Quantity over quality?
Quantitative analysis — bolstered by AI — shifting how stock-based funds drive outperformance for all
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It’s not just the ultimate number-cruncher. Super computers — and their proprietary artificial intelligence algorithms — are analysing thousands of reports, news and other word-based documents about tens of thousands of investments.
In the words of a leading, veteran quantitative analyst Arup Datta, based in Boston: “It’s about building a better mousetrap.”
Of course, the trap isn’t for catching vermin. It’s to glean additional market insight — an edge — to eke out a few extra basis points of outperformance.

For at least two decades, that’s been the promise of quantitative investing.
Since the advent of computers, investors have sought to gain an advantage over the rest of the market. Quantitative analysis is the apex of this effort and with each advance in computing power, it has gotten better.
“I’m in my 23rd year doing quantitative equity investing and I have lots of gray hairs from many lessons learned,” says Datta, head of the Mackenzie Global Quantitative Equity Team.
He recently chatted with the Free Press about quantitative analysis — or quant investing — and why more investors should consider it for their portfolios.
First, it does indeed favour quantity over quality — though its goal is much the same as a fundamental investor, who seeks to invest in a handful of high-quality investments.
“My wife is a fundamental equity analyst and I am a quant,” Datta says with a laugh. That has led to spirited debates about which approach is better, and he admits his wife had an edge in the past. “For good fundamental analysis, you go very deep into a name (of a stock).”
You read its quarterly and annual reports. You read analysts’ reports and use metrics like the price to earnings ratio (how much investors are willing to pay in stock price for every dollar of earnings). All of this is to determine a company’s future growth.
Thorough analysis can lead to finding undiscovered gems before other investors turn onto their upside. But quants “go less deep,” Datta adds.
For example, Mackenzie — which has invested heavily in this tech-driven approach — still uses 25 metrics. But it analyzes a universe of 20,000 stocks, ranking them quickly according to those measures.
“One-third of those (metrics) measure value, so what is the price to earnings of a company, compared with its peers?” Datta says.
And is the company undervalued or overvalued, rightly or wrongly?
Another one-third of the metrics analyze companies’ growth prospects. “We look at how a company has been growing and what’s the likelihood it will continue to grow.”
The remaining third of the metrics Mackenzie uses examine the quality of a stock: does it have better free cash flow, for example, than its peers?
“Fundamental investors look at these, too, but quants can do it much more widely and quickly using computer power,” Datta adds.
He notes fundamental stock pickers cannot cover the tens of thousands of companies a computer can.
“In an environment like today, it (computing power) gives us an edge because we can find opportunity almost every day and act on it relatively quickly,” he says. “That’s hard for fundamental managers to do.”
Datta has a track record to back up his argument. For years, he headed up a successful boutique quant investment firm with pension funds and other large institutional investors as clients.
About seven years ago, he came to Mackenzie — one of Canada’s largest mutual and exchange-traded fund firms — intrigued by its plan to put quant investing to the forefront of its stock strategies.
Among the first funds he and his crew of data scientists and computer programmers took over was the Mackenzie Emerging Markets Fund — today, a top-rated Morningstar fund. It’s in the top 90th percentile of its class of funds, and has outperformed its benchmark annually.
They have also led the Mackenzie Global Equity Fund since 2022, which is in the top 99th percentile among all global equity funds analyzed by Morningstar.
“We have an all-weather approach, meaning we can navigate every environment,” Datta says. That involves shifting from the metrics favouring growth when conditions were good — like before U.S. President Donald Trump took office in November — to a quality slant today that favours companies with the best competitive advantages that allow them to maintain and even grow market share amid downturns.
Yet it’s not just the ability to number-crunch. Advances in natural language processing — the backbone of generative AI — enables computers to read financial and news reports that pertain to companies’ future potential.
“If you asked me 30 years ago what the link is between computer science and linguistics, I would have said nothing,” Datta says. “The world has changed.”
A decade or more ago, quants like Datta might have missed out as early investors on “category killers like Amazon and Starbucks,” he adds.
Now, thanks to improved processing power fostering vastly superior analysis, his team was able to latch onto processor chipmaker Nvidia before it took off in price. Equally notable, he was able to reduce exposure to Nvidia and other big tech stocks before they nose-dived in a few months ago.
Now Mackenzie has turned quants loose on other funds in its lineup. And if successful, expect more providers to put more robots to work, looking for an edge.
Just don’t expect hot stock tips, Datta adds.
“Quant is not about telling you about a sexy stock and its performance,” he says. “It’s more about positioning, to capture the right basket of stocks heading in the right direction — a truly diversified, all-weather approach.”
Joel Schlesinger is a Winnipeg-based freelance journalist
joelschles@gmail.com