The nature of statistical learning theory
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Self-organizing maps
Outline for a Logical Theory of Adaptive Systems
Journal of the ACM (JACM)
Evolutionary Computation: The Fossil Record
Evolutionary Computation: The Fossil Record
Regression neural network for error correction in foreign exchange forecasting and trading
Computers and Operations Research
Accurate value-at-risk forecasting based on the normal-GARCH model
Computational Statistics & Data Analysis
Recurrent self-organising maps and local support vector machine models for exchange rate prediction
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Hybridizing data stream mining and technical indicators in automated trading systems
MDAI'11 Proceedings of the 8th international conference on Modeling decisions for artificial intelligence
International Journal of Intelligent Systems in Accounting and Finance Management
Cartesian genetic programming for trading: a preliminary investigation
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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This paper describes a hybrid model formed by a mixture of various regressive neural network models, such as temporal self-organising maps and support vector regressions, for modelling and prediction of foreign exchange rate time series. A selected set of influential trading indicators, including the moving average convergence/divergence and relative strength index, are also utilised in the proposed method. A genetic algorithm is applied to fuse all the information from the mixture regression models and the economical indicators. Experimental results and comparisons show that the proposed method outperforms the global modelling techniques such as generalised autoregressive conditional heteroscedasticity in terms of profit returns. A virtual trading system is built to examine the performance of the methods under study.