Kalman filters and neural networks in forecasting and trading

Sermpinis, G. , Dunis, C., Laws, J. and Stasinakis, C. (2012) Kalman filters and neural networks in forecasting and trading. In: Jayne, C., Yue, S. and Iliadis, L. (eds.) Engineering Applications of Neural Networks, 13th International Conference EANN 2012 Proceedings, EANN 2012, CCIS 311. Series: Communications in Computer and Information Science (311). Springer Berlin Heidelberg: Berlin Heidelberg, pp. 433-442. ISBN 9783642329081 (doi:10.1007/978-3-642-32909-8_44)

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Publisher's URL: http://dx.doi.org/10.1007/978-3-642-32909-8_44


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.

Item Type:Book Sections
Glasgow Author(s) Enlighten ID:Laws, Mr Jason and Stasinakis, Dr Charalampos and Sermpinis, Professor Georgios
Authors: Sermpinis, G., Dunis, C., Laws, J., and Stasinakis, C.
College/School:College of Social Sciences > Adam Smith Business School > Economics
College of Social Sciences > Adam Smith Business School > Accounting and Finance
Publisher:Springer Berlin Heidelberg

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