Alternative data as the new source of alpha: Quandl

The treasure hunt for new alpha sources in the investment world continues. Seems like every decade or two, there is a new way that arbitrages itself out and then the market discovers the next best alpha source. So, we went from extracting alpha from Long/Short strategies (the hedge fund recipe) to quantitative algorithmic trading, then to High-Frequency trading, and now we wonder whether “Alternative Data” will be the name of the game going forward since the previous approaches don’t work anymore. Makes sense to look for the “keys” that may unlock new sources of alpha, in that Data direction. By 2020 we will be swimming in 35 zetabytes of data (1ZetaByte = 10007 bytes) and with 50 billion of connected devices.

The democratization of data is already here. The gatekeepers of public data are being gradually taken out. However, there are still big players that don’t have modular offerings and charge high annual subscription fees ($20k or more) to investment professionals for their “all-inclusive” services of data and tools (public info ++). Bloomberg remains one of them but is adding API capabilities to their offering while still dominating a captive clientele even though there are plenty complaints about not cleaning up the data provided. Eikon of Thomson Reuters has already a lot of free data available in their desktop offering.

We have covered alternative data for investment professionals many times in the past. Starting with Xignite the first and leading disruptor in the data space. Then looking at the kinds of alternative data and whether the growing robo-advisory trend makes any use of it. On the Fintech Genome platform, there are many topics that cover sentiment analytics, AI and ML for investment purposes; which are also specific types of alternative data.

The focus of today’s post is on the current pulse of alternative data for investment professionals. Quandl is a leading Fintech in providing multiple types of such data and more importantly assisting its clients to monetize their data.

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Quandl not only sources the data, cleans it, but also uses data scientists to filter, combine, and package data sets that can have alpha potential for specific needs. A global macro manager needs alternative data related to real-time flows of global trade (e.g. energy, coal, iron, shipping, etc); a traditional stock manager needs more data related to consumer insights and transactions of a company/sector, employment trends etc; algorithmic traders need statistically robust trading signals; ect…

Quandl treats its institutional clients much like retail consumers, therefore, offering a high-end user-experience and creating new value on top of the high-quality data. Quandl is the platform that is now focusing on creating new revenue streams for its clients by educating them on how to productize their own internally produced data (i.e. not necessarily purchased by Quandl), creating the infrastructure to deliver it in a consumable way and distributing it in a complaint way according to privacy and data protection laws (GDRP in the EU; information privacy laws in the US). Quandl is not providing any kind of data that contains personally identifiable information (PII). Investment professionals are interested in aggregate data, like how many people bought a service or a product, or downloaded an app; not the name and address of these individuals.

Quandl has over 200,000 users currently (last fall was Series B funding). For now, these users I suspect are using the Quandl alternative data as complementary to the Bloomberg or Eikon terminal. Time is the only way to obtain evidence that the Quandl alternative data is a new source of alpha and Quandl can train clients to create sources of revenue from their own data.

Efi Pylarinou is a Fintech thought-leader, consultant and investor. 

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