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Modeling and Trading the EUR/USD Exchange Rate Using Machine Learning Techniques
BibTeX
@MISC{Theofilatos_modelingand,
author = {Konstantinos Theofilatos and Spiros Likothanassis and Andreas Karathanasopoulos},
title = {Modeling and Trading the EUR/USD Exchange Rate Using Machine Learning Techniques},
year = {}
}
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Abstract
Abstract — The present paper aims in investigating the performance of state-of-the-art machine learning techniques in trading with the EUR/USD exchange rate at the ECB fixing. For this purpose, five supervised learning classification techniques (K-Nearest Neighbors algorithm, Naïve Bayesian Classifier, Artificial Neural Networks, Support Vector Machines and Random Forests) were applied in the problem of the one day ahead movement prediction of the EUR/USD exchange rate with only autoregressive terms as inputs. For comparison reasons, the performance of all machine learning techniques was benchmarked by two traditional techniques (Naïve Strategy and moving average convergence/divergence model). Trading strategies produced by the machine learning techniques of Support Vector Machines and Random Forests clearly outperformed all other strategies in terms of annualized return and sharp ratio. To the best of our knowledge, this is the first application of Random Forests in the problem of trading with the EUR/USD exchange rate providing extremely satisfactory results. Keywords- EUR/USD Exchange Rate; future direction prediction; Naïve strategy; MACD strategy; Naïve Bayesian Classifier; K-
Keyphrases
random forest eur usd exchange rate support vector machine average convergence divergence model sharp ratio present paper aim na ve strategy traditional technique future direction prediction ecb fixing first application state-of-the-art machine autoregressive term trading strategy satisfactory result comparison reason classification technique k-nearest neighbor algorithm keywords eur usd exchange rate na ve bayesian classifier movement prediction bayesian classifier artificial neural network macd strategy
