Abstract
As machine learning becomes more popular in all fields, its use is well known in finance and economics. The growing number of people using models to predict the market’s behaviour can modify the market itself so it is more predictable. In this context, the key element is to find out which variables are used to build the model in a macroeconomic environment. This paper presents an application of kernel methods to predict the EUR/USD relationship performing variable selection. The results show how after applying a proper variable selection, very accurate predictions can be achieved and smaller historical data is needed to train the model.
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Herrera, LJ. et al. (2015). Modeling the EUR/USD Index Using LS-SVM and Performing Variable Selection. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9095. Springer, Cham. https://doi.org/10.1007/978-3-319-19222-2_14
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DOI: https://doi.org/10.1007/978-3-319-19222-2_14
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