Implementation of iterative k-means-+ and ant colony optimization (ACO) in portfolio optimization problem
M. A. Rezani, Gatot Fatwanto Hertono, B. D. Handari · AIP conference proceedings · 2020
Portfolio optimization aims to provide investors with the highest returns and the least amount of risk. To that end, investors diversify to improve a portfolio’s effectiveness by reducing risks. In this research, we used the iterative k-means -+ algorithm as a clustering method and ant colony optimization (ACO). Clustering was used to diversify a portfolio based on the financial ratio of each stock. Iterative k-means -+ improves the solution obtained using k-means by removing 1 cluster (minus), dividing another cluster (plus) and re-clustering with each iteration. After clustering, some of the stocks are chosen and their weights are determined using a metaheuristic method, ant colony optimization (ACO). The numerical result of this method is evaluated with the data. This research yielded the results in which the performance of the iterative k-means -+ and ACO methods yields better returns and Sharpe ratios compared to those of the S&P 500 index data used.