What is quant investing and why does it matter for your portfolio?

Quant investing has a PR problem. Mention it to most retail investors and they picture an omniscient machine spitting out mysterious numbers without any human interaction, like Rehoboam in Westworld.

According to RQI Investors' Dr David Walsh and Dr Joanna Nash, that picture couldn’t be further from reality. Speaking on First Sentier Investors' Curious Podcast, Walsh explained the misconception and what quant investing actually is.

"Quant investing is not a black box at all. It's the systematic application of good investment ideas," he said. 

"We take good investment ideas - things that work - to try and differentiate between good stocks and bad stocks. And we use data and systems and technology to implement those in the most efficient way that we can."

This human insight is what is given to the model. Dr Nash uses “corporate culture” as an example – the idea that companies with strong culture from the top down tend to outperform because employees are more productive. 

The challenge here is turning something that is qualitative into something a portfolio can be built around.

"This is where some of the newer AI tools help us," Nash said. "We can use natural language processing to see how people speak, to try and draw culture out of that, and see whether the way they speak in their prepared speeches versus when they speak off the cuff are the same or not."

Bets on ideas, not stocks

If quant investing is using human insights to differentiate between good and bad stocks, then what is its distinction to fundamental investing?

"It's about exposures to ideas more than it is about individual stocks," Walsh explains. 

Fundamental investors find a stock they believe will outperform and hold it. Quant managers instead tilt a portfolio towards a broad idea – value, quality, momentum – while actively controlling for risks they don't want to take.

Logically, using many ideas combined does more heavy lifting than a single idea or factor. Since a cheap stock and a trending stock are often not the same, a portfolio built on multiple factors at once smooths out the ride - diversification across multiple drivers of return, not conviction in one.

On the question as to whether quant’s strong run will continue, Nash points to this diversification.

"When people say we're going to enter a Quant Winter again, I don't think that's going to be the case," she said, referring to the period between 2018-2020 which saw many quant funds underperform, particularly those tilted heavily towards value.

"You'll get that consistent performance from every one of those different exposures, which will then give us that strong core performance."

Where AI actually helps – and where it doesn't

The clearest use case for AI in quant investing is processing unstructured information at scale. 

"The ability to read text, the ability to look at unstructured data, credit card transactions, look at visuals and be able to interpret those has opened up all those different data sources to quants," Nash said. 

"These tools have made us sort of become more like fundamental investors in the sense that we can analyse a whole lot more information."

The second is picking up non-linear relationships that older models miss. Walsh's example is where an analyst forecast that's wildly out of step with consensus, for instance, can carry far more of a signal than one sitting near the average.

Looking ahead, Walsh expects AI to increase volatility rather than reduce it, simply because more players can now access similar tools and data.

"There's a lot more ability, and a lower barrier to entry, for other smaller groups to come in and use these tools to exploit ideas they can see without really applying the insight," he said.

Both Walsh and Nash agreed that AI won't replace the people generating investment ideas. 

"The computers can't bring those investment ideas - you still need the people behind them," Nash said.

The part investors tend to skip over

Some of RQI's recent research has been in refining how existing signals are built.

"Previously, we may have looked at four different components, helping to explain, say, valuations. Now with machine learning tools, we can look at 50 or 100 different components," Nash said. 

"What we've found with some of that research is that we get a similar performance from the signal, but we lose the drawdown, which is obviously very, very important."

Her conclusion applies beyond quant funds. 

"You can have 1,000 amazing insights, but if you don't get exposure to them in the portfolio, there's no point having them."

To listen to this episode of the Curious podcast in full, click here.