Modeling and Trading the EUR/USD Exchange Rate Using Machine Learning Techniques

Journal Title: Engineering, Technology & Applied Science Research - Year 2012, Vol 2, Issue 5

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.

Authors and Affiliations

K. Theofilatos, S. Likothanassis, A. Karathanasopoulos

Keywords

Related Articles

A Survey and Analysis of the Relationship between Human Resources Management and Organizational Performance

This study aims to evaluate the relationship between human resources management and organizational performance with emphasis on the mediating role of organizational innovation. The study methodology is descriptive-correl...

Fat Quantitation in Liver Biopsies Using a Pretrained Classification Based System

Non-Alcoholic Fatty Liver Disease (NAFLD) is a common syndrome that mainly leads to fat accumulation in liver and steatohepatitis. It is targeted as a severe medical condition ranging from 20% to 40% in adult populations...

An Investigation on the Ultimate Strength of Cold-Formed Steel Bolted Connections

This paper presents experimental results and finite element analysis of the cold-formed steel bolted connection under shear loading. Experiments are conducted to study the ultimate behaviors, such as ultimate strength an...

Developing an Algorithm to Consider Mutliple Demand Response Objectives

Due to technological improvement and changing environment, energy grids face various challenges, which, for example, deal with integrating new appliances such as electric vehicles and photovoltaic. Managing such grids ha...

A Study of the Effective Lifetime of Aluminum Buckets Used in Blood Bank Centrifuges

Rotating parts of blood bank centrifuges are under heavy mechanical cyclic stresses due to their centrifugal loading conditions. Estimating the effective lifetime for these parts is very important for their application....

Download PDF file
  • EP ID EP88709
  • DOI -
  • Views 322
  • Downloads 0

How To Cite

K. Theofilatos, S. Likothanassis, A. Karathanasopoulos (2012). Modeling and Trading the EUR/USD Exchange Rate Using Machine Learning Techniques. Engineering, Technology & Applied Science Research, 2(5), -. https://europub.co.uk/articles/-A-88709