Quantitative investing in 2026: data, AI and human judgment

Quantitative investing has entered a new phase. While systematic investing has been a part of global markets for decades, the environment in which quantitative strategies operate today can feel markedly different than it did five years ago: markets are more volatile, policy shocks are more common, and artificial intelligence is changing the way investment teams work.

At the same time, institutional investors are increasingly focusing not only on returns, but also on managing risks, resilience in times of stress and transparency in decision-making.

What will quantitative investing look like in 2026 in this evolving landscape? How much of that is driven by machines and how much by human judgment?

A changed data landscape, familiar market dynamics

From a market perspective, the current environment shows similarities with the past. Periods of market concentration, high valuations of leading companies and narrative trading are not new. We’ve seen similar dynamics during the dot-com bubble, the global financial crisis, and more recently, the pandemic-induced market volatility.

What has changed is not the nature of the markets, but the tools investors have to navigate them.

The biggest shift in quantitative investing is access to much richer, more complex and more diverse data sets than ever before. Forty years ago, systematic strategies relied almost entirely on structured financial data: earnings, balance sheets and price movements. Today they can integrate large amounts of unstructured information such as text, patents and other alternative data sources.

This has been made possible by advances in machine learning, natural language processing and cloud computing. These innovations have expanded the quantitative toolkit, allowing investment teams to process and analyze information that was previously inaccessible or too expensive to use at scale.

For example, we recently incorporated global patent applications into our investment process – a dataset spanning millions of pages of text. Processing this data would have taken a month just a year ago; today it can be done in about a week. This allows us to gain deeper insight into innovation pipelines and assess how research and development can translate into future earnings growth.

Crucially, this is not about replacing fundamental analysis, but about improving it. AI helps us better understand what companies do, but investment conclusions must remain based on economic reality.

AI as an accelerator, not a stock picker

There is a common misconception that AI is now ‘picking stocks’. That’s not how systematic investing works for us – and it’s not how it should work.

Our approach has always been to build transparent, explainable models – what we call ‘white box’ systems rather than ‘black box’ systems. We believe that we should be able to trace every investment decision back to specific data inputs and economic reasons. When a stock is added or removed from a portfolio, we can explain exactly what has changed in its fundamentals and why that change is historically important.

We believe the role of AI is to accelerate analysis, broaden the lens and make it feasible to integrate new forms of data – not to make autonomous investment decisions.

Human supervision: essential for three reasons

• Model design: Machines can’t determine how macroeconomic conditions, valuations and business cycles interact – that requires human expertise
• Model selection: Choosing between techniques such as neural networks, decision trees, or random forests requires judgment
• Avoiding overfitting: Models that perform perfectly in backtests can fail in real markets if they are poorly constructed. Experience and domain knowledge are critical to mitigating this risk.

In other words: AI improves the process: humans remain firmly in control.

Systematic investing in a more uncertain world

One area where quantitative strategies are increasingly appreciated is managing risk in turbulent markets. Investors today are less fixated on total returns and more concerned with how portfolios behave during crises. In this regard, systematic approaches may be particularly appropriate.

By design, quantitative portfolios are highly diversified across companies, sectors and countries. They aim to avoid overreliance on a single source of risk and instead derive returns from scalable stock selection models applied to global markets.

This structure can help protect investors from unpredictable shocks – whether geopolitical, regulatory or macroeconomic. For many investors, this predictability and resilience are becoming just as important as performance.

Part of our job is to help investors sleep well at night, knowing that their portfolios are not overly exposed to unforeseen risks.

ESG: A strength, not a limitation

Another major shift in the investment landscape is the rise of environmental, social and governance considerations.

Far from being a burden, these developments often play to the strengths of systematic investing.

We started integrating ESG criteria into our portfolios over a decade ago, long before it became mainstream. Quantitative approaches are particularly effective here because they allow us to systematically avoid companies with high carbon emissions or water intensity, while identifying comparable companies with stronger ESG profiles and similar financial attractiveness.

This enables what we call a ‘double bottom line’: delivering both responsible investment results and strong financial returns. ESG is not treated as a trade-off, but as an extra dimension in portfolio construction.

Where human judgment matters most

Despite all technological advances, human judgment remains crucial for successful quantitative investing.

Our primary skill as quantitative investors is not predicting markets; building robust, repeatable models and implementing them with discipline. Once a model has been thoroughly tested, the key is to trust it and avoid emotional intervention during periods of market stress.

At the same time, people still play a crucial role in interpreting model results, ensuring that they align with economic logic and continuously improving the investment process.

Looking ahead: evolution, not revolution

Over the past forty years, we have stayed at the forefront of systematic investing by consistently applying new techniques – from early neural networks to natural language processing.

The next phase will likely bring even deeper integration of AI, more diverse data sources, and faster computing capabilities. But the core principles remain unchanged: rigorous analysis, transparency, diversification and disciplined execution.

We focus not on predicting the destination, but on equipping research and portfolio teams with the best tools available so they can continue to innovate and deliver results for investors.