Counsel Views – Conjunction function

Stu Morrow:

Hello and welcome to Counsel Views.

Early on in my career as an Equity Analyst with RBC Global Asset Management, I was introduced to the rigours and practical applications of quantitative investing that were used across the firm. We learned early on that by combining quantitative investing with more traditional fundamental equity analysis, that we could improve investment outcomes over a market cycle.

Fundamental analysis at its core is where an individual seeks to determine whether a stock is attractive by analysing the fundamentals of a company. These fundamentals include the company’s growth prospects, position in its industry, profitability, its valuation, and the quality of its management team. And it is common for fundamental analysts to use their judgment in these assessments.

In its purest form, quantitative investing is a systematic approach to equity investing that incorporates large amounts of data to build a range of portfolios. The typical process is to examine data to determine what factors lead the stock to outperform the overall market or a specific benchmark. Unlike fundamental analysis, there’s no judgment day to day; the models do their own thing without emotion. And for some that is the attraction of quantitative investing.

Here with me today to discuss quantitative investing, how it fits into a portfolio, and how the combination of both fundamental and quantitative investing can improve the odds of favourable outcomes for investors, are my two special guests.

First, is Jaco Van der Walt, Vice President and Global Head of Quantitative Investments with RBC Global Asset Management. Jaco leads the Portfolio Management and Research Teams and oversees the Quant Systems Team. He is passionate about innovation in investment excellence and sees these as essential tools to achieve investment goals. He is also a member of the RBC Investment Strategy Committee and has previously held an executive role at one of South Africa’s largest financial service companies, leading the Investment Management office.

And my second guest is Norman So, Senior Portfolio Manager, Quantitative Investments at RBC Global Asset Management. Norman joined PH&N in 2004, where he was part of the Quantitative Research Group, facilitating the development of quantitative resources and research initiatives. He transitioned over to the RBC Quantitative Investments Team in 2011. And among other quantitative equity strategies, Norm is the Lead Portfolio Manager on the RBC QUBE Low Volatility Equity Funds.

Gentlemen, welcome to Counsel Views.

Jaco Van der Walt:

Thank you, Stu.

Norman So:

Thank you.

Stu Morrow:

Nice to have you here.

Jaco, maybe I’ll start with you. And in my introduction, I provided a pretty quick overview of quantitative investing.

So, how do you see the benefits of the approach of quantitative investing?

Jaco Van der Walt:

Stu, your summary captures it well. Like fundamental investing, our thinking is based on sound fundamentals, such as profitability, quality, and so forth. But what makes it different is that we code our investment thinking in quantitative models that are tested and run on data.

Doing it this way has several advantages.

Firstly, it allows us to combine human and machine in our investment process. While machines are much more efficient at crunching big data sets and performing a large number of calculations, we rely on human input to design and drive our process. By doing it this way, we leverage the best of both worlds.

Secondly, by using data, our process becomes empirical. This allows us to test ideas and pick winners based on actual data and quantitative measurement, which reduces subjectivity and adds rigour to our process.

Thirdly, by coding our investment process into quantitative models, it takes the emotion out of it and makes our approach systematic, which helps us to avoid behavioural traps and unnecessary judgment.

It also allows us to scan for the best portfolios across thousands of securities. This adds breadth and allows us to diversify away from the risk in individual securities, and instead focus on the long-run systematic drivers of return, such as low volatility and others.

For these reasons, Stu, we think quantitative strategies are good complements to fundamental strategies and client solutions, since they rely on different investment processes and different sources of return to add value.

Stu Morrow:

Very interesting. Thank you, Jaco. That’s a great addition to the overview.

And I was wondering, when you’re thinking about areas of innovation in quantitative investing, as it’s happening at a rapid pace, where do you and your team spend your days? What’s sort of the focus for you and your teams?

Jaco Van der Walt:

You’re right, Stu. The explosion of data and computing power has played into the hands of quantitative investors, happening quite rapidly. But it also makes ongoing innovation essential.

For us, ESG is one such area of innovation that we’re focusing on. As investors pay more attention to environmental, social, and governance issues, and more ESG data become available, it has the potential to, A, increase risks for securities with poor ESG characteristics, and B, to provide tailwinds to securities with strong ESG characteristics.

On the back of this investment belief, we have integrated ESG into all our strategies. Our current approach is to reduce ESG risk in a way that has minimal impact on our value proposition.

At a security level, we filter out those securities with the poorest ESG scores, while at a portfolio level, we also set a constraint to ensure that our ESG exposures are the same or better than that of the benchmark. This allows us to control for ESG risk with minimal impact on our risk-adjusted returns.

While some have been successful at picking ESG winners in concentrated portfolios, the empirical evidence of long-term alpha from ESG across a broad universe is still quite patchy. The results of recent years are ever encouraging, particularly those out of Europe. And we’ll continue to evolve in our framework as new insights and data become available.

Another example of innovation for us has been in the area of intangible assets and relates to alpha research, which is always important to us. These assets include things like patents, brand names, and computer software. While the use of intangible assets has grown significantly over recent years, these assets are often not recorded on the balance sheet, leading to biased estimates for many financial ratios.

We included intangible assets into our models by computing a proxy based on R&D and SG&A expenses. Our research found that the inclusion of these intangible assets improved the performance of several signals that we use in our profitability and value factors, which have in turn enhanced our ability to add value to clients.

