A Framework to Perform Asset Allocation Based on Partitional Clustering

Over the past years, many approaches to perform asset allocation have been proposed in the literature. Most of them tackle this problem as an optimization task, where the goal is to maximize return, whilst minimizing the risk. However, such approaches require the inversion of a positive-definite covariance matrix, usually resulting in the concentration of allocation, instability and low performance. Some methods have been recently introduced to solve this problem by facing it as a clustering problem. This paper introduces a framework for asset allocation based on partitional clustering algorithms. The idea is to segment the assets into clusters of correlated assets, allocate resources for each cluster and then within each cluster. The framework allows the use of different partitional clustering algorithms, intragroup and intergroup allocation methods. Also, various assessment criteria are considered, and a specialized initialization method is proposed for the clustering algorithm. The framework is evaluated with the Brazilian Stock Exchange (B3) data from the period 12/2005 to 04/2020. Different initialization methods are used for the clustering algorithm together with two intergroup and two intragroup techniques, resulting in five experimental scenarios. The results are compared with the Ibovespa index, the mean-variance model of Markowitz, and the risk-parity model recently proposed by López de Prado.