Examples of innovators and early adopters (based on publicly available information):

Quantitative funds

Acadian Asset Management. In 2014 in an Institutional Investor article entitled “Unconventional Wisdom” the CIO of Acadian Asset Management, John Chisholm, stated “my firm is exploring ways that we might discern investor and insider sentiment from big data, including news, blogs, tweets and product reviews”. On 7th March 2017 Acadian announced a partnership with Microsoft’s Bing Predicts big data technology to inform its investment decisions: see link.

Blackrock’s Scientific Active Equities (“SAE”) Investment Group. In 2015 in a note entitled “The (new) tools of the trade” Blackrock’s SAE gave some examples of work it does: “Sophisticated algorithms search for investment insights by sifting through vast amounts of public data. Among other things, SAE’s applications look to: 1) analyze text; 2) identify a specific piece of data (think of a needle in a haystack); 3) review customer/supplier information; 4) catalogue web search traffic; and 5) discover relevant news items”.

Winton Capital Management. In a 2015 AFR article entitled “Hedge funds ‘ripe’ for digital revolution”, Winton stated that it trawls “through credit card and social network data to forecast sales, analyzing rail car networks to measure the pulse of the US economy or…weather data to forecast crop yields. In February 2017 Winton hosted an interesting workshop in San Francisco entitled “Using Technology to Reform Economics”. Please click on this link for more information.

Discretionary funds

Citadel. A Bloomberg article dated 22nd November 2016 stated “Citadel in October [2016] promoted its former chief risk officer, Alexander Luyre, into a new role of Chief Data Officer”.

Point72. At a CB Insights conference in July 2016 the Chief Market Intelligence officer of Point72, Matthew Granade, stated that alternative data is useful for generating alpha. He said “it is a real change from how investing used to work…if you want to understand what is going on with McDonald’s, you are going to have to look at credit card transactions data, you are going to look at geo-location data, at app downloads and a handful of other things. And suddenly you are going to have a very robust picture of how McDonald’s is doing and you are not going to have to talk to McDonald’s about that”.

Third Point. Third Point’s 2016 year-end investor letter stated: “We have added data science to our toolkit for identifying interesting, uncorrelated opportunities”.

Mutual funds

State Street. In an August 2016 Institutional Investor article entitled “Unexpected risk meets unexpected data,” State Street’s Chairman and CEO highlighted a few ways investors can use surprising sources of information to enhance portfolio transparency and identify risk exposure ahead of potential black swan events e.g. “Online retail. When consumers order products, they may be helping investors better track inflation trends to help recalibrate investment strategies before – and after- an event. PriceStats, an inflation series built by State Street Global Markets on online data, uses technology to monitor price fluctuations on roughly 5 million items and tends to identify price shocks faster than similar measures of offline prices, helping investors quickly understand potential shifts in inflation in more than 70 countries”.

Schroders. In its 2015 annual report, Schroders stated that “analysis of ‘big data’ could become a key differentiator…this year we set up a Data Insights team, representing a significant new initiative for the Group. The team is focused on developments in data analytics for investment and research, to enhance and complement the existing skills of our fund managers and analysts”.

NN Investment Partners. In a January 2017 interview, a senior portfolio manager (Mark Robertson) told Markets Media that the firm began thinking about whether social media, artificial intelligence and big data had a place in its investment process in 2009. He gave an example of gains in financials that were partly driven by sentiment data: “last June and July financials were unloved…sentiment picked up and by September, when incorporated with financial analysis, there was a strong signal to buy. By the end of 2016 we had 10% outperformance over the benchmark”.