Mean-value at risk portfolio selection problem using clustering technique: A case study

Abstract

Each financial investment refers to a highly volatile environment in the global market, thus adding uncertainties in the financial market makes an optimal portfolio selection problem a major disadvantage in the market scenario. In this paper, we present an integrated approach to a portfolio selection problem using clustering technique. A classification of historical stock data from the Sensex Bombay Stock Exchange into a cluster is presented using the K-Mean technique. Also, the Mean-Value-at-Risk model is used to select the optimum portfolio using non-convex programming problems. Finally, the analytical findings are supported by a case study.