C.A. Zapart (UK)
Artificial Intelligence Applications, Genetic Algorithms, Neural Networks, Optimization, Time Series Analysis, Wavelets
The paper presents results of simulated long-short equity pairs trading with optimised static and dynamic wavelet correlation models. The models trade using a set of twenty five major capitalisation stocks from the sector "Materials". Wavelet correlation models are optimised with help of arti ficial neural networks and genetic algorithms. Trading sim ulations show that models are able to make consistent prof its during rising and falling markets even when a typical established long-short arbitrage strategy remains flat.
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