Forecasting Stock Market Trend using Prototype Generation Classifiers

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F12%3A39895106" target="_blank" >RIV/00216275:25410/12:39895106 - isvavai.cz</a>

Result on the web

<a href="http://www.wseas.org/multimedia/journals/systems/2012/56-333.pdf" target="_blank" >http://www.wseas.org/multimedia/journals/systems/2012/56-333.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Forecasting Stock Market Trend using Prototype Generation Classifiers

Original language description

Currently, stock price forecasting is carried out using either time series prediction methods or trend classifiers. The trend classifiers are designed to predict the behaviour of stock price's movement. Recently, soft computing methods, like support vector machines, have shown promising results in the realization of this particular problem. In this paper, we apply several prototype generation classifiers to predict the trend of the NASDAQ Composite index. We demonstrate that prototype generation classifiers outperform support vector machines and neural networks considering the hit ratio of correctly predicted trend directions.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

IN - Informatics

OECD FORD branch

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Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2012

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

WSEAS Transactions on Systems

ISSN

1109-2777

e-ISSN

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Volume of the periodical

11

Issue of the periodical within the volume

12

Country of publishing house

GR - GREECE

Number of pages

10

Pages from-to

671-680

UT code for WoS article

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EID of the result in the Scopus database

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