Stu Morrow:

Fascinating and interesting work. It’s such a rapid pace of development, and I’m very, very interested to see how your current approaches to reduce risk using ESG factors and building that it into the process. It’s very important and top-of-mind to our clients. Thank you for that overview.

Norm, can you give our clients an overview of the RBC QUBE Low Volatility Series of Funds that you manage? I mentioned them in your introduction. But maybe you can walk us through those funds that are under your management.

Norman So:

Hi, Stu. Contrary to classical financial theories, academic studies have found that securities with lower risk tend to outperform higher-risk securities.

In these studies, risk is often defined one of two ways. First is in terms of beta to market. That is how sensitive a stock is to market moves.

The second definition of risk is in terms of the historical volatility of stock price. This observed low-risk anomaly, where securities with low risk outperformed those with higher risk, is often explained by investor behaviour biases. And as you mentioned earlier, Stu, our quantitative approach is used to systematically capture this effect.

Our funds are fully invested equity funds with no use of derivatives. We construct them using a two-step numeric optimization process, emphasizing a low absolute risk and low beta portfolio, while tilting toward securities with strong stability, profitability, and quality characteristics.

We tilt towards these three characteristics because our historical backtests have found that securities that score well in these tend to not only have better go-forward returns, but also lower risk as well. This matches our intuition.

For example, a firm that is more profitable than its peers will have more of a financial buffer when the economy starts turning against it. The result is a product for our clients that offers equity-like returns and reduced levels of risk.

In terms of regions, our team manages Canada, U.S., and global low-vol funds. This aligns with academic studies, which have found that, over long periods of time, the low-risk anomaly exists within equities across pretty much all regions and geographies tested.

Stu Morrow:

That’s great. Thanks, Norm.

If we could focus on the low-volatility approach. How has the low-volatility strategy historically performed during periods when we’ve seen equity and bond markets in an upheaval, for lack of a better term? And/or, let’s talk about even today’s environment, a period of rising rates and potentially inflation as well.

Norman So:

Good question, Stu.

First and foremost, the primary objective of a low-volatility strategy is to reduce equity risk and provide secure risk-adjusted returns while doing so. In everything we implement in the model, risk is the primary consideration. As such, the objective of these products are different than most other traditional equity strategies, and this should be taken into consideration when determining the performance of a strategy.

With that said, we do believe that the low-volatility approach is a successful long-term buy-and-hold strategy. In fact, an academic paper from several years ago looked at U.S. data going back to 1929 and found evidence of the low-risk anomaly. That’s 90 years of history, going through all sorts of business and interest rate cycles, and it gives us additional confidence in the long-term efficacy of this strategy.

In our own work, which looks at 25-plus years of data, we have found some interesting observations.

In terms of risk reduction, we find that the low-vol strategy is successful in consistently reducing portfolio volatility throughout a business cycle. That said, volatility reduction is greatest when market volatility is high. Low vol shines most during high-vol markets.

From a return perspective, when comparing to broader equity markets, low vol also shines most when markets fall. Our backtests show that the low-vol strategies offer positive relative returns in virtually every market correction over the past 25 years, offering clients better downside protection compared to traditional equity funds.

How about when markets are rising? The answer is it depends on what’s driving the markets. If rising markets are driven by high beta or junk rally, then our low-vol funds, which have a strong low-beta tilt, will struggle to keep up. But, if rising markets are not driven by high-beta stocks, then the funds will keep up fine and may even outperform on a relative basis.

We’ve seen this over the last two years, since the pandemic market bottom back in March 2020. For the first 12 months, rising markets were driven by very strong high-beta rally, and the low-vol funds had a very tough relative performance.

But since this high-beta rally ended a year ago, around the end of February 2021, low-vol relative performance has been much better. This can especially be seen between March and December last year, where our low-vol funds matched and even beat the broader market indices, despite markets continuing to rise at the time.

We’ve looked at our global backtests over the last 25 years and found that, contrary to popular belief, low-beta stocks outperformed high-beta stocks quite often during rising markets. This occurred in 44% of the years when markets were up, even before accounting for the reduction in risk of low-beta securities. When you combine this with the 100% low-beta outperformance during years market fell, you end up with a pretty strong long-term strategy.

In terms of interest rate sensitivity, our analysis has shown that the low-vol funds typically have averaged around a 0.7 level of beta to equity markets. That also means that the fund’s sensitivity to interest rates and inflation is around 70% the same as the broader equity index. The other 30% of the fund, or where the low-vol funds differ from traditional equity indices, has a positive correlation to bonds.

This makes sense as low-vol funds are typically overweight sectors and securities that have stronger and more stable dividend and earning streams, such as consumer staples and utilities names. Overall, we would classify the interest rate sensitivity with low-vol funds as mostly equity-like, with a small tilt towards bond-like.

Stu Morrow:

Very well said, Norm. Thank you for that. And thank you for the overview for quantitative investing as well, Jaco.

Absolute pleasure speaking with both of you. And thank you for taking the time out of your busy day to speak directly to our clients, and the approach that you bring with that to RBC Global Asset Management.

Jaco Van der Walt:

Thank you very much. It’s a pleasure.

Norman So:

Thank you, Stu.

Stu Morrow:

And to our clients, thank you for taking the time, as always, to listen to our podcasts. As always, if there are any questions about what you’ve heard here today, please contact your Investment Counsellor at any time. Bye for now.