The Next Generation of Quant Investing

Today’s investment landscape is radically different than it was even a decade ago. Information moves faster, market leadership can shift quickly and investors must contend with everything from earnings reports and regulatory filings to geopolitical developments, shifting supply chains and rapidly evolving narratives around artificial intelligence.

For investors, the challenge goes beyond finding and analyzing information. It’s also a matter of knowing what to do with it.

As the volume of market data grows, investors are looking for ways to process that information more efficiently and reliably, and they are increasingly considering quantitative approaches to meet that need. For the first time, Quant funds are now the most sought-after hedge fund category, according to a survey of institutional investors collectively managing more than $1 trillion in assets, MarketWatch reported in January.

Along with this growing interest, quantitative investing has evolved considerably. Rather than replacing traditional investment research, many modern quant managers are combining fundamental analysis with advances in computing power, machine learning and natural language processing to evaluate opportunities at a scale that would be impossible through human analysis alone.

“The way we do things is much more akin to a fundamental manager, except we are using computing power to the hilt,” says Arup Datta, Head of Mackenzie Investments’ Global Quantitative Equity Team.

Finding signals in more complex markets

As markets have become more complex, investors face a growing challenge in understanding the forces influencing investment opportunities. Market leadership varies between sectors and styles, and geopolitical developments can alter markets overnight.

Datta believes this environment is precisely why systematic investing has become more relevant today. He argues that systematic quantitative approaches offer the advantage of breadth and consistency, evaluating every company through the same repeatable process.

Rather than focusing on a limited universe of companies, or relying on established assumptions about industries, his team’s quantitative models evaluate more than 20,000 stocks globally every day, ranking companies using a combination of long-term financial characteristics and short-term signals.

Evaluating such a broad opportunity set in a systematic, data-driven fashion can uncover opportunities that traditional fundamental approaches might overlook. During the past several years, for example, quantitative models identified strengths in gold producers and semiconductor companies, even when many active managers remained hesitant because of long-held views around valuation or sector fundamentals.

“Quants have no such preconceived notions,” Datta says. “We look everywhere.” Transparency is also important. “I’ve always believed it can’t be a black box; you need to explain your process to investors as much as possible, so that they understand how you pick stocks, when you may do well and when you may not do well,” he says. “That’s always been my approach.”

An expanded toolbox, thanks to AI

Artificial intelligence is further expanding the modern quant’s toolkit.

Advances in graphics processing units (GPUs), machine learning and natural language processing have dramatically expanded what quantitative teams can analyze, Datta says. Modern models can now interpret corporate filings, earnings call transcripts and other information across multiple languages, extracting insights from data that was previously difficult to analyze consistently.

But Datta is careful to distinguish between using AI as a research tool and allowing it to make investment decisions on its own. “The machine will not do it — I absolutely do not buy that,” he says.

Rather, Datta views AI as a force multiplier for experienced investors — a perspective shared by the majority of traders, according to a Bloomberg Intelligence study. Tasks that once took weeks can now be completed in days, allowing research teams to test more ideas, evaluate more companies and spend more time interpreting results.

“You are still the conductor running the show,” Datta says. “We just have better tools.”

Human judgment remains essential

Technology — even tech as powerful as AI — remains only part of the investment process. For Datta, better tools don’t eliminate the need for experienced investors, but rather make human judgment more accurate and valuable.

Human oversight is a defining feature of his team’s approach, Datta says. The team meets daily to assess whether changing environments merit adjustments to proprietary investment factors, ultimately employing quantitative methodology as the foundation to make better investment decisions.

As markets continue to evolve and react to global events, Datta believes the quantitative investment industry’s competitive edge will continue to be driven by integrating advanced technical capabilities with investment expertise. In a landscape characterized by information overload, the future of quant investing is in the hands of firms that can marry two essential elements: people and technology.