Return Momentum Strategy
By Alan Gigi and Martin Geissmann | October 1, 2020
This strategy is part of a series of strategies we will post based on the insightful research paper by Martin Geissmann and Eric McGill titled 'The Value of Timely Supply Chain Data'. This paper derived various trading signals based on the network connections between companies and found many alpha generating long-short strategies involving customer and supplier momentum, fundamental profitability factors, unlisted companies and complex interactions.
In return momentum strategies we look first at customers’ and then at suppliers’ past stock performance. The notion of looking at customers is straightforward: a company should be doing well if its customers are doing well (which should be reflected in an increase in everyone’s market valuation). One step up the supply chain, the suppliers of that one company should also be doing well.
The same mechanism for suppliers is a bit less intuitive (i.e. a company’s stock is doing well if its suppliers are doing well), but could reflect suppliers providing better goods and services to their customers that improve the customers’ productivity (or inversely dropping goods or services that require replacement). For it to work, the stock of a customer of a company should rise after the rise of the stock of its supplier. As reported in the following, we actually find patterns in both directions, yet less clear for the supplier side. This is reasonable because suppliers changing their offerings is less frequent than customers simply purchasing more or less of the same offerings.
The graph below shows the performance of a return momentum strategy with a return lag of one month on all direct customers. The strategy delivers a SR of 1.07, and a significant CAPM alpha of 6.7% (annualised). Positive returns could also be found for the six and twelve months lagged direct customer momentum.
Momentum strategies on direct suppliers could also be identified to have delivered positive (yet weaker) returns. While their CAPM alpha was mostly insignificant, the loading on the Fama French and momentum factor was small and insignificant, which shows that their performance is not driven by systematic market momentum. The same observation could also be made for the momentum strategies on the customer side, and implies that neither is fully incorporated into the stock market.
Looking at second degree customers, we also find the L/S strategy to have delivered positive abnormal returns, see graph below. The strategies on all momentum lags resulted in positively performing L/S portfolios, with the six month lagged signal on the second degree customers performance delivering the highest SR of 0.92.
The momentum strategy on first degree customers performed best with the shortest lag (1 month) but declined significantly with longer lags. The same momentum strategy on second degree customers delivered better results with a six months lag. This supports the argument of delayed spread of information across the network. On the company itself, information is quickly priced in, whereas it takes some time to spread to other companies up the supply chain as analysts slowly incorporate the information.
Also as expected, the less intuitive and less frequent supplier side momentum performance is indeed lower. A company’s value does not seem to be influenced much by an idiosyncratic move in its suppliers’ stock as opposed to its customers’ stock.
One possible explanation for our signals’ performance is that smaller capitalisation stocks are generally covered by fewer analysts. Therefore, one might expect that those stocks exhibit higher market inefficiencies and would be more profitable in a supply chain momentum strategy. To evaluate such patterns, we form conditional double-sorted portfolios, i.e. first sorting the equities according to their market capitalisation (where we use three quantiles low/medium/high), and second sorting for the momentum signal as before.
Contrary to the hypothesis, we do not find better performance with small-caps. This indicates that profound knowledge on the supply chain is relevant for all company sizes, small to large. For example, the large cap (top 1/3) subset shows a clear pattern in quantile portfolio returns according to the second degree customers (see graph below). We reason that while large caps are more covered by analysts, those do not necessarily take into account the elevated complexity of those companies’ supply chain connections. This bodes well for the long-term success of a supply chain trading strategy.