Why today’s market is considered supportive of systematic investing

Scott Bennett, Director of Invesco Solutions and portfolio manager of the Invesco Global Enhanced Equity Fund joined Zenith’s panel discussion on management styles. Scott shared his insights on the performance of quantitative strategies in the current environment and the role they can play in portfolios. 

The below is the summary of Scott’s contribution to the discussion. Watch the video: Zenith’s Investment Insights panel: Is there a role for each funds management style in portfolios?

Quantitative strategies have performed strongly over the past 12 months, but the backdrop has not always been so supportive. The period often described as the ‘quant winter’, from 2018-2020, was particularly challenging for many systematic and value-oriented managers. Near-zero interest rates contributed to a heavily growth-dominated market in which valuation signals were rewarded less consistently, creating a difficult environment for strategies with meaningful exposure to value.

Today, however, the environment looks markedly different, creating a more supportive backdrop for systematic investing. 

Over the last 18 to 36 months, interest rates have begun to normalise and fundamentals have been rewarded more consistently. In this environment, quantitative managers can also benefit from a disciplined approach to portfolio construction that seeks to strip out unrewarded risks. With markets increasingly influenced by macro risks, from tariffs to conflicts in the Middle East, even small differences in sector or country exposure can lead to large performance differences. For us, effectively managing those risks has helped us deliver a more stable return pattern through this period.

Balancing value, quality and momentum

Many fundamental equity managers have a strong valuation component to their stock selection process and may believe in some form of mean reversion. Valuation is important to us too, but we also look at a range of different factors and drivers of markets, enabling us to build portfolios in a more diversified way.

You don't want to be overexposed to one style, and the benefit of combining different styles is that they are generally well diversified. We think value is important all the time and believe investors should always consider the price they are paying for a particular investment. But ultimately, you want to make sure the investment is not driven by price alone, there should be solid underlying fundamentals, the company itself should be a high-quality business, and it should be growing. Value is always important, but it is equally important to gain exposure to other styles and factors.

Scaling fundamental insights through data and technology

One of the pushbacks against quantitative management styles is that they can be seen as a black box, with a lack of transparency and understanding. But our investment process is fundamentally based. We look for solid economic intuition behind the underlying signals we use. Whether it is value, quality or momentum, these ideas have been around for a very long time. You can go all the way back to Graham and Dodd, who wrote the textbook on investing and intrinsic value. We apply a similar concept in our models.

Everything is fundamentally based, where we have an advantage is that we can scale up our investment process to consider thousands and tens of thousands of securities. What we are really looking to do is scale up those fundamental insights.

AI has been a great enabler for portfolios, but we see its role as being less about idea generation and more about understanding the fundamental drivers we already examine. It can help us better understand the interconnectedness of those factors and drivers, and how they operate not just individually, but together in a more nonlinear way.

Evolving the investment process

As data expands exponentially and new technologies emerge, markets have a way of overcoming inefficiencies. That means quantitative managers must continually adapt and improve their investment processes and the signals used to generate ideas. There is no shortage of new data coming on board or new insights to extrapolate. What we come back to is making sure there is a strong economic link between any signal or data set we use and the relationship we expect it to capture.

One signal we have brought into the portfolio more recently is credit card transaction data. We like momentum and companies with growing earnings profiles and positive analyst revisions. The challenge is that we generally hear from companies every quarter in the US and once every six months outside the US. We therefore look for data that can move us more quickly from a stale, lagged data set to a more contemporaneous one.

We take credit card transaction data on a daily basis and map those transactions to the underlying companies listed on stock exchanges. This gives us a much more contemporaneous view of what is happening with their sales and revenues. Importantly, we still need strong economic intuition, if we can identify companies growing their sales in real time, that may lead to positive analyst revisions, which can ultimately result in positive returns.

How momentum has evolved

Momentum can be a strong driver of underlying returns. The idea is that companies that have done well tend to persist over the next six to 12 months. It is a well-established finding in academic research and was explored by Jegadeesh and Titman around 30 years ago. But the way momentum is understood and measured has come a long way since then.

Originally, the definition of momentum was largely based on 12-month price returns, if a stock had delivered positive returns, it was expected to continue doing well. Meanwhile, if it had delivered negative returns, it was expected to continue doing poorly over the medium term. Today, rather than focusing on prices alone, many of our models also consider the underlying fundamentals of a business, including its earnings growth. We use credit card transaction data to understand the general momentum of a company, as well as natural language processing to assess what analysts and management saying about a company.

We also examine linkages, what is happening with a company's customers? What is happening with its suppliers? It’s competitors? And what is happening with other stocks in its cohort? This helps us assess whether the momentum is genuine and persistent. Momentum today looks very different from momentum 30 years ago, and it will continue to evolve.

It has been a strong period for momentum. Markets have been volatile, but momentum has remained persistent through that volatility. For us, however, the aim is not to rely solely on momentum. We seek to balance its contribution to portfolio risk with two other key drivers: the quality of the business and, importantly, the price we are paying for that exposure.

In summary

The current market environment is proving supportive for quantitative approaches, and the disciplined combination of human insight, evolving data and diversified return drivers can help systematic strategies capture opportunities while managing the risks that increasingly shape market outcomes.