Comparing bio-inspired heuristic algorithm for the mean-CVaR portfolio optimization

Abstract

Risk aversion parameter is a coefficient that denotes the trade-off between the risk and the return in an optimal investment. This coefficient had widely used to modify the mean-variance portfolio optimization procedure. In this study, we develop become a mean-CVaR optimization problem with risk aversion. We investigate the usage of several biological-based heuristic algorithms such as genetic algorithm, grasshopper optimization, firefly optimization, moth flame optimization, particle swarm optimization, grey-wolf optimization, and dragonfly optimization to solve this portfolio optimization procedure. Empirical study with Indonesian Stock data show that the Grey-Wolf Optimization yields better performance than the others.