Companies do not exist in isolation but are connected through their supply chain relationships. Treating companies as nodes and their supply chain relationships as directed edges, we see supply chain networks in which goods, value, and information are transmitted from one node to another. Although the concept seems new, the supply chain network has long been a natural ecosystem that a company lives in, depends on, and evolves within. Traditionally, analysis around a company's financial performance has been primarily focused on the company's fundamentals, without much attention given to the supply chain network. For investment managers, the supply chain should be a natural extension of traditional fundamental research.
However, supply chain data is difficult to harness. Firstly, information is often held within the secret confines of a company in order to protect themselves from their competitors, as revealing such information can potentially disrupt the supply chain ecosystem. Therefore, the supply chain network data coverage is moderate even while using the best available data source. Secondly, most information available on a company's supply chain is due to its own voluntary disclosure, which may be biased toward large companies and reputable supply chain partners.
In this report, we leverage a unique dataset that provides over ten years of history on supply chain relationships – the FactSet Revere data, and explore predictive signals using information about a company's upstream suppliers and downstream customers including stock returns, fundamentals, and number of linkages. The results are encouraging as we find that the performance of a company's customers and suppliers is predictive of its own stock returns. We further show that major events from supply chain partners have impacts on a company's stock returns as well.
We argue that the predictive relationships in the supply chain may be long-term sources of alphas. The supply chain data is proprietary, complex, and difficult to analyze, and as such it may take the market time to digest and react appropriately. Second, the lag time effect may not only be due to investors' inattention, but also due to the slow diffusion of companies' operational information to their supply chain partners. Our results are robust as they show that the recent dissimilation of supply chain data has not arbitraged away the performance of such strategies.
We further delve into the multiplicity of supply chains and leverage graph theory such as Google's PageRank algorithm to study the network implications of the supply chain. We find potential alpha opportunities by identifying companies with important network position.