A Network View of Portfolio Optimization Using Fundamental Information - Yan , Xiangzhen; Yang , Hanchao; Yu , Zhongyuan; Zhang , Shuguang (Frontiers in Physics, 2021)
This article proposes the use of a novel approach to portfolio optimization, referred to as "Fundamental Networks" (FN). FN is an effective and robust network-based fundamental-incorporated method, and can be served as an alternative to classical mean-variance framework models. As a proxy for a portfolio, a fundamental network is defined as a set of "interconnected" stocks, among which linkages are a measure of similarity of fundamental information and are referred to asset allocation directly. Two empirical models are provided in this paper as applications of Fundamental Networks. We find that Fundamental Networks efficient portfolios are in general more mean-variance efficient in out-of-sample performance than Markwotiz's efficient portfolios. Specifically, portfolios set for profitability goals create excess return in a general/upward trending market; portfolios targeted for operating profitability perform better in a downward trending market, and can be considered as a defensive strategy in the event of a crisis.