8,483 research outputs found

    "General Conclusions: From Crisis to A Global Political Economy of Freedom"

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    In this chapter I sum up the basic problems for a new theory of 21st century financial crises in light of the Asian and other subsequent crises. My conclusion is that there are indeed deep structural causes at work in the global markets that affect the political economy of countries and regions. Methodologically, new concepts, models and theories are constructed, at ;least partially, to conduct further meaningful empirical work leading to relevant policy conclusions. This book belongs to the beginning of intellectual efforts in this direction. Political economic analyses at the country level, CGE modeling within a new theoretical framework, and neural network approach to learning in a bounded rationality framework point to a role for reforms at the state, firm and regional level. A new type of institutional analysis called the 'extended panda's thumb approach' leads to the recommendation that path dependent hybrid structures need to be constructed at the local, national, regional and global level to lead to a new global financial architecture for the prevention--- and if prevention fails--- management of financial crises.

    Environmental Dynamic, Business Strategy, and Financial Performance: an Empirical Study of Indonesian Property and Real Estate Industry

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    Firm’s strategic orientation involves synchronizing environmental dynamics, corporate strategy and capital structure in order to achieve firm performance targets. The co-alignment model used successfully in the hospitality industry might be used in a wider context as a framework in explaining these relationships simultaneously. Using the data of public firms in Indonesia during the period of 1996-2010, we found that co-alignment model can be implemented in property and real estate industry as well as in hospitality industry

    A new perspective on the competitiveness of nations

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    The capability of firms to survive and to have a competitive advantage in global markets depends on, amongst other things, the efficiency of public institutions, the excellence of educational, health and communications infrastructures, as well as on the political and economic stability of their home country. The measurement of competitiveness and strategy development is thus an important issue for policy-makers. Despite many attempts to provide objectivity in the development of measures of national competitiveness, there are inherently subjective judgments that involve, for example, how data sets are aggregated and importance weights are applied. Generally, either equal weighting is assumed in calculating a final index, or subjective weights are specified. The same problem also occurs in the subjective assignment of countries to different clusters. Developed as such, the value of these type indices may be questioned by users. The aim of this paper is to explore methodological transparency as a viable solution to problems created by existing aggregated indices. For this purpose, a methodology composed of three steps is proposed. To start, a hierarchical clustering analysis is used to assign countries to appropriate clusters. In current methods, country clustering is generally based on GDP. However, we suggest that GDP alone is insufficient for purposes of country clustering. In the proposed methodology, 178 criteria are used for this purpose. Next, relationships between the criteria and classification of the countries are determined using artificial neural networks (ANNs). ANN provides an objective method for determining the attribute/criteria weights, which are, for the most part, subjectively specified in existing methods. Finally, in our third step, the countries of interest are ranked based on weights generated in the previous step. Beyond the ranking of countries, the proposed methodology can also be used to identify those attributes that a given country should focus on in order to improve its position relative to other countries, i.e., to transition from its current cluster to the next higher one

    Farm SMEs sustainability assessment based on Bellagio Principles. The case of Messinian Region, Greece

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    Purpose: Sufficient support of the sustainability of farm products embedded in a region (such as Products of Designated Origin / PDOs) to overcome significant obstacles to access domestic and remote markets. Main research question is how to overcome such inherent difficulties and transform them into challenges and opportunities to the new market environment. Design/methodology /approach: Combination of simplicity with the complicated issue of sustainability for awareness of small farmers SMEs and their collective representatives. Improve the understanding of the Sustainable Supply Chain Management (SSCM), to facilitate sustainability through use of the ‘Bellagio Principles’ for assessing sustainability of local farm products and facilitating further enhancement. Use of certain PDOs farm products of the Messinian region of Greece, such as local Sfela Feta cheese, olive oil, olives and raisins, to assess sustainability and improvement. Formation of a conceptual constructive action R&D framework of broader use in building-up and performing implementation of holistic supply chain strategy. Expected Findings: Providing better understanding of the SSCM. Insights on how SMEs co-operatives can collectively apply holistic strategies concerning local farm PDOs to fulfil competitiveness and sustainability requirements, under variant product and market conditions. Originality / Value : Improving the know-how, focusing on the sustainability of regional, traditional products and its effects upon supply chain performance and market access. Practical implications for regional-based farm SMEs in the design of holistic value creation strategies to produce sustainable competitive advantage. Interactive cause and effect dynamic implications of sustainable development on social, economic and physical environment

    Journal of Asian Finance, Economics and Business, v. 4, no. 1

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    Predicting Credit Default among Micro Borrowers in Ghana

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    Microfinance institutions play a major role in economic development in many developing countries. However many of these microfinance institutions are faced with the problem of default because of the non-formal nature of the business and individuals they lend money to. This study seeks to find the determinants of credit default in microfinance institutions. With data on 2631 successful loan applicants from a microfinance institution with braches all over the country we proposed a Binary logistic regression model to predict the probability of default. We found the following variables significant in determining default: Age, Gender, Marital Status, Income Level, Residential Status, Number of Dependents, Loan Amount, and Tenure. We also found default to be more among the younger generation and in males. We however found Loan Purpose not to be significant in determining credit default. Microfinance institutions could use this model to screen prospective loan applicants in order to reduce the level of default. Keywords: Microfinance, Loan Default, Default Prediction, Logistic Regressio

    Forecasting inflation with thick models and neural networks

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    This paper applies linear and neural network-based “thick” models for forecasting inflation based on Phillips–curve formulations in the USA, Japan and the euro area. Thick models represent “trimmed mean” forecasts from several neural network models. They outperform the best performing linear models for “real-time” and “bootstrap” forecasts for service indices for the euro area, and do well, sometimes better, for the more general consumer and producer price indices across a variety of countries. JEL Classification: C12, E31bootstrap, Neural Networks, Phillips Curves, real-time forecasting, Thick Models

    Data Warehouse Design and Management: Theory and Practice

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    The need to store data and information permanently, for their reuse in later stages, is a very relevant problem in the modern world and now affects a large number of people and economic agents. The storage and subsequent use of data can indeed be a valuable source for decision making or to increase commercial activity. The next step to data storage is the efïŹcient and effective use of information, particularly through the Business Intelligence, at whose base is just the implementation of a Data Warehouse. In the present paper we will analyze Data Warehouses with their theoretical models, and illustrate a practical implementation in a speciïŹc case study on a pharmaceutical distribution companyData warehouse, database, data model.

    CLASSIFICATION OF ENTREPRENEURIAL INTENTIONS BY NEURAL NETWORKS, DECISION TREES AND SUPPORT VECTOR MACHINES

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    Entrepreneurial intentions of students are important to recognize during the study in order to provide those students with educational background that will support such intentions and lead them to successful entrepreneurship after the study. The paper aims to develop a model that will classify students according to their entrepreneurial intentions by benchmarking three machine learning classifiers: neural networks, decision trees, and support vector machines. A survey was conducted at a Croatian university including a sample of students at the first year of study. Input variables described students’ demographics, importance of business objectives, perception of entrepreneurial carrier, and entrepreneurial predispositions. Due to a large dimension of input space, a feature selection method was used in the pre-processing stage. For comparison reasons, all tested models were validated on the same out-of-sample dataset, and a cross-validation procedure for testing generalization ability of the models was conducted. The models were compared according to its classification accuracy, as well according to input variable importance. The results show that although the best neural network model produced the highest average hit rate, the difference in performance is not statistically significant. All three models also extract similar set of features relevant for classifying students, which can be suggested to be taken into consideration by universities while designing their academic programs
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