Quant reimagined

Over more than a century, quant has evolved from a purely theoretical concept to a practical approach to investing in financial markets. Ideas that were once confined to the world of academia have been implemented by numerous investment strategies, often with remarkable success.

Along the way, there have been some high-profile failures too. For some, this has led to a degree of skepticism and even cynicism towards quant strategies.

But our view is that today’s quant is not the quant of old — and that important lessons have been learned from the past two decades.

Previous quant crises such as 2007’s "quant quake" and the 2008 Global Financial Crisis, for instance, illustrated how overly leveraged or overly concentrated quant approaches were prone to weakness during periods of extreme market volatility.

Today, having learned from those crises, we know that diversification is a prerequisite both between factors such as value, size and momentum and within them. Rather than relying on one formulation for each factor, strategies needed to have several formulations of each.

We also know that quants need to look beyond the standard informational inefficiencies when looking for mispricing in the markets. Behavioral factors, which depend on human emotions rather than hard information, are also a powerful force. In this sense, we view it as encouraging that recent bouts of extreme volatility have — so far — not generated equivalent quant-led crises of their own. In our view, this suggests that quant investors have learned the lessons of the past.

Beyond that we can also say that — due to some truly remarkable advances in computing power in recent years coupled with an abundance of data — today’s quant is not the quant of old. It’s immeasurably more powerful and has access to insights that would have been unthinkable even just a decade ago.

Data! Data! Data!

Take data, for instance. Today, you can carry terabytes of data with you in the phone in your pocket. And along with much lower storage costs, there’s a growing appreciation of the importance of data for all facets of the economy. In 2006, the British mathematician Clive Humby said that ‘data is the new oil’; since then, the increased digitization of most businesses has made data far easier to collect.

These shifts are revolutionary. While traditional datasets are still available, we now have an ever-expanding range of new and deep datasets. Many of these weren’t even fathomable 20 years ago — real-time records of every credit-card transaction or satellite photographs of every parking lot in the world, for example.

If data is the raw material from which quant strategies draw their lifeblood, then, truly, we live in an age of superabundance.

Superhuman insight

But data is nothing without the ability to interpret it meaningfully. And it’s here that the recent extraordinary advances in computer analysis and machine learning come into play.

When these technologies are applied to these new datasets, they offer insights that human analysts simply can’t match. Humans can’t count all the cars parked around the world, for example. But machines can — and they can update their figures and their forecasts every single day. And real-time data — the number of trucks leaving a company’s factories, say — offers spin-free insights that may not be obtainable from company representatives.

This is why we believe today’s quant bears little relation to the quant strategies of the past. Our view? The story of Quant will continue to develop from here. Its 100-plus years of evolution are just the opening chapters in a longer, more complex narrative.

To learn more about how quant strategies have evolved and where they will go from here, read our in-depth report: A history of quant.