Kalman Filters and Neural Networks in Forecasting and Trading

  • Georgios Sermpinis
  • Christian Dunis
  • Jason Laws
  • Charalampos Stasinakis
Part of the Communications in Computer and Information Science book series (CCIS, volume 311)

Abstract

The motivation of this paper is to investigate the use of a Neural Network (NN) architecture, the Psi Sigma Neural Network, when applied to the task of forecasting and trading the Euro/Dollar exchange rate 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 and two different NN architectures, a Multi-Layer Perceptron and a Recurrent Network. We combine our NN forecasts with Kalman Filter, a traditional Simple Average and the Granger- Ramanathan’s Regression Approach. 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. 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.

Keywords

Psi Sigma Network Recurrent Network Forecast Combinations Kalman Filter 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Georgios Sermpinis
    • 1
  • Christian Dunis
    • 2
  • Jason Laws
    • 3
  • Charalampos Stasinakis
    • 1
  1. 1.Business SchoolUniversity of GlasgowUK
  2. 2.Liverpool Business SchoolUK
  3. 3.Management SchoolUniversity of LiverpoolUK

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