Application of Swarm Intelligence to Portfolio Optimisation

摘要

Portfolio optimisation is a thoroughly research domain, and with developments in asset allocation objective functions, current optimisation methods are proving insufficient. This study compares the performance and convergence time of different swarm-intelligence based methods for 2 types of objective functions, each consisting of 12, 24, 48 and 96 stocks each. The objective functions are Sharpe ratio maximisation and maximization of Value at Risk weighted return of the portfolio. Cuckoo search, Firefly, Micro-Bat Echolocation, Elephant Herd, Harmony Search, Flower Pollination, Differential Evolution and Particle Swarm Optimisation algorithms are used for optimisation and analysis. The performance of each algorithm is measured by the resultant return, variance measured risk, sharpe ratio and time per iteration. The portfolios are sampled from and the return and risk is calibrated from Nifty100 historical price data.