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The hunt for alternative data in the finance industry

The hunt for alternative data in the finance industry

Leor Distenfeld

Mar 6, 2020

More investors and major hedge funds are considered quants today – that is, they make decisions that are driven in part by insights discovered using AI and bots. As a result, more and more data from what is considered ‘alternative’ sources is needed to feed their algorithms and continue to give them a unique view of the market.

From news and social media data, to satellite imagery, to natural language, weather, credit card purchases, app installs and engagement, web traffic, patent filings and more, leading indicators contained within these external data points serve as forward-looking trading signals and are increasingly seen as a valuable commodity to help asset managers beat out the competition. According to, more than 160 financial institutions now employ at least 340 full-time data analysts, scientists and engineers.

The hunt for alternative data in the finance industry

According to the Financial Times, “Money managers can use ‘natural language processing’ to unearth patterns in analyst earnings calls, TV interviews or central bank speeches; ‘deep learning’ on satellite images of car parks to guess footfall; or aggregate and analyse millions of emailed receipts and credit card swipes to get an edge on consumer spending trends.”

To mine this data, institutional investors are hiring analysts by the dozen, creating a scarcity in this niche talent pool and driving up salaries. The Financial Times reports that investment groups have more than quadrupled their number of “alternative data” analysts over the past five years.

The hunt for alternative data in the finance industry

As a result, a new class of data-driven startups has emerged to collect, enrich and package those alternative data sources in a way that’s easy for algorithms to process and analyze. Consulting firm Tabb Group reports that this “alternative data” market was worth about $200 million in the US in 2016 and is expected to double in four years.

Data-driven startups leverage alternative data types to inform financial firms

Hedge Funds have long been looking to breaking news and social media insights using tools like Meltwater to inform critical and timely decisions. Today about 75% of hedge funds already use social media and social-driven news feeds to inform investing decisions, according to Greenwich Associates.

Today we have access to a treasure trove of additional alternative data sources that can be combined with social and news media to offer traders real time, predictive insights. Through a number of partnerships, data companies like Quandl are looking to Outside Insight from new sources, like private aircraft flight paths, Tesla sales, credit card purchases, FX rates and more to help predict things like “potential M&A activity as well as corporate investments, partnerships and expansions.”

What else can these types of datasets help predict? According to a recent press release from the firm, in January 2017, for example, the Johnson & Johnson private jet stayed parked near Actelion HQ for five days, one week prior to announcing a $30 billion deal between the companies. This is all information that can give analysts a massive upper hand to help them make informed trading decisions.

While details like private jet flight paths, Tesla purchases and the movement of drill rigs are rather specific data points, there are a number of external data sources that can be leveraged to regularly benchmark a brand against competition, discover trends and patterns and predict what’s coming. Outside Insight is an AI-driven platform that aggregates these alternative sources and pulls out insights and patterns, making it easier for business leaders to determine where they need to investigate further, invest more resources and change their strategies.