 31·0–51·8] per 100 000 population) and in adults aged 70 years or older (66·2 [52·5–75·5] per 100 000 population), closely followed by conflict and terrorism for both age groups (10·2 [9·3–11·3] deaths per 100 000 population for 50–69 years and 45·7 [41·5–50·3] deaths per 100 000 population for ≥70 years). The highest annual percentage change in mortality rates due to injury was observed in Afghanistan among people aged 70 years or older (400·4% increase; mortality rate 1109·7 [1017·7–1214·7] per 100 000 population). The leading cause of DALYs was transport injuries for people aged 50–69 years (1798·8 [1394·1–2116·0] per 100 000 population) and unintentional injuries for those aged 70 years or older (2013·2 [1682·2–2408·7] per 100 000 population). The estimates for HALE at 50 years and at 70 years in the Eastern Mediterranean region were lower than global estimates. Eastern Mediterranean countries with the lowest SDIs and HAQ Index values had high prevalence of injury DALYs and ranked the lowest for HALE at 50 years of age and HALE at 70 years. The leading injury mortality risk factors were occupational exposure in people aged 50–69 years and low bone mineral density in those aged 70 years or older. Interpretation: Injuries still pose a real threat to people aged 50 years or older living in the Eastern Mediterranean region, mainly due to transport and violence-related injuries. Dedicated efforts should be implemented to devise injury prevention strategies that are appropriate for older adults and cost-effective injury programmes tailored to the needs and resources of local health-care systems, and to curtail injury-associated risk and promote healthy ageing. Funding: Bill & Melinda Gates Foundation