Clustering of large number of stock market trading rules

Title:

Clustering of large number of stock market trading rules

Creator:

Lipinski , Piotr

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:b1116a19-89b2-43ad-a692-37905dbf9016
uuid:b1116a19-89b2-43ad-a692-37905dbf9016

Subject:

Clustering, self-organizing maps, data mining, stock market financial time series, and stock market trading rules

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

This paper addresses the problem of clustering in large sets discussed in the context of financial time series. The goal is to divide stock market trading rules into several classes so that all the trading rules within the same class lead to similar trading decisions in the same stock market conditions. It is achieved using Kohonen self-organizing maps and the K-means algorithm. Several validity indices are used to validate and assess the clustering. Experiments were carried out on 350 stock market trading rules observed over a period of 1300 time instants.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2005 Volume:15 | Number:4

Harvested from:

CDK

Metadata only:

false