The stock market plays a vital role in the economic development of any country. Stock market performance can be measured by the market capitalization ratio as well as many other factors. The primary purpose of this study is to predict the movement of the stock market based on the total market capitalization of the Dhaka Stock Exchange (DSE) using autoregressive integrated moving average (ARIMA) models as well as artificial neural networks (ANN). The data set covers monthly time series data of total market capitalization from November 2001 to December 2018. This study also shows the best model for forecasting the movement of DSE market capitalization. The ARIMA (2,1,2) model is chosen from among the several ARIMA model combinations. From several artificial neural networks (ANN) models as a modern tool, a three-layer feed-forward topology using a backpropagation algorithm with five nodes in the hidden layer, one lag, and a learning rate equal to 0.01 is selected as the best model. Finally, these selected two models are compared based on the Root-Mean-Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Theil’s U statistic. The results showed that the estimated error of ANN is less than the estimated error of the traditional method. Doi: 10.28991/ESJ-2022-06-05-09 Full Text: PDF
BackgroundCoronavirus has spread to almost every country since its emergence in Wuhan, China and countries have been adopted an array of measures to control the rapid spread of the epidemic. Here, we aimed to assess the person's knowledge, attitude and practices (KAP) toward the COVID-19 epidemic in Southeast and South Asia applying the mixed study design (cross-sectional and systematic review).MethodsIn the cross-sectional study, 743 respondents' socio-demographic and KAP-related information was collected through an online population-based survey from the Malaysian population. In the systematic review, the database PubMed, Web of Science and Google Scholar search engine were searched and related published articles from South and Southeast Asia were included. Frequency distribution, Chi-square association test and binary logistic regression were fitted using cross-sectional data whereas random effect model and study bias were performed in meta-analysis. We used 95% confidence interval and P <0.05 as statistical significances.ResultsThe prevalence of good knowledge, positive attitude and frequent practice toward COVID-19 epidemic were 52.6%, 51.8% and 57.1%, respectively, obtained by cross-sectional data analysis. The KAP prevalence were ranged from 26.53% (Thailand) to 95.4% (Nepal); 59.3% (Turkey) to 92.5% (Pakistan); and 50.2 (Turkey) to 97% (Afghanistan), respectively, obtained by 18 studies included in the meta-analysis. The prevalence of KAP was higher [84% vs. 79%, Pheterogeneity <0.001; 83% vs. 80%, Pheterogeneity <0.001; 85% vs. 83%, Pheterogeneity <0.001] in South Asia compared to Southeast Asia, obtained by subgroup analysis. Some studies reported mean level instead of the proportion of the KAP where the score varied from 8.15–13.14; 2.33–33.0; and 1.97–31.03, respectively. Having more knowledge and attitude were encouraged more likely to practice toward COVID-19. Study suggests age, gender, education, place of residence and occupation as the most frequent significant risk factors of KAP toward COVID-19.ConclusionThe study sufficiently informs how other countries in Southeast and South Asia enriches their KAP behaviors during the pandemic which may help health professionals and policymakers to develop targeted interventions and effective practices.
Climate governance has become a global issue, and it has proved difficult for any government to tackle this issue on its own. The role of civil society is most crucial, particularly in ensuring transparency and accountability in climate finance. Under certain international agreements, a huge amount of money is channeled in climate-vulnerable countries like Bangladesh through the climate financing mechanism. This is a tempting opportunity for a country routinely ranked first in the corruption index. This paper explores whether the growing involvement of various non-state actors in climate financing, under the dominant mechanism, creates a new ground for corruption together with the state actors. The paper aimed at helping ensure that climate finance decisions and actions are conducted with transparency, accountability, and integrity to prevent corruption and misuse of funds from undermining climate objectives. The main objective of the paper is to increase the capacity of stakeholders, particularly civil society, to contribute to the creation, implementation, and supervision of climate finance governance policies, with the participation of stakeholders, including government, fund managers, donors, Civil Society Organizations, non-governmental organizations, private sectors, and media analysis. Via content analysis, this study found that the Civil Society Organizations are getting caught up in the vicious circle of corruption in the climate finance sector in Bangladesh. Without having a separate mechanism for the Civil Society Organizations, there is little chance that their role will be positive in tackling corruption in this sector.
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