The purpose of this research is to study the relationship between risk and return on the BRVM. The empirical results, obtained using the Asymmetric Response Model (ARM) model, show the asymmetric nature of the return of the securities that are rated on them. This does not reflect the level of risk taken by investors, which is much higher than the return obtained. While this result is consistent with the distancing characteristics of risk and return observed in emerging markets, it highlights above all the need to rebalance the relationship between risk and return at the RSE in order to make it more attractive for investors.
Affecting millions of individuals yearly, malaria is one of the most dangerous and deadly tropical diseases. It is a major global public health problem, with an alarming spread of parasite transmitted by mosquito (Anophele). Various studies have emerged that construct a mathematical and statistical model for malaria incidence forecasting. In this study, we formulate a generalized linear model based on Poisson and negative binomial regression models for forecasting malaria incidence, taking into account climatic variables (such as the monthly rainfall, average temperature, relative humidity), other predictor variables (the insecticide-treated bed-nets (ITNs) distribution and Artemisinin-based combination therapy (ACT)) and the history of malaria incidence in Dakar, Fatick and Kedougou, three different endemic regions of Senegal. A forecasting algorithm is developed by taking the meteorological explanatory variable Xj at time t−𝓁j, where t is the observation time and 𝓁j is the lag in Xj that maximizes its correlation with the malaria incidence. We saturated the rainfall in order to reduce over-forecasting. The results of this study show that the Poisson regression model is more adequate than the negative binomial regression model to forecast accurately the malaria incidence taking into account some explanatory variables. The application of the saturation where the over-forecasting was observed noticeably increases the quality of the forecasts.
The objective of this paper is to study the contemporaneous relationship and the dynamic relationship between the stock index return and the trading volume on the Bourse Régionale des Valeurs Mobilières using daily data from 5 January 2015 to 31 October 2022. Estimations are made using the generalized method of moments (GMM) and generalized autoregressive conditional heteroscedasticity or GARCH (1,1) specifications for the contemporaneous regressions and the vector autoregressive specification for the dynamic (causal) relationship. The contemporaneous specifications show that there is no significant relationship between stock returns and trading volume. Neither of the two variables significantly influences the other. Furthermore, the dynamic specification shows a causality running from stock returns to trading volume but the reverse is not true. For the period covered by the study, the results invalidate both the mixture of distribution hypothesis and the sequential information arrival hypothesis and open the way for other considerations such as behavioral models.
Considering the environmental field around the Congo Basin area (in Central Africa), many initiatives implement and communicate on their activities via the internet, yet the information they provide remains very little exploited, even inaccessible. The data is scattered on the internet and it is quite difficult to find information in a fairly precise way. Despite the existence of Internet research services (search engines) developed to facilitate the search for information in the vast data network that is the Internet, there are still concerns about quality, and the relevance of information provided in as research results. In this context, we present in this paper a construction approach and the architecture of a search engine dedicated to the environment around the Congo Basin area. This paper develops a theoretical approach that uses scientific analysis and empirical approaches to conceptualize the optimization of the relevance of the results of a thematic search engine by aggregating tools. This approach is a compilation of ideas, methods and tools that, put together, will improve the relevance of the results of a thematic research.
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