Evolving with thought

The investment industry is evolving rapidly, with disruptions occurring in the areas of data, technology, ESG, climate and others. 

Many of these developments are taking place at the heart of quantitative investing. This creates opportunities for us to evolve - but we have to evolve with thought.

In this video we share how our Quantitative Investment Team at RBC is evolving, by looking at 3 areas where we are adjusting.

• First, we look at how we are evolving the way we blend human and machine with developments in alternative data and machine learning.
• Second, we show how we have evolved our investment process to integrate environmental, social and governance considerations.
• Third, we are placing even greater emphasis on portfolio engineering to ensure we evolve our investment processes with rigour.

Alternative data and machine learning

Our investment approach leverages both human and machine to make investment decisions:

• Human input is used to identify “what makes a good investment”. Examples include: 
  
  
  • Companies that are more profitable than their peers, have better growth profiles, are trading at cheaper valuations, are less volatile and so forth.
    
    
    • Companies with these features tend to generate better investment outcomes over long periods.

With the developments in data science, both inputs – human and machine – need to evolve; so does the way in which we blend the two.

As quants we thrive on data. One area where there has been an explosion is in alternative, unstructured data. 

Think of the abundance of useful information that is available in text data, generated from company filings or earnings calls for instance.  Or the information contained within satellite images. 

These are valuable sources of information that we can use alongside the vast sets of traditional financial data we already employ.

Unstructured data require the use of machines to extract signals, with techniques such as natural language processing. 

Once the signals are extracted, we can plug them into our existing models to test whether they improve the investment outcomes.

In this way we remain true to our investment philosophy, where investment decisions are based on strong economic rationale, supported by solid empirical evidence.

This is different from an unsupervised approach where investment decisions are purely data driven and economic rationale and human input is minimized. 

While we see an increased opportunity to leverage machines, we believe that the right answer still lies in blending both…and that people should continue to play a crucial role in the making of investment decisions. 

ESG and climate

We believe that environmental, social and governance considerations matter when making investment decisions.

While we find periods during which E, S or G have generated positive investment returns, 

we do not find enough evidence to generalise this, across time and regions, which we is something we would require before creating stand-alone alpha factors from E, S and G.

Our current approach therefore focuses on ways to mitigate ESG risks.

We invest in factors that are rewarded in the long-term. But the risks that are created by poor ESG are not rewarded, which is why we do two things:

(1) we remove companies with the worst ESG metrics from our portfolios, and 

(2) we ensure that the average ESG exposure of our overall portfolio is better than that of its benchmark.

We have set these restrictions in such a way that does not negatively impact our investment proposition.

Our current focus is on climate risk, where we are looking at ways to tilt away from companies that do not have credible plans to transition towards a low carbon economy. 

We benefit by having direct access to an in-house team of ESG experts at RBC GAM, including a climate expert… 

and we collaborate closely with them to ensure our efforts have a positive and meaningful impact on our portfolios.

Portfolio engineering

By combining human and machine, we make the investment process systematic. This makes it more rigorous and scientific and allows us to evaluate existing ideas and test new ideas explicitly.

In a world that is rapidly changing, process becomes even more important - and a systematic investment process even more valuable.

The job of portfolio engineering is to ensure that each part of the process delivers what it was designed to do, ensuring that we are consistent in the way we implement our investment philosophy and process. 

It lets us see which elements are driving returns in our portfolios, allowing us to do a better job of tilting towards exposures we want and limiting exposures to risks that do not generate alpha. 

In portfolio engineering we scrutinize every component of our investment process, much like a Formula 1 team would do after a race…

to see which components need improvement and testing to see how new components perform on the track.

We continue to develop new tools to help us with this task, such as the waterfall analysis we recently added…

This allows us to measure the impact of every step in our portfolio construction, whether we are dialing up risk or adding new constraints…

This is a powerful tool that adds rigour when we design and evaluate our models.

Collaborating within a team of experts

To succeed in quantitative investing, one needs experts with skills across a range of capabilities, including a deep knowledge of investments, quantitative methods, data, technology and implementation…

and these experts have to collaborate effectively as a team.

In the last part of this video we share how we are super-charging our collaboration, to ensure we continue moving our business forward, together, as a team.

Our team is made up of three focus areas: portfolio management, research and systems… each with its own, clear accountabilities…

but with one over-arching goal – to deliver exceptional investment outcomes to our clients.

In a world of rapid disruption, the role of the team becomes even more important. One also needs t-shaped individuals…

people with deep expertise in a particular field but who are able to integrate their thinking easily across related fields.

We also focus on honing cultural edges that would support innovation. We encourage each other to be:

• curious, open-minded & eager to learn
• to be driven to continually improve what we do; 
• to have a sense of urgency, which is so needed in an era of rapid disruption; 
• to collaborate effectively; and
• to follow a rigorous and scientific approach.

This all comes together very practically in the execution of our research. While the members of the research team drive the process, we set up task teams for each research project made up of members of the research and portfolio management teams, and we include experts from other areas, such as technology or climate, when needed…

This ensures that we involve a diverse blend of skills and perspectives and that we incorporate practical considerations from the start. Each task team has clear deliverables and deadlines and meet regularly to ensure we maintain momentum in our research. 

The investment industry is evolving rapidly, with many developments taking place at the heart of quantitative investing. 

We are excited about the future of quant investing at RBC. 

We will continue to highlight the ways in which we are evolving our models and investment processes as we leverage these opportunities, to ensure that we remain well-positioned to deliver exceptional investment outcomes.