Current Issue : April - June Volume : 2019 Issue Number : 2 Articles : 5 Articles
This article analyzes through a panel data analysis covering the period 1994 to\n2016, the effects of the 2008 financial crisis on the determinants of Foreign\nDirect Investment in the West African Economic and Monetary Union\n(WAEMU). From the analysis, it turns out that this crisis has strongly impacted\nFDIâ??s determinants in the union. In fact, as consequence, it created a\nmuch more infatuation of resource-seeking investors in spite of market-\nseeking investors. This result shows that those countries can still attract\ninvestors even in time of crisis if they have tremendous natural resources,\nwhich makes the protection of these resources very important. In addition,\nwe can notice that after the crisis, the stability in the region became an important\ndecisional variable for foreign investors. That points out the importance\nof promoting a good political governance. Furthermore, the study has\nalso shown that the financial crisis has been a stepping corner for the emergence\nof a third type of investors in the region (efficiency seeking investors)....
The Bretton Woods system was abandoned by the U.S. government in 1971.\nIn order to learn to avoid the structural flaws that led to the collapse and ensure\na more stable economic condition in the future, this article aims to research\nthe cause of the collapse. It elaborates on the two main causes of the\nBretton Woods system: structural ones, such as the incompatible role of the\nUSD and the conflicting sovereign goals, and the operational ones, such as\nthe reluctance of other countries to follow the exchange rate rules. It then\ndisplays viewpoints from these two causes, and analyses them, that is, strengthens\nor undermines them, with facts and reasoning. Finally, it reaches the\nconclusion that the Bretton Woods system broke down in 1971 due to structural\nfactors instead of operational ones and gives some brief lessons from the\nfailure....
Behavioral finance is a novel approach in the financial markets domain. It\noriginates due to an urgent need to overcome and deal with the outstanding\nissues that traditional investors face in todayâ??s modern finance system. Thus,\nit is said that certain investors who do not have perfectly sensible elucidation\nregarding some financial situations and issues can recognize these issues better\nby means of certain financial models. Likewise, in a number of behavioral\nfinance models, investors are known to be unable to bring an up-to-date of\ntheir beliefs in the correct manner. However, other models show that investors\nadopt questionable choices in some cases. Thus, this paper introduces the\nbehavioral finance, describes the background and the aims and objectives of\nthe study, and it introduces the standards of the behavioral finance....
The multiple attempts at empirical evidence, yet recent, fail to truly dispel the\ntheoretical vagueness of the effects of public debt on economic growth. The\naim of this work is to demonstrate that public overindebtedness negatively\nimpacts economic activity in developing countries. From estimation by the\ngeneralized momentsâ?? method in the system of the relationship between economic\ngrowth and outstanding public debt on data of the Gabonese economy,\nwe get that an increase in the public debt in this country, causes a deceleration\nof economic activity, thus reflecting a scissor effect between public debt\ntrend and that of economic growth....
This paper mainly analyses the forecasting of sub-sovereign credit ratings using machine learning methods in the non-US, Europe and other regional and sub-sovereign ratings. Specific focus is based on developing an accurate forecasting model based on machine learning. The forecasting accuracy was examined on two forecasting horizons, one and two years ahead. The study was designed to determine the cost sensitivity of various machine learning methods and to develop an accurate decision-support system that minimizes the cost of credit rating classification for sub-sovereign entities across countries and world regions. Each side of the economic, financial and debt and budget, revenues and expenditures were considered to provide sufficient inputs for the machine learning models. The analyses is to consider the ordinal character of the rating classes, classification cost (cost-sensitivity) which is used as objective function, in assessing credit ratings and evaluating of bonds i.e. regional credit rating modeling. This paper has been able to demonstrate that machine learning models based on current available financial and economic data present accurate classifications of credit ratings. Also the sub-sovereign credit rating forecast signified that the Random Forest and SMO algorithm performed significantly better than the statistical methods. Some practical implications were also provided....
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