Q's for quants: A chat with our quant equity managers

Vanguard Quantitative Equity Group (QEG) plays a special role in that effort. Buzzing with data scientists, doctors of math, and engineers, this group has been among the first at Vanguard to incorporate cutting-edge technologies such as artificial intelligence (AI) and machine learning in the pursuit of market-beating results. This has created a wealth of ideas for the entire company, enabling Vanguard to drive systematic analysis, continually invest in technology and people, and develop innovative trading strategies and investment ideas, all while keeping costs low.

The benefits ultimately flow to our clients, said John Ameriks, head of QEG. “People always ask, ‘What is the secret sauce of quantitative equity investing at Vanguard?’” he said. “A lot of it has to do with being embedded in a much broader organization that has unmatched investment capabilities.”

In this Q&A, Ameriks and Sharon Hill, head of global and income active equity and co-manager of Vanguard Equity Income Fund, discuss why Vanguard’s conviction in quantitative equity investing and our approach, which benefits from our low costs and long-term focus, stand out from the competition.

Ameriks: Historically, I don’t think the industry has done a great job of explaining active. We’ve tended to focus on contrasting active and passive like it’s some kind of horse race or wrestling match. For an individual investor, active is not the opposite of indexing—it is everything else other than indexing. We should focus less on the “active” or “passive” label and focus more on questions like “What is the strategy? What is the philosophy that a manager is pursuing to try to add value? As an investor, do I believe that philosophy is sound and belongs in my portfolio? How much risk and emphasis on that strategy is right for me?”

Active strategies may have a role in many investors’ portfolios alongside different forms of indexing. In some ways, Vanguard investors may be particularly well-suited to use and benefit from active strategies, because we know from our data that our clients are disciplined and patient. They may be more likely to stick with their investments, even in down periods, which may make them more likely to succeed with active funds.

Hill: Active can also be an opportunity for investors to act on specific investment ideas or themes. Among our strategies are factor funds and dividend income-oriented funds, which are for investors who want to have exposure to factors like value or quality or stocks with high dividend yields. For example, Vanguard U.S. Value Factor ETF provides a strong tilt to the well-understood premium associated with stocks that are cheap relative to the market. These funds provide a more focused tilt than would be possible with a typical index fund.

Ameriks: This goes back to Jack Bogle, our founder, as well as Gus Sauter and the other architects of Vanguard’s philosophy. When they started QEG more than 30 years ago, they had conviction that quantitative management could be an effective way to add value over the long term using our established low-cost, disciplined investment philosophy. They believed that we could leverage our infrastructure, disciplined process, large datasets, and technology to identify patterns likely to generate persistent alpha for investors—helped by the fact that we could do all of this at very low cost.

We’re able to conduct rigorous fundamental analysis across a global universe of securities because we have a systematic process. In contrast, traditional “fundamental” active portfolios have humans looking over stocks one by one. This can lead to concentrated portfolios, which can limit capacity, as well as narrowness of focus that increases risk. Our quantitative strategies offer a way to give the advantages of active investing at scale and with a breadth of holdings.

Hill: A key component of our edge is that we are part of Vanguard. We have technology, investment expertise, access to specialists in risk and trading, and a global footprint well above what a typical quant manager would have.

The other thing that differentiates Vanguard from other active managers is our culture of patience. Like many managers, we have experienced protracted periods of underperformance that some call “quant winters.” There were shops that went under during or after these episodes. At Vanguard, we have that long-term vision, patience, and understanding that if we do what’s right for investors—if we oversee our active managers to make sure that we’ve picked the right ones and that they are doing what they say they’re doing—we’re going to stick with it.

Ameriks: Yes, and the structure of our company is another advantage. We don’t have outside shareholders like most firms do, so we can focus squarely on meeting the investment needs of our clients and allow patience to pay off. 

Ameriks: I just got back from Silicon Valley with the rest of the investment management team. It was absolutely amazing to see up close where the cutting edge of this tech is and where it is headed. AI allows us to incorporate more information from more sources than ever before to help us evaluate what a good security looks like relative to its industry peers. Right now, for example, we’re using these tools to help us do what quants call “contextualization,” meaning we are using the tech to interpret a large set of macroeconomic patterns to give us more insight into the relative attractiveness of the same fundamental themes and signals that we’ve used for decades before AI came along. For example, while we have always used technology and data to help us identify and compare stocks with high dividend yields, we can now use AI to help us analyze how those comparisons should change as interest rates and other macro factors change.

Hill: One small but fun way AI has changed my day-to-day is the new generative AI tools available to help us program; we’ve all become more efficient programmers because of them. More importantly, AI has enhanced the way we combine factors in our models and incorporate nonlinear interactions. The use of machine learning to model stock returns isn’t necessarily new—quants have been doing it since the late 1980s—but these algorithms have gotten so much more available and accessible. They run faster so that we’re able to incorporate them into our models on a daily basis. In the past, that was harder to do at scale in a production environment.

Ameriks: People sometimes jump to the idea that quantitative investing involves a bunch of algorithms, high-speed computing and trading, and impenetrable equations. We fight that a lot. There are some quantitative funds that run that way, but our human managers are essential to what we do. We like to say that our “computers” have families, hopes, and dreams.

Hill: Another common misconception is that it’s all formulaic. For example, if you pull up the model we use for dividend-paying stocks, you’ll see a factor that measures how a company’s dividend has changed over the last 10 years. You might ask, “Why 10 years? Why not 11? Why not 9?” We look at the data, but we are making many subjective decisions, testing them like scientists and asking things like, “What is the sensitivity of the time period?” That’s where quantitative active management is an art as well as a science. 

Ameriks: These funds require a lot of investment knowledge, so they require more of a hands-on approach, either on the part of an advisor that’s working with a client, or on the part of an individual investor. I’m excited about all the new tools and approaches that can help people make these decisions. As these tools get better, hopefully there’s a broader and broader audience for more sophisticated, more tailored portfolio construction that will help more investors better achieve their goals.