How Quant Investing Can Help Meet Investor Goals

There's been this buzzword — objective-based investing — that has centered on a very specific type of investment that typically people would think of as multi-asset class, go-anywhere-type investments, with a cash or CPI-plus- type of objective.

I'd broaden the definition. We need to always remind ourselves of what the end investor's goal is and that they have employed us to help them achieve that outcome, however narrow the mandate might be. For example, if an investor is seeking an emerging markets small cap strategy, it's important to understand how that exposure will help them achieve their investment goal so we can devise the appropriate objectives within that mandate.

As the investment industry in recent decades has evolved to focus on narrower mandates, it has caused us to lose sight of that link back to the end investor's objective. The types of conversations we are having with our clients include explicit objectives of getting more from the core part of their equity portfolio, how they might be thinking about de-risking their growth assets, or how to reduce redundancy in their active multi-manager equity portfolio.

Absolutely. Success for me and my investment team means being involved in that conversation with all of our clients right from the beginning. It's about engaging with the client even before they have issued an RFP. The client has a very broad investment problem to solve in the context of their equities, and our definition of success is being able to help them solve it.

Quantitative investing means different things to different people. It can involve a lot of buzzwords and very technical language that some investors find difficult to understand.

One of our core beliefs is that markets are not efficient — things like behavioral biases and limits to arbitrage create the opportunity for active managers to add value. Secondly, we believe that our investment intuition is best applied to a very broad investment universe in order to minimize stock-specific risks in portfolios and take advantage of the breadth the market offers us. Quantitative investing allows us to systematically take advantage of anomalies and market inefficiencies across a very wide range of markets and securities.

Our third core belief is that just being quantitative isn't enough. It isn't enough to just identify statistical inputs and stock returns — data mining. Instead we need a very strong investment and economic rationale to start with before we look to prove our investment intuition with data and scientific experiment.

Our quantitative process provides a framework that enables us to apply our insights on predictive stock returns to all kinds of different investor objectives.

For instance, an investor may have an index-like core, and employ us to achieve incremental alpha with low tracking error relative to the index. We build portfolios that use our forecast of expected returns and models of risk.

That's one way of applying a quantitative process to an objective — of low tracking error and low alpha. But the same engine that we use to forecast stock returns can then be applied also to a very different investor objective. So picture an investor who has a growth objective but needs to minimize the total volatility of their portfolio. We can take the same models of expected return and apply them using an algorithm incorporating these end objectives.

It's especially relevant today in a world of low expected return, where valuations are high and volatility episodes are frequent. Even 10 years on from the global financial crisis, people are still very focused on the crisis and are thinking about risk in their portfolio.

Balancing expected returns and expected risks is a very complex problem to solve. It's something that can't be done in your head and not even on a spreadsheet. Investors need to have a component of their portfolio that enables them to control the risk as well as deliver the return. It's very difficult to do that without quantitative methods. And again, we always have an investment intuition and economic rationale underlying these methods.

Big data is interesting but the way the investment industry is using it is still largely in its infancy. We have invested in the necessary infrastructure that enables us to process large amounts of data. It helps us to get more granular in our forecasting.

For example, we can model a retailer's activity in more systematic ways. Fundamental analysts may have counted how many people were coming through the door but now we can do that and more with new data sets. Which is why I don't always call it big data, I call it new data, because it's not always big but it's definitely new.

It comes back to my first statement, about it being a buzzword, one that's typically used to remove benchmarks.

So new benchmarks are required. Returns themselves are not enough. What are the objectives — and benchmarks — from a risk perspective? Are they market-relative risk objectives? Are they total risk objectives?

In an industry very centered on tracking error, total risk objectives are only just starting to be thought about.

Talking to end clients has been eye-opening for me as a professional. It helps you understand what people worry about and what they're trying to achieve. It has helped me to be a better investor.

The goal of a client might be to maximize returns and preserve capital. That's true of many individual investors — such as people entering retirement — but applies to many corporate investors too. It is a problem well solved by objective-based investing.

Which comes back to my view that objective-based investing is just always reminding ourselves of why we're here. We're not here to beat benchmarks. We're here to help all investors achieve their wealth objectives. And no matter how narrow or how wide the mandate, we do whatever we can to make sure the portfolio objectives are aligned with the end investor's goals.