Will family offices be replaced by machine learning algorithms?

Recently, an open source machine learning platform called Seldon, one of Barclays Techstars accelerator winners, said during a presentation that 47% of jobs will be lost to cognitive machines in the next ten years. Yes, that’s right, nearly 50% of all jobs. And many of those job losses will be in finance.

From an asset management and family office perspective, the threat of serious disruption will likely come from machine learning and artificial intelligence (AI).  Some asset managers are taking it very seriously. BlackRock, one of the world’s biggest asset managers, made headlines in October last year because it was in talks with Google to do AI-based research which was described as “a very exciting joint venture”. Earlier in the year, BlackRock hired Bill MacCartney, a former Google scientist, to help it apply machine learning to quantitative-investment strategies.

Another big player is JPMorgan Asset Management’s hedge-fund business Highbridge Capital, which is known to be working with San Francisco-based Sentient Technologies to develop investing strategies using AI. Financial firms seem to be turning to external partners to conduct this sort of research, while also scrambling to recruit AI scientists and stay up to date in terms of the revolution in data science and machine learning.

Meanwhile, Bridgewater Associates, the world’s largest hedge fund with about $154 billion under management, has been vocal about its use of AI. These days, it’s not uncommon to hear about investment firms hiring data scientists with PhDs in neural networks, or physicists and astronomers who can remove the noise from data signals. What you’re seeing at the big asset managers and hedge funds is filtering down to family offices, although few, if any, have hired AI scientists to help them develop investment strategies. That may change in the future.

Experts in the field see AI strategies being used for long-term macro strategies, which an army of human analysts would have difficulty deciphering. For example, making large bets around the price of oil – how will oil prices impact different industries and what does that mean from a portfolio investment? This could take a group of analysts sometime to interpret, but AI would be much faster – and cheaper.

Using Bridgewater as an example, the head of AI at Publicis.Sapient, Josh Sutton said: “If you look at their (asset managers) historic trading strategies, it’s been very much long-term bets around what’s happening at a macro level. They have built their entire business on having some of the best research and analytics in the industry and some of the smartest minds thinking on that.”

Sutton, who has worked in financial services for 15 years, said he expects machine learning to be overlaid with more common sense AI technologies to mimic the role of an analyst. AI, it seems, can be deployed to do everything from putting together valuation models around companies, to doing entry-level macro analysis of what’s happening in various industries and coming up with hypotheses with this data.

Sutton agreed that analysts’ jobs are absolutely going to be under threat in the future. “And that’s going to present a little bit of a quandary because this is going to do two things,” he said.

“Firstly, it’s going to dramatically increase the coverage that companies can have, where historically they have been constrained by the number of analysts that they pay and the amount of research that an analyst can perform during a normal work week. That constraint goes away as you look at leveraging AI platforms.

“But a separate, longer-term challenge is that the analyst role has typically been a grooming role for talent. It enables people to come in, learn the business and progress through the organisation to things that require a greater degree of insider trading and portfolio management capability. So you are taking away some of that training ground. It’s a little bit of a Catch 22, in that companies are having greater capability to cover more and generate more insights from wider coverage area. But at the same time they will suffer from a lack of grooming for future talent.”