Peter Sleep, senior investment manager at Seven Investment Management, says there are two big themes in the market making it tough for quants.
The first is the market’s obsession with very large and expensive Faang stocks such as Alibaba and Tencent. Sleep says many systematic funds will be betting against these stocks, which are flying now, and will be suffering as a result.
“When you read about the terrible performance of the Faangs you may start to see the systematic funds outperforming,” he says.
The second theme is that specialist trend following strategies are having a tough time. Both CTAs and multi-factor funds with a significant trend-following component, have suffered since the sudden equity market fall in February and subsequent rally.
“Add to this the trendless fixed income and currency markets and you get a very tough first half for trend followers,” says Sleep.
Matthew Beddall, chief executive at recently-launched investment house Havelock London, says 2018 reminds him a lot of early 2007 when there was a nasty sell-off in the quant industry.
Beddall, who previously spent 17 years with well-known quant house Winton Capital, notes the common denominator is that volatility was very low then and it is now.
“When volatility is low, quant strategies tend to assume risk has gone away,” he says. “My own view is as quant strategies become more popularised it is going to be more difficult for people to make money and there has been an awful lot of money flowing into quant in the last 10 years.”
Know what you are buying
Quant strategies are still proving popular, but you have to understand what you are buying, says Philip Bagshaw, senior portfolio specialist at City Asset Management which uses two core holdings considered systematic or quant: Old Mutual Global Equity Absolute Return (Gear) and ADG Systematic Macro.
He says: “We try and be fairly agnostic. It is more about how well you think the strategy can deliver its objectives. Sometimes that is via a person, sometimes that is systematic.
Bagshaw says as a starting point you have to understand and believe why the strategy can produce its returns. He says a lot of funds point to their algorithm or returns in the past but that is a bad starting point.
“You have to start with what are you exploiting,” he says. “If you don’t know what you are using your algorithms to exploit, you are on a road to nowhere basically. It has probably just been luck.”
Hard to define
The term quant covers a pretty broad spectrum of investment styles and people, ranging from very simple smart beta-type investments to very complex, almost sci-fi styles of investment. This makes it difficult to define.
According to Beddall, roughly speaking quants fall into two categories: global macro which includes firms that bet on big global market trends such as currencies, commodities, stock indices and bonds; and those that bet on companies and public equity.
Beddall strategically positioned his firm in the middle ground between pure quant at one end and fundamental active management at the other, after observing a bifurcation in the industry between the two groups.
“On the one hand you have got the quants who are excellent with computers and maths but on average have less interest in business and fundamentals, and on the other you have traditional analysts and fund managers who understand a lot about fundamental but are less good with numbers and tech.
“I felt having a set up with a foot in each canoe was unusual, so we have seen the rise of quant strategies and a number of fundamental managers have looked to move towards the middle.”
The rise of data science
As part of this evolution process, the term ‘data science’ has come to the fore. Something of a buzzword now, Havelock says data science involves combining computing, statistics, and domain knowledge (knowledge of investment management) to identify where and how data can be useful.
As such, many asset management groups now have teams of computer scientists dedicated to discovering what to do with the vast amounts of data available.
For example, Havelock hired data scientist Kate Land to lead its data science effort, while Gam Systematic has Dr Camilla Schelpe as lead scientist across its portfolios.
Gam believes having a scientific approach to data cleaning and machine learning is essential in a world of big data to avoid falling for some of the basic risks of investing. These include relying on unclean or mismarked data, succumbing to overfitting bias with machine learning and believing that you have models that are better than you would believe if you took a rigorous scientific approach.
Man and machine
Given the rapid progression of AI, are we not at the stage when computers can do it all?
Beddall says often quants are portrayed as the computer is always making the decision but the reality is computers just do what they are told and there is a lot of human input that lies behind the typical strategy.
“When you get a house built, the architect draws up a blueprint and the builder builds it,” he says. “For a quant firm, the human is the guy drawing the blueprint and the computer is the one building it. You still need someone to create the blueprint.
“You don’t just go to Dell, buy a computer and wahoo, it’s making money.”