Link Interaction Factors Strategy

By Alan Gigi and Martin Geissmann | October 26, 2020


This strategy revolves around processing the complex network of customer-supplier and use network statistics to provide a systems view of a company’s importance within the overall economy.

A more complex strategy using interaction factors is derived from the following two key steps:
  1. We calculated the change in the number of customers and suppliers, including changes in unlisted companies such as government or near government entities. Why? To accommodate customers that are less exposed to the volatility of the market and are willing to overpay above market prices.
  2. We looked at the eigenvector centrality measure, representing the importance of a company in the global economy and it most closely follows the flow of money in the economy. This allows us to analyze performance on multiple tiers of the supply chain.


We initially used the mere number of first degrees inward, outward, or total, as signals, but the result in a L/S strategy was inconclusive. While similar to Zhao et al. (2015), some degree ratios (e.g. number of degrees divided by market capitalization) deliver better results, but such signals are seemingly heavily synthesized (data dredging) and don’t reflect an underlying base truth.

In contrast, using changes in the number of degrees give better results. As shown in Table 1, we get positive returns (yet comparably small SR) for the change in number of outward degrees. That is to say, those companies that recently reported additional outward connections (i.e. new customers) outperformed, which makes sense.

We also report the change in unlisted degrees. Those are trading partners that are not listed on a stock exchange, which includes private companies but also several government or government- near entities (US Government, Department of Defense, United Nations etc.). This is motivated by the idea that such customers tend to be less prone to economic cycles and might tend to pay above market prices as they focus on a longer-term horizon. We observe that an increase in connections with unlisted companies resulted in a better performance (SR above 0.7 for all inward/outward/total), which clearly reflects the value of gathering data beyond the standard filings.

Table 1: Sharpe ratio of fundamental flow strategies

Sharpe ratio of fundamental flow strategies

To incorporate graph characteristics beyond the first degree, we have to look at centrality measures. Centrality gives a score of importance to each node in a network. While several approaches to measuring the centrality have been proposed, we chose the eigenvector centrality as it most closely follows the path of money flowing through the economy.

As expected, we find better performances in companies with higher centrality scores. More interconnected companies on average outperform on average. Interestingly, we do not observe a significant loading of the L/S portfolio’s return series on the the Fama/French’s SMB factor (nor on their other factors), indicating that the centrality incorporates further characteristics beyond a company just being small or large. Since complex network analytics are difficult to intuitively process, this is a logical finding.

We also find value in the change of the centrality score. Companies whose centrality has recently increased tend to outperform. The L/S strategy delivers a SR of 1.11, see Table 2.

Table 2: Sharpe ratio of centrality strategies

Sharpe ratio of centrality strategies
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