It’s hard to see how the trend towards an increasing quantification of the investment business. Like the trend toward ESG investing the question is not if or when the question is how much. In some very real ways, quantitative techniques are already front and center in the investment business. The rise of smart beta ETFs and the wave of new robo-advisors all speak to the practical application of quantitative investment techniques.
One way the rise of quant will affect the investment business is the skill set required for new entrants. Barry Ritholtz recently spoke to Matthew Rothman head of Global Quantitative Equity Research at Credit Suisse. The entire discussion is worth a listen, but at the end, Rothman spoke about the skills needed to be a good quant. Here is the advice he gives to aspiring quants.
- “Program, program, program, program.”
- The ideal quant candidate: can program, understands statistics/econometrics, understands finance and is curious.
- It’s hard to teach programming.
- “Be skeptical of your own work. Find the errors in your work before someone else does.”
- Lastly, “Have a healthy respect that models can be wrong. Even the best models will go through periods of underperformance.”
Everybody uses quantitative techniques in different ways. Some firms use it in support of fundamental managers. Some firms like Man Group have wholeheartedly adopted artificial intelligence techniques throughout their business. From a big profile at Bloomberg:
By 2015 artificial intelligence was contributing roughly half the profits in one of Man’s biggest funds, the AHL Dimension Programme that now manages $5.1 billion, even though AI had control over only a small proportion of overall assets. Elsewhere in the company—and in the industry as a whole—AI technology is being used to find the speediest way to execute trades, to make bets on market momentum, and to scan press releases and financial reports for keywords that could signal that a stock will rise or fall. Even Man’s very human discretionary division, where business is centered on experienced asset managers, is exploring AI techniques.
Machine learning techniques like those used at Man Group are a standard tool for data scientists not only in finance but elsewhere. Druce Vertes writing at Alpha Architect has a primer on machine learning for investors walks even old school quants through the basics. Vertes writes:
In the investing world, machine learning is at an inflection point. What was bleeding edge is rapidly going mainstream. It’s being incorporated into mainstream tools, news recommendation engines, sentiment analysis, stock screeners. And the software frameworks are increasingly commoditized, so you don’t need to be a machine learning specialist to make your own models and predictions.
There is a risk that we all become too reliant on quantitative models and techniques. In light of recent incidents, the US Navy has issued orders to “go to back to basics.” Navy ships will now “use old-fashioned compasses, pencils and paper to help track potential hazards…” While the high-tech tools the Navy has are useful, an understanding of the basic tools is still necessary.
Black boxes can be useful. In a certain respect who cares how returns are generated so long as they are being generated. The big question is how much do investors need to know about how their money is being managed? Josh Brown at the Reformed Broker writes:
Our strategy – which is to be over the top and almost unnecessarily transparent in educating clients on our portfolios – stands in direct contrast with this. I suppose the investor groups for these two different approaches are self-selecting to some degree. Some people really want to understand what’s going on with their money while others will accept the fact that maybe they can’t but someone else will.
Investors of all stripes are going to have to come to terms with an increasingly quantitative investment business. Educating ourselves is the only solution. There is no substitute for having a written investment plan in place and recognizing how each component works therein. What we don’t understand, we fear. Things we fear we have a tendency to run from at the most inopportune times.
