The world’s largest asset manager, BlackRock, announced this week that it is setting up a new centre for research into artificial intelligence. The lab, based in the heart of Silicon Valley, is part of the group’s Tech 2020 plan and demonstrates how active fund managers are aiming to harness technology to improve fund performance and lower costs at a time when both are under scrutiny.
This is the latest of BlackRock’s forays into technology. It already gleans insights from variuos sources, including analysing traffic through corporate websites, text analysis of earnings calls transcripts, and looking at smartphone geolocation data to see where people are shopping.
Schroders, BNY Mellon, and a host of other fund management groups are also looking at ways to harness ‘big data’ to improve their investment returns. A report last year by S&P found that 80% of asset managers plan to increase their investments in big data over the next 12 months. Only 6% of asset managers argue that it is not important.
A recent Barclays report found that using alternative data, such as social media feeds, satellite data, or credit card data is now more prevalent using economic data, such as employment or inflation figures, or sell-side data, such as analyst reports or broker recommendations.
This growth has a number of drivers: the dramatic increases in computer processing power, storage capacity and information has created vast amounts of data. Research group IDC predicted a ten-fold increase in worldwide data by 2025.
Most of this is unstructured. But increasingly, asset managers recognise that harnessing this data can provide additional insight. Going one step further, using this data may even be necessary for future returns if competitors are using it and it becomes increasingly reflected in share prices.
Is Big Data the Key to Big Profits?
There are a number of concerns in this. For example, will the advantage ultimately be arbitraged away as more investors look at the same data?
There is data everywhere and investment managers need to pick and choose where they ask their data scientists to look. They may all be responding to the same cues - if for example, a company is subject to some kind of supply chain scandal, most fund managers will look at the impact on their brand. This may arbitrage the advantage away.
The Schroder data insights team believes not: “Effective data science will unearth insight that is unlikely to have been captured by others. The greater the quantity of data that may be relevant to understanding an enterprise, the more combinations and permutations of analysis it becomes possible to conduct.
"By extension, the likelihood of other parties conducting exactly the same analysis diminishes. It seems likely therefore that data science at scale within a large investment organisation will generate insight that is differentiated and hard or unlikely to be precisely replicated.
“Far from creating a level playing field, where more readily available information simply leads to greater market efficiency, the impact of the information revolution is the opposite: it is creating hard-to access “realms” for long-term alpha generation for those players with the scale and resources to take advantage of it.”
Adapt to Survive
This may be the other problem. Does the type of investment required in big data mean that larger investment managers gain a strategic advantage? Man Group chief executive Luke Ellis recently said that a failure to adopt quantitative approaches could prove fatal to funds. "If you don’t understand how to treat data with respect, you will get eaten alive," he said.
It may depend on the sector of focus. It is clear that there are some sectors where big data insights can make a notable difference – such as the financial or consumer sector. There are other areas where there is relatively little ability to add value – utilities, for example.
Equally, smaller fund groups can look externally to specialist research groups, so are not dependent on having large in-house teams. It is also clear that data science cannot replace investment skills. Schroders says that left to their own devices, data scientists won’t ask the right questions and need an insightful investment team. Knowing what makes a difference matters.
There is also the question of whether this is simply a marketing tool. Investment management groups need to be seen to add value over and above passive but cannot compete on cost. This is a way for them to keep costs lower but be seen to be doing more than simply replicating an index. In this way, it may play an important role in the future of active management.
Does it add value? It is probably too soon to tell. Schroders was a pioneer and it only set up its division in 2015. It is also difficult to monitor – if a fund manager asks for specific insight into a stock, it is difficult to isolate the impact of that insight. However, it is another tool for active managers to push back on passive rivals.
