The Bond–Equity–Fund Relation Using the Fama–French–Carhart Factors: A Practical Network Approach

Abstract

The main goal of this article is to show the relation between global equity and bond funds from a network perspective. The authors demonstrate the advantages of graph theory to explain the collective fund dynamics. The results show that equity and bond funds have a significant exposure to the Fama–French–Carhart factors. The authors argue that the network is dynamically driven by equity funds with their centrality scores and risk factor exposure and can transmit and amplify system-wide stress or inefficiencies in the factor bets. Using graph theory, the authors demonstrate that the return-based relationships between bond and equity funds are asymmetrical and the network is sufficiently clustered. Specifically, equity funds connect the different clusters. The HML factor is significant both on a single-fund level and as a web determinant. Therefore, investors should pay close attention to it when managing funds and deriving asset allocations. Finally, the authors provide a machine learning approach to how fund managers, plan sponsors, and analysts can derive equity–bond allocations, based on centrality scores, factor exposure, and hierarchical clustering of asymmetrically connected assets. TOPICS:Equity portfolio management, statistical methods, simulations, big data/machine learning Key Findings • The authors use topology and a concept from physics to show strong–weak return- and factor-based relationships between global equity and bond funds using a directed network approach. • The Fama–French–Carhart factors determine an asymmetrical bond–equity fund relation and different fund clusters. The network exhibits a structure that differs according to the market cycle. Equity funds play the most central role in the market and connect the different clusters. • Investors can use the bond–equity interconnectedness and network topology in multiple ways. The authors show that simple passive allocations, fund-of-funds solutions, and cluster-wise control for risk factors are some of the possible applications.