Early Warning Systems (EWSs) for banking sectors are used to measure occurrence risks of banking crises, generally observed with a rundown of bank deposits and widespread failures of financial institutions. In countries with a small number of banks, for example Turkey with 48 banks (BDDK, 2011), every bank may be considered to have a systematic importance since the failure of any individual bank may carry a potential threat to lead to a banking crisis. Taking into account this fact the present study focuses on EWSs in Turkey. Since there is no single correct EWS to apply to all cases, in this study, 300 models were constructed and tested to find models as accurate as possible by using a trial-and-error process and by searching optimal feature subset or classifier methods. Empirical results indicate that prediction accuracy did not increase significantly while we got closer to the actual occurrence of bankruptcy. An important finding of the study was that trends of financial ratios were very useful in the prediction of bank failures. Instead of failures as a result of instant shocks, the banks&#039 / failures followed through a path: first a downward movement affected the efficiency of the banks&#039 / officers and the quality of management structure measured with &quot / Activity Ratios&quot / , then the profitability of the banks measured with &quot / Profit Ratios&quot / declined. At last, the performance and the stability of banks&#039 / earnings stream measured with &quot / Income-Expenditure Structure Ratios&quot / and the level and quality of the banks&#039 / capital base, the end line of defense, measured with &quot / Capital Ratios&quot / . At the end of study, we proposed an ensemble model which produced probability ratios for the success rates of the banks. The proposed model achieved a very high success rate for the banks we considered.

The aim of this study was developing an energy benchmarking method for urban quarters in order to design or manage the energy efficiently at urban scale. Rational implementation of energy efficiency strategies for the urban settlements can be possible with the comprehensive building stock models which have the ability to estimate and evaluate the energy demand of the building stock together with surrounding urban structures. The research initially focused to determine the factors which have an influence on the urban energy performance and rating, labeling, benchmarking systems used for evaluation of the energy performance. The field of study has narrow down to residential urban quarters which is approximately 70 % of the building stock. It has been considered surrounding attributes, building properties and occupant behavior. The results of the measurements from casestudy area were evaluated with regresional analysis in order to understand which factors have influence on energy performance. Artificial representative residential settlements were configured in that some of the factors were not possible to consider or evaluate with casestudy data. These representative residential settlements were evaluated with EnergyPlus simulation program and the results were compared with each other for forming a benchmarking method. The simulation results shows that density of the area and building envelope properties have significant effect on the urban energy performance. In order to use these benchmarking results, simulations were applied to the web tool. This urban energy benchmarking method provide the quantified energy performance of the different settlement types for urban designers and urban energy planners enabling them to estimate the urban energy requirement without socio economic context.