About: Forecasting FTSE Bursa Malaysia KLCI Trend with Hybrid Particle Swarm Optimization and Support Vector Machine Technique

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	Stock trend forecasting is one of the important issues in stock market research. However, forecasting stock trend remains a challenge because of its irregular characteristic in the stock indices distribution, which changes over time. Support Vector Machine (SVM) produces a fairly good result in stock trend forecasting, but the performance of SVM can be affected by the high dimensional input features and noisy data. This paper hybridizes the Particle Swarm Optimization (PSO) algorithm to generate the optimum features set prior to facilitate SVM learning. The SVM algorithm uses the Radial Basis Function (RBF) kernel function and optimization of the gamma and large margin parameters are done using the PSO algorithm. The proposed algorithm was tested on a pre-sampled 17 years record of daily Kuala Lumpur Composite Index (KLCI) data. The PSOSVM approach is applied to eliminate unnecessary or insignificant features, and effectively determine the parameter values, in turn improving the overall prediction results. The optimized feature space of technical indicators of the algorithm is proven by the experimental results showing that PSOSVM has outperformed SVM technique significantly. Stock trend forecasting is one of the important issues in stock market research. However, forecasting stock trend remains a challenge because of its irregular characteristic in the stock indices distribution, which changes over time. Support Vector Machine (SVM) produces a fairly good result in stock trend forecasting, but the performance of SVM can be affected by the high dimensional input features and noisy data. This paper hybridizes the Particle Swarm Optimization (PSO) algorithm to generate the optimum features set prior to facilitate SVM learning. The SVM algorithm uses the Radial Basis Function (RBF) kernel function and optimization of the gamma and large margin parameters are done using the PSO algorithm. The proposed algorithm was tested on a pre-sampled 17 years record of daily Kuala Lumpur Composite Index (KLCI) data. The PSOSVM approach is applied to eliminate unnecessary or insignificant features, and effectively determine the parameter values, in turn improving the overall prediction results. The optimized feature space of technical indicators of the algorithm is proven by the experimental results showing that PSOSVM has outperformed SVM technique significantly. (en)
Title	Forecasting FTSE Bursa Malaysia KLCI Trend with Hybrid Particle Swarm Optimization and Support Vector Machine Technique Forecasting FTSE Bursa Malaysia KLCI Trend with Hybrid Particle Swarm Optimization and Support Vector Machine Technique (en)
skos:prefLabel	Forecasting FTSE Bursa Malaysia KLCI Trend with Hybrid Particle Swarm Optimization and Support Vector Machine Technique Forecasting FTSE Bursa Malaysia KLCI Trend with Hybrid Particle Swarm Optimization and Support Vector Machine Technique (en)
skos:notation	RIV/61989100:27740/13:86089370!RIV14-MSM-27740___
http://linked.open...avai/predkladatel	IT4Innovations
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(ED1.1.00/02.0070)
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Abraham Padath, Ajith
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Vysoká škola báňská - Technická univerzita Ostrava / IT4Innovations
http://linked.open...dnocenehoVysledku	75427
http://linked.open...ai/riv/idVysledku	RIV/61989100:27740/13:86089370
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Support vector machine; Stock trend forecasting; RBF kernal function; Particle swarm optimization (en)
http://linked.open.../riv/klicoveSlovo	Support vector machine RBF kernal function Stock trend forecasting Particle swarm optimization
http://linked.open...ontrolniKodProRIV	[EDB78A847B1E]
http://linked.open...v/mistoKonaniAkce	Fargo
http://linked.open...i/riv/mistoVydani	New York
http://linked.open...i/riv/nazevZdroje	2013 World Congress on Nature and Biologically Inspired Computing, NaBIC 2013
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	4 (xsd:int)
http://linked.open...vavai/riv/projekt	IT4Innovations Centre of Excellence
http://linked.open...UplatneniVysledku	2013
http://linked.open...iv/tvurceVysledku	Abraham Padath, Ajith Muda, A. K. Choo, Y. H. Zhen, Z.L.
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2013-08-12 (xsd:date)
number of pages	6 (xsd:int)
http://bibframe.org/vocab/doi	10.1109/NaBIC.2013.6617856
http://purl.org/ne...btex#hasPublisher	Elsevier
https://schema.org/isbn	978-1-4799-1415-9
http://localhost/t...ganizacniJednotka	27740

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