RPS: Portfolio asset selection using graph based representation learning

Portfolio optimization is one of the essential fields of focus in finance. There has been an increasing demand for novel computational methods in this area to compute portfolios with better returns and lower risks in recent years. We present a novel computational method called Representation Portfolio Selection by redefining the distance matrix of financial assets using Representation Learning and Clustering algorithms for portfolio selection to increase diversification. RPS proposes a heuristic for getting closer to the optimal subset of assets. Using empirical results in this paper, we demonstrate that widely used portfolio optimization algorithms, such as Mean-Variance Optimization, Critical Line Algorithm, and Hierarchical Risk Parity can benefit from our asset subset selection. •RPS for Portfolios: This article proposes RPS, a novel method using representation learning and clustering to improve portfolio diversification.•Optimal Asset Selection: RPS redefines asset distances, leading to a more optimal selection for portfolio construction.•Boosts Existing Algorithms: RPS works with established optimization methods like MVO, CLA, and HRP for potentially better results.