When Johnson & Johnson agreed to buy Actelion for $30 billion on January 26, 2017, its stock soared almost 20 percent. The deal was the largest in J&J’s 130-year history.
This was good news for Och-Ziff Capital Management Group LLC (who had built a stake worth about $766 million), Eton Park Capital Management (whose bet was almost $500 million) and Elliott Management Corp. ($200 million).
The three funds, however, did not make their bets blind; they had backed up their belief by good old-fashioned sleuthing. They tracked J&J’s private jet movements with alternative data. According to Bloomberg, “as a Johnson & Johnson corporate jet remained mostly parked a short drive from Actelion Ltd. headquarters near Basel, Switzerland, they grew increasingly confident of a big score”.
By having access to jets data, Och-Ziff, Eton Park and Elliott Management not only profited from their information advantage, but they also hedged their risk exposure in the position. While many speculated on the opportunity, these funds traded on knowledge few others had.
In Search of an Edge
At the heart of financial data is the search for insights — the desire to gain an accurate and deep understanding of the market before anybody else.
Business intelligence, however, need not come from the skies. Today, every company is a data company. Firms produce huge quantities of data and they consume huge quantities of data in the pursuit of profit. One way or another, companies and analysts are seeing – in real time! – the same data that will one day become the content of 10-K filings, economic releases or financial news.
As an investor, this is both an opportunity and a threat. The opportunity is to find an edge in all these new data sources. The potential here is astronomical: Every single industry is going to be transformed by data and early access to that data means an inside view on all those industries and companies.
The threat is that others may beat you to the punch. We’re all aware that every model has a shelf life. Sooner or later, the ideas and techniques behind every proprietary analytical technique diffuse into the broader world — at which point, that technique is no longer the source of a competitive edge or alpha. This pattern applies to the world of data.
Rare, unique and proprietary data eventually diffuses and becomes commonplace, easily available, table stakes. Even company fundamentals were once unique. The best analysts constantly reinvent their models and source new data to avoid their inevitable obsolescence. If you don’t use unique data sources, you will be trading against people who do use them and who have more information than you.
Luckily, there is no shortage of unique insights out there because of the trail of data left by the widespread digitization of businesses — what the finance industry calls exhaust data. Firms like Walmart and Target know exactly what you search for and what you end up buying. Other firms like ADP, Mastercard and FedEx are intimately involved in payrolls, transactions, delivery and every other stage of the commercial pipeline. And every single action that these firms take is recorded and stored for analysis. We may never be able to access all of it, due to privacy and ethical concerns, but it is very much there.
Firms like Walmart and Target know exactly what you search for and what you end up buying. Other firms like ADP, Mastercard and FedEx are intimately involved in payrolls, transactions, delivery and every other stage of the commercial pipeline.
Human interactions are also becoming digitized. Social networks, instant messaging and web search paint a dynamic, real-time picture of what people are interested in and who they’re talking to. Again, every single action is recorded and stored for posterity.
Smartphones are ubiquitous. This means an accurate location sensor, audio recorder, still/video camera and internet connection in every pocket. Almost no part of the world is outside the limits of cellular coverage.
Cars and trucks now have embedded sensors, tracking position, velocity, traffic and much more. Satellites and GPS have gone from the preserve of the few (military) to the plaything of the many; imagery and position data are today a public good.
The Baltic Dry Index, the ADP Payrolls Survey, the ISM Manufacturing Index and the NAHB Housing Survey are all examples of exhaust data that are not traditionally financial in nature (coming as they do from a shipping insurer, a payroll provider, a supply chain group and a constructors association, respectively). Nonetheless, they move markets. In recent years, many such alternative datasets have come to the fore, created by companies in every sector of the economy.
Below are several industries from which Quandl has discovered powerful exhaust data:
The volume of new car sales is a key driver of performance for auto manufacturers. Because the number of insurance policies issued for new cars almost perfectly correlates with new car sales volume, auto insurance data can provide investors a daily count of new car insurance policies, sliced by manufacturer.
Oil rig movements tell important stories about various parts of the energy ecosystem. In the past, investors of such assets relied on monthly reports for production volume and demand, today they can infer commodity transportation from automatic identification system (AIS) data mapped onto port data. By tracking vessel counts and load factors at ports dedicated to ore exports, for instance, analysts are able to estimate iron ore sales for each producer with very high accuracy — weeks to months in advance of official publication.
Business Health Metrics
Using intercompany payment patterns (payment amounts, delays and delinquencies) investors can discern whether a company is paying its debts on time. This data can then help gauge debtor distress — a leading indicator of stock underperformance.
Corporate and Executive Air Travel
Executive travel provides leading insights into corporate strategy — most notably mergers and acquisitions, investments, partnerships and expansions. Accessing departure and arrival data on corporate flights, therefore, can help investors predict market-moving events.
The Spectrum of Diffusion for Alternative Data
We’ve barely scratched the surface of potential alternative data sources. The data that will become available in the next decade dwarf what we have access to right now.
But as new data becomes available, “old” data will become commonplace. Diffusion is inevitable and analysts must prepare for this eventuality.
Data has a natural life cycle.
- NASCENT Newly discovered data sources are rare, jealously guarded and valuable because they offer investors a significant competitive advantage.
- DIFFUSING But as time goes by, this data diffuses to a wider audience and its uniqueness and exclusivity diminishes. Nonetheless, analysts continue to use the data because not doing so would leave them at an information disadvantage.
- FULLY DIFFUSED Finally, the data becomes table stakes: fully priced in by a market that has moved on to the next source of edge.
Smart analysts who have internalized the spectrum of diffusion are aware of this dynamic and are always ready to adapt. In evaluating whether to invest in unique data, some questions to ask are: Do the people who you are trading against have this information? Are you willing to risk the possibility that they do? After all, you don’t want people with better information trading against you.
Those who can access the information the fastest will stay ahead of the market.
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