Portfolio selection and unsystematic risk optimisation using swarm intelligence

Abstract

The financial crisis of 2008, got investment managers to study alternate methods of portfolio construction by focusing on optimisation of risk distribution. In this paper, we introduce a new method of risk diversification of an equity portfolio, based on the individual securities risk contribution. Markowitz’s portfolio optimisation methods were widely believed to diversify the risks until 2010, when it caught the financial market practitioners by surprise. But the scale of misjudgement raised doubts and opened the drawbacks of existing methods in portfolio construction and risk management. In this study, we propose a new technique for portfolio construction and unsystematic risk optimisation, using a well known meta heuristic from evolutionary computation, known as Grey Wolf Optimizer (GWO). We build a portfolio construction model using machine learning methods, to minimise risk contributors from our universe of securities, and then target to increase the asset diversification by maximising the maximum-diversification ratio. We apply boosting techniques to construct portfolio using ensemble of two well known techniques, known as maximum diversification ratio and risk budgeting of portfolio. We have benchmarked our findings by examining the returns of major global indices, and then illustrate our results by comparing with an alternate well known portfolio construction technique, the risk parity portfolio. For comparison, we have considered the length of investment horizon, returns and risks, as factors to present our findings by using the proposed portfolio optimisation and diversification method. Our technique offers practical insights and scope for further investigation into alternate methods of portfolio diversification and risk management using machine learning algorithms.