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Agent based Stock Clustering for Efficient Portfolio Management

by Preeti Baser, Jatinderkumar R. Saini
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 116 - Number 3
Year of Publication: 2015
Authors: Preeti Baser, Jatinderkumar R. Saini
10.5120/20317-2381

Preeti Baser, Jatinderkumar R. Saini . Agent based Stock Clustering for Efficient Portfolio Management. International Journal of Computer Applications. 116, 3 ( April 2015), 35-41. DOI=10.5120/20317-2381

@article{ 10.5120/20317-2381,
author = { Preeti Baser, Jatinderkumar R. Saini },
title = { Agent based Stock Clustering for Efficient Portfolio Management },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 116 },
number = { 3 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 35-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume116/number3/20317-2381/ },
doi = { 10.5120/20317-2381 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:56:04.460986+05:30
%A Preeti Baser
%A Jatinderkumar R. Saini
%T Agent based Stock Clustering for Efficient Portfolio Management
%J International Journal of Computer Applications
%@ 0975-8887
%V 116
%N 3
%P 35-41
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This research paper proposed agent based framework for portfolio management using non-hierarchical clustering method. The framework included various agents such as data agent, clustering agent, ranking agent, portfolio manager and user agent. The data agent collected financial ratio of Nifty 50 companies from financial database. Clustering agents generated clusters and DB index computed to find optimum cluster size of each method. Validation agent evaluated the performance of k-means, k-medoids and fast k-means using intra-class inertia. Clusters generated by k-means used for investment and portfolio analysis using Markowitz model. This research helped to assemble a diversified portfolio of stocks with the use of clustering

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Index Terms

Computer Science
Information Sciences

Keywords

Clustering Data mining (DM) Davis-Bouldin (DB) Index Dunn Index k-means k-medoids Partitioning Around Medoids(PAM) Silhouette index

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