We all see what happens to the companies that supply Apple with components the moment Apple’s newest product is opened up and it’s guts are cataloged, delight for those small firms included and disaster for the firms that have been passed by. Cohen and Frazzini dig deeper, beyond the obvious linkages you here about day in and day out (auto suppliers, tech suppliers) and capture a broad range of customer-supplier linkages. As with the research on risk sentiment in annual filings this is a fairly intuitive idea; companies that are linked financially are likely linked in their stock returns as well.
They use 11,484 customer-supplier relationships where the customer makes up more than 10% of the suppliers total sales, dated from 1980 t0 2004. They predict that a large shock to the customer’s stock price will take time to be reflected in the supplier’s price, thus creating a trading opportunity in the supplier’s stock. In their paper, the construct a long-short strategy that took long positions in 20% of customer stocks with the highest returns in month t-1, and short positions in the 20% of customer stocks with the lowest returns in t-1. They find
The customer momentum strategy that is long the top 20% good customer news stocks and short the bottom 20% bad customer news stocks delivers Fama and French (1993) abnormal returns of 1.45% per month ( t-statistic = 3.61), or approximately 18.4% per year.
At a previous firm we did the work to collect the customer-supplier relationship data, which was maddening and slow work. We found our backtest results were similar to Cohen and Frazzini’s but the effect did not work so well with larger supplier companies (the relationships were already known we surmised) and in certain sectors like financials.
It seems like a relatively straightforward idea that statements by a firms management would contain information outside of what the firms financials say. Now, while reading up on the corporate filings (10Qs and 10Ks) of your favorite five companies is pretty easy, what about analyzing the statements of say the largest 3000 companies in the country? This would be a daunting task, and Feng Li from University of Michigan set out to figure out a way to gather all this data and analyze it. He looked at the language in annual filings for for around 3,000 firms over a ten year time span. Using a text algorithm Li assessed the incidence of language associated with risk in companies annual filings (1oKs). Li finds that:
an increase in risk sentiment is associated with lower future earnings: Firms with a larger increase in risk sentiment have more negative earnings changes in the next year. Risk sentiment of annual reports can predict future returns in a cross-sectional setting: Firms with a large increase in risk sentiment experience significantly negative returns relative to those firms with little increase in risk sentiment in the twelve months after the annual report filing date.
To explain plainly, and again this is pretty straight forward, companies that suddenly begin to worry about risk a whole lot more are more likely to be exposed to more actual risk. This should be reflected in lower annual returns in companies with heightened risk concerns. To that end, Li contends that:
A hedge portfolio based on buying firms with a minor increase in risk sentiment of annual reports and shorting firms with a large increase in risk sentiment generates an annual Alpha of more than 10% measured using the four-factor model including the Fama-French three factors and the momentum factor.
While I take past returns and back-tested returns with a grain of salt, the idea makes intuitive sense and implementing Li’s strategy is an interesting way to take a broad look at how companies are thinking about the risks they are exposed to.
Here is the paper, for those interested in reading all about risk sentiment in annual reports -> ssrn-id898181
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