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Modeling, Forecasting and Trading the EUR Exchange Rates with Hybrid Rolling Genetic Algorithms -Support Vector Regression Forecast CombinationsGeorgios Sermpinis* Charalampos Stasinakis** Konstantinos Theofilatos*** Andreas Karathanasopoulos**** June 2015 AbstractThe motivation of this paper is to introduce a hybrid Rolling Genetic Algorithm -Support Vector Regression (RG-SVR) model for optimal parameter selection and feature subset combination. The algorithm is applied to the task of forecasting and trading the EUR/USD, EUR/GBP and EUR/JPY exchange rates. The proposed methodology genetically searches over a feature space (pool of individual forecasts) and then combines the optimal feature subsets (SVR forecast combinations) for each exchange rate. This is achieved by applying a fitness function specialized for financial purposes and adopting a sliding window approach. The individual forecasts are derived from several linear and non-linear models. RG-SVR is benchmarked against genetically and non-genetically optimized SVRs and SVMs models that are dominating the relevant literature, along with the robust ARBF-PSO neural network. The statistical and trading performance of all models is investigated during the period of 1999-2012. As it turns out, RG-SVR presents the best performance in terms of statistical accuracy and trading efficiency for all the exchange rates under study. This superiority confirms the success of the implemented fitness function and training procedure, while it validates the benefits of the proposed algorithm.

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.

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Operational risk is currently gaining increasing importance in Finance. Presently, practically all literature has been devoted to operational risk measurement and qualitative management. However, little literature has been devoted to quantifying the impact of operational risk on share prices, yet share value tends to be a key incentive to managing risks. Furthermore the impact of operational risks (e.g. management, internal processes and controls) tends to be a significant barrier to investment in emerging markets.In this paper, we quantify the impact on share prices from operational risk by applying the Single Index Model, which is one of the most recognised and popular models for share pricing in industry. Using monthly stock price data from major stock markets, we show that there exist variations in operational risk across different markets (developed versus emerging markets) as well as across different industry sectors.Our results support the idea that analysing and managing operational risk improves investment performance. In particular, operational risk accounts for significant differences between emerging markets and developed markets in a way that is consistent with current literature on firm specific risk and management in emerging markets. This paper will therefore be of interest to industry, emerging market investors and analysis of operational risks.