Higher order and recurrent neural architectures for trading the EUR/USD exchange rate

Quantitative Finance

Volumn 11, Issue 4, 2011, Pages 615-629

  Dunis, Christian L ^a   Laws, Jason ^a   Sermpinis, Georgios ^a  

^a LIVERPOOL JOHN MOORES UNIVERSITY   (United Kingdom)

Author keywords

Options volatility; Quantitative trading strategies; Risk management; Volatility modelling

Indexed keywords

EID: 79953179007     PISSN: 14697688     EISSN: 14697696     Source Type: Journal    
DOI: 10.1080/14697680903386348     Document Type: Article

References (36)

1. Adam, O., Zarader, L. and Milgram, M., Identification and prediction of non-linear models with recurrent neural networks. Laboratoire de Robotique de Paris, 1994.
2. Austin, M., Bates, G., Dempster, M., Leemans, V. and Williams, S., Adaptive systems for foreign exchange trading. Quant. Finan., 2004, 4, 37-45.
3. Connor, J. and Atlas, L., Recurrent neural networks and time series prediction, In Proceedings of the International Joint Conference on Neural Networks, 1993, pp. 301-306.
4. Dunis, C. and Huang, X., Forecasting and trading currency volatility: an application of recurrent neural regression and model combination. J. Forecast., 2002, 21, 317-354.
5. Dunis, C., Laws, J. and Evans, B., Trading futures spreads: an application of correlation and threshold filters. Appl. Financial Econ., 2006a, 16, 1-12.
6. Dunis, C., Laws, J. and Evans, B., Modelling and trading the gasoline crack spread: a non-linear story. Deriv. Use, Trad. Regul, 2006b, 12, 126-145.
7. Dunis, C., Laws, J. and Sermpinis, G., The robustness of neural networks for modelling and trading the EUR/USD exchange rate at the ECB fixing. J. Deriv. Hedge Funds, 2009, forthcoming.
8. Dunis, C. and Williams, M., Modelling and trading the EUR/USD exchange rate: do neural network models perform better? Deriv. Use, Trad. Regul., 2002, 8(3), 211-239.
9. Dunis, C. and Williams, M., Applications of advanced regression analysis for trading and investment. In Applied Quantitative Methods for Trading and Investment, edited by C. Dunis, J. Laws, and P. Naim, 2003 (Wiley: Chichester).
10. Elman, J.L., Finding structure in time. Cogn. Sci., 1990, 14, 179-211.
11. Fulcher, J., Zhang, M. and Xu, S., The application of higher-order neural networks to financial time series. Artificial Neural Networks in Finance and Manufacturing, 2006 (Idea Group: London).
12. th IASTED International Conference on Artificial Intelligence and Soft Computing, Palma de Mallorca, Spain, 2006, pp. 119-124.
13. Ghosh, J. and Shin, Y., Efficient higher-order neural networks for classification and function approximation. Int. J. Neural Syst., 1992, 3, 323-350.
14. Giles, L. and Maxwell, T., Learning, invariance and generalization in higher order neural networks. Appl. Optics, 1987, 26, 4972-4978.
15. Hryshko, A. and Downs, T., Development of machine learning software for high frequency trading in financial markets. Business Applications and Computational Intelligence, 2006 (IGI: Hershey).
16. Husmeier, D., Neural Networks for Conditional Probability Estimation - Forecasting Beyond Point Predictions (Perspectives in Neural Computing), 1999 (Springer: London).
17. Hussain, A., Ghazali, R. and Al-Jumeily, D., Dynamic ridge polynomial neural network for financial time series prediction, in IEEE International Conference on Innovation in Information Technology, IIT06, Dubai, 2006.
18. Jeng, D., Watada, J., Wu, B. and Wu, J., Fuzzy forecasting with DNA computing. DNA Computing, 2006 (Springer: Berlin).
19. Kaastra, I. and Boyd, M., Designing a neural network for forecasting financial and economic time series. Neurocomputing, 1996, 10, 215-236.
20. Kamijo, K. and Tanigawa, T., Stock price pattern recognition: a recurrent neural network approach, In Proceedings of the International Joint Conference on Neural Networks, 1990, pp. 1215-1221.
21. Karayiannis, N. and Venetsanopoulos, A., On the training and performance of high-order neural networks. Math. Biosci., 1994, 129, 143-168.
22. Knowles, A., Hussein, A., Deredy, W., Lisboa, P. and Dunis, C.L., Higher-order neural networks with Bayesian confidence measure for prediction of EUR/USD exchange rate. CIBEF Working Papers, 2005. Available online at: www.cibef.com.
23. Kosmatopoulos, E., Polycarpou, M., Christodoulou, M. and Ioannou, P., High-order neural network structures for identification of dynamical systems. IEEE Trans. Neural Netw., 1995, 6, 422-431.
24. Lindemann, A., Dunis, C. and Lisboa, P., Level estimation, classification and probability distribution architectures for trading the EUR/USD exchange rate. Neural Netw. Comput.Applic., 2004, 14, 256-271.
25. Lindemann, A., Dunis, C. and Lisboa, P., Probability distributions and leveraged trading strategies: an application of Gaussian mixture models to the Morgan Stanley technology index tracking fund. Quant. Finan., 2005, 5, 459-474.
26. Lisboa, P.J.G. and Vellido, A., Business applications of neural networks. In Business Applications of Neural Networks: The State-of-the-Art of Real-World Applications, edited by P.J.G. Lisboa, B. Edisbury, and A. Vellido, pp. 7-22, 2000 (World Scientific: Singapore).
27. Ozun, A., Using new information technologies for modeling data on global markets: an efficient interaction between 'artificial' human brain and economics, In Proceedings of the Conference on Human and Economic Resources, 2006, pp. 349-359.
28. Psaltis, D., Park, C. and Hong, J., Higher order associative memories and their optical implementations. Neural Netw., 1988, 1, 149-163.
29. Redding, N., Kowalczyk, A. and Downs, T., Constructive higher-order network algorithm that is polynomial time. Neural Netw., 1993, 6, 997-1010.
30. Shapiro, A.F., A hitchhiker's guide to the techniques of adaptive nonlinear models. Insurance, Math. Econ., 2000, 26, 119-132.
31. Shin, Y. and Ghosh, J., The Pi-Sigma network: an efficient higher-order neural network for pattern classification and function approximation, in Proceedings IJCNN, Seattle, 1991, pp. 13-18.
32. Tenti, P., Forecasting foreign exchange rates using recurrent neural networks. Appl. Artif. Intell., 1996, 10, 567-581.
33. Tino, P., Schittenkopf, C. and Doffner, G., Financial volatility trading using recurrent networks. IEEE Trans. Neural Netw., 2001, 12, 865-874.
34. Ullrich, C., Seese, D. and Chalup, S., Foreign Exchange Trading with Support Vector Machines, Advances in Data Analysis, 2007 (Springer: Berlin).
35. Vecci, L., Piazza, F. and Uncini, A., Learning and approximation capabilities of adaptive spline activation neural networks. Neural Netw., 1998, 11, 259-270.
36. Zhang, M., Xu, S.X. and Fulcher, J., Neuron-adaptive higher order neural-network models for automated financial data modelling. IEEE Trans. Neural Netw., 2002, 13, 188-204.