A Portfolio Optimization Algorithm Using Fuzzy Granularity Based Clustering

Abstract

Clustering algorithms are applied to numerous problems in multiple domains including historic data analysis, financial markets analysis for portfolio optimization and image processing. Recent years have witnessed a surge in use of nature inspired computing (NIC) techniques for data clustering to solve various real world optimization problems. Granular Computing (GC) is an emerging technique to handle pieces of information, known as information granules. In this paper, an ensemble of fuzzy clustering using Particle Swarm Optimization and Granular computing for stock market portfolio optimization. The model is then tested on stocks listed in Hong Kong Stock Exchange. Experimental results suggested that clusters formed through Fuzzy Particle Swarm Optimization (FPSO) with Granular computing are well suited and efficient for portfolio optimization. For comparison, we have used a benchmark index of Hong Kong Stock Exchange called as Hang Sang Composite Index (HSCI). Results proved that results of proposed approach are better in comparison to benchmark results of HSCI.