A novel approach to portfolio selection using news volume and sentiment

Abstract

In this study, we develop a novel approach to portfolio diversification by integrating information on news volume and sentiment with the k-nearest neighbors (kNN) algorithm. Our empirical analysis indicates that high news volume contributes to portfolio risk, whereas news sentiment contributes to portfolio return. Based on these findings, we propose a kNN algorithm for portfolio selection. Our in-sample and out-of-sample tests suggest that the proposed kNN portfolio selection approach outperforms the benchmark index portfolio. Overall, we show that incorporating news volume and sentiment into portfolio selection can enhance portfolio performance by improving returns and reducing risk.