Forecasting and Trading the EUR/USD Exchange Rate with Stochastic Neural Network Combination and Time-Varying Leverage

Decision Support Systems, Volume 54, Issue 1, December 2012, pp. 316-329

Posted: 17 Apr 2013 Last revised: 21 Apr 2013

See all articles by Georgios Sermpinis

Georgios Sermpinis

University of Glasgow

Christian Dunis

John Moores University - Business School

Jason Laws

University of Liverpool - Accounting and Finance Division

Charalampos Stasinakis

University of Glasgow, Department of Economics

Date Written: May 21, 2012

Abstract

The motivation of this paper is to investigate the use of a Neural Network (NN) architecture, the Psi Sigma Neural Network (PSN), when applied to the task of forecasting and trading the Euro/Dollar (EUR/USD) exchange rate using the European Central Bank (ECB) fixing series and to explore the utility of Kalman Filters in combining NN forecasts. This is done by benchmarking the statistical and trading performance of PSN with a Naive Strategy, an Autoregressive Moving Average (ARMA) model and two different NN architectures, a Multi-Layer Perceptron (MLP) and a Recurrent Network (RNN). We combine our NN forecasts with Kalman Filter, a traditional Simple Average, the Bayesian Average, the Granger-Ramanathan's Regression Approach (GRR) and the Least Absolute Shrinkage and Selection Operator (LASSO). Finally, we apply a time-varying leverage strategy based on RiskMetrics volatility forecasts in order to further improve the forecasting performance of our models and combinations. The statistical and trading performance of our models is estimated throughout the period of 2002-2010, using the last two years for out-of-sample testing. In terms of our results, the PSN outperforms all models' individual performances in terms of statistical accuracy and trading performance. The forecast combinations also present improved empirical evidence, with Kalman Filters outperforming by far its benchmarks. We also note that after the application of the time varying leverage, all models except ARMA show a substantial increase in their trading performance.

Keywords: Psi Sigma network, Recurrent Network, Forecast combinations, Kalman Filter, LASSO, Leverage

Suggested Citation

Sermpinis, Georgios and Dunis, Christian and Laws, Jason and Stasinakis, Charalampos, Forecasting and Trading the EUR/USD Exchange Rate with Stochastic Neural Network Combination and Time-Varying Leverage (May 21, 2012). Decision Support Systems, Volume 54, Issue 1, December 2012, pp. 316-329, Available at SSRN: https://ssrn.com/abstract=2252023

Georgios Sermpinis

University of Glasgow ( email )

Adam Smith Business School
Glasgow, Scotland G12 8LE
United Kingdom

Christian Dunis

John Moores University - Business School ( email )

John Foster Building
98 Mount Pleasant
Liverpool, L3 5UZ
United Kingdom

Jason Laws

University of Liverpool - Accounting and Finance Division ( email )

United Kingdom

Charalampos Stasinakis (Contact Author)

University of Glasgow, Department of Economics ( email )

Adam Smith Business School
Glasgow, Scotland G12 8LE
United Kingdom

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