Old-School Alternative Data
In 1958, Philip Fisher published the widely acclaimed book Common Stocks and Uncommon Profits. In this book, he advocated use of the "scuttlebutt" technique as a way to find and keep strong growth companies in your portfolio.
In case you aren't the seafaring type... "scuttlebutt" is a sailing term for a particular place on the ship where deckhands would exchange a lot of gossip. It's like the water cooler, but for deckhands.
As you might have guessed, the scuttlebutt method is a system whereby the investor asks a specific set of questions to different people involved with the company under consideration. These people can include employees, managers, executives, owners, suppliers, vendors, competitors, and outside researchers. Certain questions from the set of 15 Fisher lays out are more or less applicable to each type of person.
I'm not here to summarize the book. I'm here to draw a parallel.
The answers sought through the scuttlebutt method are an old-school form of alternative data.
What is Alternative Data?
I define alternative data as non-obvious leading indicators of financial performance. Typical modern examples include social media data, satellite imagery, and foot-traffic data.
I don't like how the industry often suggests that alternative data must be big and automated. I think it is much more important that alternative data is non-obvious and rare. Information asymmetry drives alpha, right?
Alternative Data and the Scuttlebutt Method
I posit that 7 of the 15 scuttlebutt questions seek alternative data as their answers. As such, it is possible to use modern alternative data methods to, at least partially, answer them. We will list the questions and some potential methods to collect data about them. They fall in three groups.
Organizational Growth Drivers
These questions relate to effectiveness of critical teams in the organization, namely R&D and sales.
Question 3 How effective are the company’s research and development efforts in relation to its size?
To answer this question, the investor could compare the quantity of patents gained or academic papers published to the size of the R&D team, the size of the company, the company's revenue, and the overall headcount.
Question 4 Does the company have an above-average sales organization?
To answer this question, the investor could collect social media data on the salespeople associated with the company to attempt to estimate their effectiveness. On the other hand, the investor could collect conference attendance rosters and measure the impact of presentations and marketing materials published by the companies.
How to measure sales effectiveness is a very opinionated topic, but, based on your inclination, some viable alternative data method ought to exist.
These questions pertain to all of the interpersonal relationships that exist inside and outside the company.
Question 7 Does the company have outstanding labor and personnel relations?
Collect data to attempt to estimate employee satisfaction, wages, and turnover. Numerous employment data sources exist for this.
Question 8 Does the company have outstanding executive relations?
Fisher believes it is important to know where executives come from and how they reached their positions. Investors can collect data about employment history of key executives. Fisher believes that a good proportion of executives should come from within the company and have a long history with the company.
Question 9 Does the company have depth to its management?
Fisher would believe there is a lot to glean from employee hierarchy charts. An accurately designed employee hierarchy chart would give the investor an idea of whether or not key executives have the power to make and execute effective change. Fisher would argue that a brilliant executive hamstrung by too much bureaucracy is bad news for innovation. An ideal hierarchy chart would display clearly who is in charge of which functional areas, and allow each person the appropriate latitude the innovate.
Transparency and Integrity
Fisher believes transparent and honest companies outperform. These questions aim to differentiate along that axis.
Questions 14 and 15 Does the management talk freely to investors about its affairs when things are going well but “clam up” when troubles or disappointments occur? Does the company have a management of unquestionable integrity?
Can transparency and integrity be evaluated with alternative data? Natural language processing fanatics would be quick to argue that running NLP against earnings calls for signs of earnestness and caution would do the trick, but I'm not so sure. The business world has seen a great number of executives with wildly different personalities achieve great returns for their stakeholders.
Nonetheless, I think I've made my point.
Fisher had a philosophy about what drives long-term growth in businesses, and his philosophy has since been applied successfully by multiple generations of investors.
It is amazing that, even today, people invest without knowing much of the information Fisher considered critically important. As the industry continues to push forward with highly quantitative methods and troves of alternative data, it is important to remember that investors have been seeking the same fundamental clues for decades. It is even more important to remember that, before computers and the internet, there were reliable ways to get that information.