Suchergebnisse

This study examines 147 banking crises in the period of 1976-2011 documented by the International Monetary Fund. The countries affected by crises are analysed in respect of publicly available World Bank indicators in the periods of three years before the crises. Machine learning methodology for subgroup discovery is used for the analysis. It enabled identification of five subsets of crises. Two of them are identified as especially useful for the characterization of EU countries affected by the banking crises in the year 2008. Fast growing credit activity is a characteristic for the first subgroup while socioeconomic problems recognized by non-increasing quality of public health are decisive for the second subgroup. Comparative analysis of the EU countries included into the second subgroup and the EU countries affected by the banking crises but not included into this subgroup demonstrated statistically significant differences in respect of World Bank good governance indicator values for the period before the crisis. Control of corruption, rule of law, and government effectiveness are the indicators that are statistically different for these sets of countries. The result is fully in accordance with the Francis's model connecting governance indicators and financial fragility. The significance of the result is in the segmentation of the corpus of countries with banking crises and recognition of connections between banking crises, socioeconomic problems, and governance effectiveness in some EU countries. The conclusions of the study might be useful for the policy makers in stressing that future banking crises prevention should also focus on governance effectiveness, more strict law implementation and especially on measures against corruption.

The starting point for the research has been the list of 147 banking crises within the period 1976-2011 prepared by the International Monetary Fund. The countries with crises have been analysed with respect to publicly available World Bank indicators in the periods of three years before the crises. The machine learning methodology for subgroup discovery has been used for the analysis. It enabled identification of five subsets of crises. Two of them have been identified as especially useful for the characterization of EU countries with banking crises in the year 2008. Fast growing credit activity is characteristic for the first subgroup while socioeconomic problems recognized by non-increasing quality of public health are decisive for the second subgroup. Comparative analysis of EU countries included into these subgroups demonstrated statistically significant differences with respect to World Bank good governance indicator values for the period before the crisis. Control of corruption, rule of law, and government effectiveness are the indicators which are statistically different for these sets of countries. The significance of the result is in the segmentation of the corpus of countries with banking crises and the recognition of connections between banking crises, socioeconomic problems, and governance effectiveness in some EU countries.

This study examines the impact of bilateral investment treaties (BITs) on Swiss foreign direct investment (FDI). It also investigates the role of BITs as protective tools of Swiss investment. This paper is based on secondary data analysis; data is obtained from various official entities. This study uses statistical and machine learning techniques in order to detect meaningful relationships between BITs and FDI flows. Our findings suggest that the implementation of BITs have an insignificant impact on the increase of Swiss FDI flows. However, from our examination, two interesting findings have emerged suggesting that the completion of BITs may have an impact on the increase of political stability and rule of law of partner countries. ; Este estudio examina el impacto de los tratados bilaterales de inversión (TBI) de la inversión extranjera directa suizo (IED). También indaga el papel de los TBI como herramientas de protección de inversiones. Esta investigación se basa en análisis de datos secundarios, los datos son obtenidos de diversas entidades oficiales. Este estudio utiliza técnicas de aprendizaje estadístico con el fin de detectar relaciones significativas entre los TBI y los flujos de IED. Nuestros resultados sugieren que la implementación de tratados bilaterales de inversión tiene un impacto insignificante en el aumento de IED suizos. Sin embargo, desde nuestro examen,dos hallazgos interesantes han surgido, proponiendo que los TBI puedan tener un impacto en el aumento de la estabilidad política y el estado de derecho de los países asociados.

This study examines the impact of bilateral investment treaties (BITs) on Swiss foreign direct investment (FDI). It also investigates the role of BITs as protective tools of Swiss investment. This paper is based on secondary data analysis; data is obtained from various official entities. This study uses statistical and machine learning techniques in order to detect meaningful relationships between BITs and FDI flows. Our findings suggest that the implementation of BITs have an insignificant impact on the increase of Swiss FDI flows. However, from our examination, two interesting findings have emerged suggesting that the completion of BITs may have an impact on the increase of political stability and rule of law of partner countries.

An interdisciplinary literature suggests that institutional environment features are significant determinants in the location of foreign direct investment. Weak institutional environment features are considered to have a negative effect on capital inflows. Conversely, empirical studies have found that investors locate in host countries providing high rates of return despite weak institutional environment features. Switzerland is home to some of the most important global investors and one of Europe's largest global investors. Swiss foreign direct investment is an interesting case to examine since it has been nurtured in a well-established institutional environment. In this paper, we evaluate Swiss foreign direct investment located in 56 countries over the period 2005-2009 using statistical and machine learning techniques. From our analysis two models emerge suggesting that Swiss investment favours countries with high political stability and high accountability. However, we also find that Swiss investment does not necessarily discriminate against countries with weak institutional environments.

This paper examines the impact of bilateral investment treaties (BITs) on Swiss foreign direct investment (FDI) flows. It also investigates the role of BITs as protective tools of Swiss investment. This paper is based on secondary data analysis; data is obtained from various official entities. This study uses statistical and machine learning techniques in order to detect meaningful relationships between BITs and FDI flows. Our findings suggest that the implementation of BITs have an insignificant impact on the increase of Swiss FDI flows. However, from our examination, two interesting findings have emerged suggesting that the completion of BITs may have an impact on the increase of political stability and role of law of host countries.

Foreign direct investment has been extensively recognised as an important resource of economic growth. Governments have been playing an active role in encouraging this type of investment. Despite efforts by governments, only a few countries have been successful in attracting investment for their tourism industry. The purpose of this paper is to detect meaningful relationships between government policies and investment.

Political risk assessment, together with portfolio analysis, has become an important part of international business investment decision making. In this paper, by using different knowledge discovery techniques, we attempt to assess the relevance and actionability of 'good governance' indicators in political risk assessment. For that purpose, we have integrated good governance indicators data with political instability classification from the Political Instability Task Force (PITF) research. From this study, two models that capture the intuitive reasoning of investors have been developed. The usefulness of the models has been evaluated by comparison with factual investment data. Our findings suggest that quantitative indicators of good governance may provide the basis for accurate and meaningful models for the assessment and prediction of political risk.