Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms -- support vector regression forecast combinations

DOI10.1016/J.EJOR.2015.06.052zbMath1347.91237OpenAlexW1504141014MaRDI QIDQ320100

Charalampos Stasinakis, Konstantinos Theofilatos, Andreas Karathanasopoulos, Georgios Sermpinis

Publication date: 6 October 2016

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2015.06.052

zbMATH Keywords

genetic algorithmsexchange ratesforecast combinationssupport vector regressionfeature subset

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Learning and adaptive systems in artificial intelligence (68T05) Financial applications of other theories (91G80)

Related Items (8)

Modelling, forecasting and trading with a new sliding window approach: the crack spread example ⋮ Personal income tax reforms: a genetic algorithm approach ⋮ Reverse adaptive krill herd locally weighted support vector regression for forecasting and trading exchange traded funds ⋮ European exchange trading funds trading with locally weighted support vector regression ⋮ Non-Linear Interactions and Exchange Rate Prediction: Empirical Evidence Using Support Vector Regression ⋮ A hybrid forecasting approach applied in the electrical power system based on data preprocessing, optimization and artificial intelligence algorithms ⋮ Forecasting government bond spreads with heuristic models: evidence from the eurozone periphery ⋮ Exchange rate forecasting using ensemble modeling for better policy implications

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