Download e-book for iPad: Investment Strategies Optimization based on a SAX-GA by António M.L. Canelas, Rui F.M.F. Neves, Nuno C.G. Horta

By António M.L. Canelas, Rui F.M.F. Neves, Nuno C.G. Horta

ISBN-10: 3642331092

ISBN-13: 9783642331091

ISBN-10: 3642331106

ISBN-13: 9783642331107

This booklet offers a brand new computational finance technique combining a Symbolic combination approximation (SAX) procedure with an optimization kernel in response to genetic algorithms (GA). whereas the SAX illustration is used to explain the monetary time sequence, the evolutionary optimization kernel is utilized in order to spot the main proper styles and generate funding principles. The proposed strategy considers numerous diverse chromosomes buildings which will in attaining higher effects at the buying and selling platform The technique awarded during this ebook has nice power on funding markets.

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Additional info for Investment Strategies Optimization based on a SAX-GA Methodology

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23 is possible to verify that two windows are defined, in the first one the algorithm decides to invest and in the second stays out of the market. To better understand the decision making by the algorithm, both windows will be examined in detail next. 23, shows the ROI in the dot/green line related to the right graphic axes. 50% 1 18 35 Quote Window 52 SAX-GA Fig. 23 S&P500 stock quote and SAX-GA investment return Fig. 5 -2 Fig. 5 -2 Like was previously presented, the first step of the SAX method is to normalize the data, in Figs.

Cybern. 1, 116–119 (2011). 34 21. J. H. Tay, Support vector machine with adaptive parameters in financial time series forecasting. IEEE Trans. Neural Netw. 14(6), 1506–1518 (2003). 820556 22. D. Zhang, H. Song, P. Chen, Stock market forecasting model based on a hybrid ARMA and support vector machines. Proceedings of the 15th International Conference on Management Science and Engineering, Long Beach, USA (2008) 23. J. Lin, E. Keogh, S. Lonardi, B. Chiu, A symbolic representation of time series, with implications for streaming algorithms.

SOM is based on competitive learning where the only one of the output nodes is activated. The winner node will then see their weights adjusted this will cause that this node becomes specialized on the kind of patterns that cause the activation. The SOM structure used is presented in Fig. 27. 2 Existing Solutions 23 Fig. 28 Bull flag matrix pattern template The authors identified two major problems; the first was the efficiency of the discovery process, the increase of the number of data points in the pattern lead to an exponential increase of the pattern discovery.

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Investment Strategies Optimization based on a SAX-GA Methodology by António M.L. Canelas, Rui F.M.F. Neves, Nuno C.G. Horta

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