This study evaluates the efficiency of peripheral European domestic banks and examines the effects of bankrisk determinants on their performance over 2007-2014. Data Envelopment Analysis is utilized on a Malmquist Productivity Index in order to calculate the bank efficiency scores. Next, a Double Bootstrapped Truncated Regression is applied to obtain bias-corrected scores and examine whether changes in the financial conditions affect differently banks' efficiency levels. The analysis accounts for the sovereign debt crisis period and for different levels of financial development in the countries under study. Such an application in the respective European banking setting is unique. The proposed method also copes with common misspecification problems observed in regression models based on efficiency scores. The results have important policy implications for the Euro area, as they indicate the existence of a periphery efficiency meta-frontier. Liquidity and credit risk are found to negatively affect banks productivity, whereas capital and profit risk have a positive impact on their performance. The crisis period is found to augment these effects, while bank-risk variables affect more banks' efficiency when lower levels of financial development are observed.

In this paper a hybrid Genetic Algorithm -Support Vector Regression (GA-SVR) model in economic forecasting and macroeconomic variable selection is introduced. The proposed algorithm is applied to the task of forecasting the US inflation and unemployment. The GA-SVR genetically optimizes the SVR parameters and adapts to the optimal feature subset from a feature space of potential inputs. The feature space includes a wide pool of macroeconomic variables that might affect the two series under study.

In this study a Krill Herd-Support Vector Regression (KH-vSVR) model is introduced. The Krill Herd (KH) algorithm is a novel metaheuristic optimization technique inspired by the behaviour of krill herds. The KH optimizes the SVR parameters by balancing the search between local and global optima. The proposed model is applied to the task of forecasting and trading three commodity Exchange Traded Funds (ETFs) on a daily basis over the period 2012-2014. The inputs of the KH-vSVR models are selected through the Model Confidence Set (MCS) from a large pool of linear predictors. The KH-vSVR's statistical and trading performance is benchmarked against traditionally adjusted SVR structures and the best linear predictor. In addition to a simple strategy, a time-varying leverage trading strategy is applied based on Heterogeneous Autoregressive (HAR) volatility estimations. It is shown that the KH-vSVR outperforms its counterparts in terms of statistical accuracy and trading efficiency, while the leverage strategy is found to be successful.