Modelling and Trading the EUR / USD Exchange Rate : Do Neural Network Models Perform Better ?
نویسندگان
چکیده
This research examines and analyses the use of Neural Network Regression (NNR) models in foreign exchange (FX) forecasting and trading models. The NNR models are benchmarked against traditional forecasting techniques to ascertain their potential added value as a forecasting and quantitative trading tool. In addition to evaluating the various models using traditional forecasting accuracy measures, such as root mean squared errors, they are also assessed using financial criteria, such as risk-adjusted measures of return. Having constructed a synthetic EUR/USD series for the period up to 4 January 1999, the models were developed using the same in-sample data, leaving the remainder for out-of-sample forecasting, October 1994 to May 2000, and May 2000 to July 2001, respectively. The out-of-sample period results were tested in terms of forecasting accuracy, and in terms of trading performance via a simulated trading strategy. Transaction costs are also taken into account. It is concluded that NNR models do have the ability to forecast EUR/USD returns for the period investigated, and add value as a forecasting and quantitative trading tool. * Christian Dunis is Girobank Professor of Banking and Finance at Liverpool Business School and Director of CIBEF (E-mail: [email protected]). The opinions expressed herein are not those of Girobank. ** Mark Williams is an Associate Researcher with CIBEF (E-mail: [email protected]). *** CIBEF – Centre for International Banking, Economics and Finance, JMU, John Foster Building, 98 Mount Pleasant, Liverpool L3 5UZ.
منابع مشابه
Modeling and Trading the EUR/USD Exchange Rate Using Machine Learning Techniques
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 t...
متن کامل1 Applications of Advanced Regression Analysis for Trading and Investment ∗
This chapter examines and analyses the use of regression models in trading and investment with an application to foreign exchange (FX) forecasting and trading models. It is not intended as a general survey of all potential applications of regression methods to the field of quantitative trading and investment, as this would be well beyond the scope of a single chapter. For instance, time-varying...
متن کاملForecasting foreign exchange rates with
(2013) Forecasting foreign exchange rates with adaptive neural networks using radial basis functions and particle swarm optimization. The content must not be changed in any way or reproduced in any format or medium without the formal permission of the copyright holder(s) Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and particle swarm optimization...
متن کاملForecasting errors , directional accuracy and profitability of currency trading : The case of EUR / USD exchange rate ∗
We provide a comprehensive study of out-of-sample forecasts for the EUR/USD exchange rate based on multivariate macroeconomic models and forecast combinations. We use profit maximization measures based on directional accuracy and trading strategies in addition to standard loss minimization measures. When comparing predictive accuracy and profit measures, data snooping bias free tests are used. ...
متن کامل