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Despite the turmoil in the global financial sector over the years, Asian banking stood still with many positive characteristics - stable, balanced competition, good governance, and profitable – becoming a fertile ground for performance studies. This paper departs from previous banking performance studies in Asia by proposing a novel stochastic Multi-Criteria Decision-Making (MCDM) based on sign decomposition, offering a novel perspective in five analytical fronts. First, criteria decorrelation and filtering from country and time impacts as an initial data treatment. Second, endogenous computation of bank criteria importance for the alternatives comprised by each bank and year under analysis, in a "one-stop-shop" approach. Third, derivation of partial performance scores for each criterion under each alternative, besides traditional overall performance scores for each alternative. Fourth, mapping the exogenous/endogenous relationships among bank filtered criteria from country and year impacts using transfer entropy. Fifth, prediction of the overall performance drivers in terms of country-related contextual variables using neural networks. Our findings indicate that bank liquidity, bank capitalization, net income as well as the ability to generate profit for the shareholders are significantly related to bank performance. In addition, we find that accounting standards have a significant impact on bank performance. Although inflation is found to be positively related to bank performance, the results show that bank performance is helpful to promote people's economic standard and life expectancy. Our study provides important implications to macroeconomic management to balance the development of macroeconomic development and bank performance. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, BOT approach has provided an increasingly popular project financing to move toward infrastructure development in Asian countries such as Iran. There are many complexities in projects because of the variety of factors in project’s trend and also the dependence of project on mainly national factors. Due to these complexities and their long-term operating, the projects meet with uncertainty and numerous risks. In this paper, an attempt is made at identifying common risks in BOT projects. A novel hierarchical structure of risks is presented on the basis of the project-oriented point of view; next, some effective criteria for risk ranking in BOT projects are introduced. Then, the problem is defined in Fuzzy Multi Attribute Decision Making (FMADM) field. Therefore, Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) and Fuzzy Linear Programming Technique for Multidimensional Analysis of Preference (FLINMAP) methods are presented in order to rank high risks in BOT projects. In this study, these two methods are compared in four respects: separation among alternatives, fuzzy error in criteria’s weights, risk response planning, and numerousness of alternatives in proportion to criteria. The proposed model is used for identifying and assessing risks in Iran BOT power plant project. Finally, the rankings of high risks are determined according to their results at the end of this paper. [Copyright &y& Elsevier]

In this paper, a type-2 fuzzy rule based expert system is developed for stock price analysis. Interval type-2 fuzzy logic system permits us to model rule uncertainties and every membership value of an element is interval itself. The proposed type-2 fuzzy model applies the technical and fundamental indexes as the input variables. This model is tested on stock price prediction of an automotive manufactory in Asia. Through the intensive experimental tests, the model has successfully forecasted the price variation for stocks from different sectors. The results are very encouraging and can be implemented in a real-time trading system for stock price prediction during the trading period. [Copyright &y& Elsevier]