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Recently, the financial sector has made use of various alternative data, such as text information, supply chain network, shipping, and satellite images. Quantitative researchers and practitioners have explored new quantitative strategies using these data sources. In particular, numerous investigations have used the supply chain network data. Supply chain network data include supplier and customer firms as well as their transaction period and sales ratio, which is the percentage of customer relationships to the supplier’s total sales. For example, the FactSet Supply Chain Relationships database included approximately 210,000 relationships in December 2019, with about 18,000 supplier firms and 19,000 customer firms worldwide.
Early researchers used this supply chain network information. Cohen and Frazzini (2008) assumed that although the prices of customer firms affect their suppliers, investors are not aware of this impact because of information-processing limitations. They showed that customer firms’ stock returns affect those of supplier firms in the following month. Chen, Gao, and Zhang (2019) focused on similar effects for US suppliers and Chinese customer firms and showed high performance of the strategy based on supplier–customer relationships. A similar analysis that has demonstrated effectiveness has been conducted in the Japanese market (Hamuro and Okada 2018; Yoshino et al. 2020). In particular, Yoshino et al. (2020) suggested using second-layer customers for customer momentum; they showed that this is more effective than using first-layer customers. Furthermore, Paatela, Noschis, and Hameri (2017) showed a relationship between customer sales growth and supplier stock return. Dai, Ng, and Zaiats (2020) showed a relationship between investor behavior and customer/supplier firms news. Shahrur, Becker, and Rosenfeld (2010) focused on sector-level supplier–customer relationships of developed countries, except for the United States. They showed the effectiveness of the customer momentum strategy based on the supplier–customer relationship data from an input-output table.
In addition, previous research applied network theory to financial analysis. Ahern (2013) analyzed the relationship between network centrality and sector return based on the assumption that sectors with high centrality play an important role in the economy; the stock return of that sector is related to systematic risk. Furthermore, the author showed that the return of sectors with high centrality is high. Wu (2015) conducted the same analysis based on stock return and firm-level supply chain network data using the FactSet Supply...