Propagating Momentum Information Through Global Supply Chain Networks

 | Sep 14, 2021 09:34

The traditional paradigm in the quantitative investment space has sought to employ factors derived from core fundamental, price, and estimate information for a given universe of issuers and securities. That model has since evolved to incorporate a broader range of alternative data sources as well as more robust ways of linking issuers to their external dependencies, which are often similarly rich in data.

Supply chain relationships data serves as a prime example of both phenomena due to the myriad of unique alpha factors that can be generated using various combinations of available data and economic linkages between companies. One of the original and often-cited studies in this area assumed that the collective performance momentum of a company’s customers is an indicator of that supplier’s future performance and that the time lag of this customer to supplier propagation stems from the finite information processing capacity of investors. Subsequent studies have observed similar customer-supplier momentum alpha with a particular focus on the U.S. market and short-term stock price propagation (e.g., one month).

In their research paper Momentum Information Propagation Through Global Supply Chain Networks, Yamamoto, Kawadai, and Miyahara extended some of the early studies in the following ways:

  • Expand the universe to the global market and supply chain network
  • Test the validity of using a network centrality as a weighting scheme in the customer momentum calculation
  • Examine customer momentum propagation over longer-term frequencies and use multiple tiers of supplier-customer relationships
h2 Customer Momentum/h2

In their research paper Economic Links and Predictable Returns, Cohen and Frazzini assumed that the return momentum of customers would propagate back to their suppliers. They proposed a new momentum factor, calculated as a weighted average of customers’ stock price returns for the past month. They used suppliers’ revenue exposure to each customer as a weighting scheme of the factor calculation.

h2 Empirical Analysis Universe and Time Horizon/h2

Cohen and Frazzini did an empirical analysis of their theory for the U.S. market. All supply chain information is sourced from public companies listed on U.S. stock exchanges . On the other hand, we conducted an empirical analysis for the constituents of a broad market index from 2003 to 2019 with monthly rebalancing. The supply chain relationship information is derived from FactSet’s supply chain relationships database, which sources more than 27,000 international public companies globally.

h2 Network Centrality as a Weighting Scheme/h2

Each customer has a different impact on the supplier’s performance. The early study used a revenue exposure of suppliers to its customer as a proxy of the impact of the momentum propagation. Revenue exposure seems like a logical proxy, although this may drastically limit our pool of relationships since those figures are not frequently disclosed to the public by suppliers. Due to a lack of information, some studies suggested using network centrality as a proxy.

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Centrality is a measure of the importance of nodes (i.e., companies) or edges (i.e., relationships) in the network. Using this measure as a weighting scheme assumes that the more important companies/relationships will have a greater impact on their suppliers' future performance. In this study, customer momentum uses edge betweenness centrality as a weighting scheme (i.e., the more important the relationship is, the more impact the customer will have on the supplier).

In order to examine the relationship between centrality and revenue exposure, all relationships are divided into five groups based on centrality. Then, the average revenue exposures are calculated for each group. The table below shows the average revenue exposures for each group in the test universe. As expected, Q1 (the highest centrality group) shows the highest average revenue exposure.