‘Early Days’ For Alternative Data
There is a range of differing opinions on the use of alternative data. Which alt data sets are best, how is alt data used, and how is alt data best integrated into trading-desk workflows are just a few of the areas up for debate.
But if there is one consensus, it’s that the future of alternative data is promising.
“It’s very much early days,” said Sanford Bragg, Principal at Integrity Research Associates. “A lot of alternative data doesn’t have much history yet. It’s going to ripen, it’s going to deepen, it’s going to get better quality. It’s going to have a huge impact, both on the buy side and the sell side.”
In financial services, Bragg defines alternative data as any form of ‘big data’ that can be used as an input to making an investment decision. It’s a broad category, with multiple types of data sets falling under the alt-data umbrella.
Twitter and other social media platforms are a source of alternative data. Given that social media itself is less than 20 years old and widespread use has only come about over the past decade, it stands to reason that leveraging social media for trading and investing is an emerging methodology.
“I think in three to five years, everyone will be using alternative data,” said George Goldman, Vice President and Head of Finance Sales at Dataminr. “It’s not an if, it’s a when do people figure out how to incorporate this into their investment processes. If you’re not at least thinking about it now, you’re going to be left behind.”
This past January, BlackRock merged its active and quantitative equity teams. Any strategic move by the world’s largest asset manager is closely watched as a potential bellwether for the industry, and Goldman noted that there is indeed a trend afoot.
“The quant guys are becoming a little bit more fundamental in nature as far as their research process goes, and the fundamental guys are becoming a little bit more quantitative,” he said. “That should bode well for alternative data, which can be considered in the sweet spot of both as that convergence occurs.”
Aside from so-called quantamental investing, hedge-fund consultant Gene Ekster cited a couple future trends in alt data: increased institutional investment adoption amid an ‘arms race’ for better datasets, and larger funds procuring raw data directly from the source rather than indirectly via research providers. “This is driven in part due to the insatiable need for unique alpha present nowhere other than noisy, unstructured datasets,” he said.
“I believe we are also going to see very large players enter the alternative data ecosystem — if not Bloomberg, then another company of its caliber,” Ekster added. “I don’t think there’s going to be a full democratization of data anytime soon, but we will see the emergence of new sophisticated marketplaces tailored to this industry that are more than just a simple brokerage model.”
Bragg of Integrity Research noted an increasing prevalence of trading and investing firms ‘scraping’ the web for unique data, for example price changes on Amazon.com or reviews of the latest Apple product, to be used as inputs on their financial models. “You also have an increasing volume of data that is not being sourced through the web,” such as satellite, geospatial and traffic data that can be used to track the volume of activity at a retail outlet.
Dollars rule Wall Street, and ultimately, a tipping point in the use of alternative data may come down to dollars — that is, the opportunity cost of dollars lost when a market-moving event breaks on social media. “Once firms recognize they are missing out, it removes the doubt about whether Twitter works for people in the financial world,” Goldman said.
Previously in this article series:
FTI trading head says it's about automation, alternative data, and analytics.
Will too many providers dampen 'alt' data's appeal?
Once the stomping grounds of quant traders, long-only firms start to search alternative data for possible alpha.
It's about leveraging the data that not everyone else is looking at to get ahead of market volatility.
'Big data' is everywhere. Traders needs to identify the tiny subset of data that stands apart.