Agent based Stock Clustering for Efficient Portfolio Management

Abstract

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 Keywords Clustering, Data mining (DM), Davis-Bouldin (DB) Index, Dunn Index, k -means, k -medoids, Partitioning Around Medoids(PAM), Silhouette index 1. INTRODUCTION Data mining is a process of automatically discovering knowledge and predicting future trends from large financial markets. It creates opportunities for companies to make proactive and knowledge-driven decision in order to gain a competitive advantage. There are varieties of DM techniques available over past decades that include classification, similarity search, cluster analysis, association rule mining. Data mining techniques are also widely applied in number of financial areas, including predicting stock prices, predicting stock indices, portfolio management, portfolio risk management, trend detection, designing recommender [27, 28]. Portfolio management is one of major problem in financial domain. In today‟s competitive financial environment, an investor wants to earn maximum profit from his assets. An investor considers an investment in securities faces with the problem of choosing from among a large number of securities. He confuses in which security he has to invest. It depends upon the risk-return characteristics of individual securities. He selects most desirable securities and likes to allocate his funds over this group of securities. Again, he faces with the problem of deciding which securities to select and how much to invest in each. The investor chooses the optimal portfolio taking into consideration the risk and return characteristics of all possible portfolios. The research work describes about an agent based framework for portfolio management using non-hierarchical clustering methods. The proposed framework consist of various agents such as data agent, clustering agent, ranking agent, user agent and portfolio manager. This framework assists investors in strategic planning and investment decision-making. This research work can help to assemble a diversified portfolio of stocks with the help of clustering and also will help investor community in specific and in turn it helps the society and economy in general for better allocation of wealth. In this research paper,