Quantitative investing has entered a new phase. While systematic investing has been part of global markets for decades, the environment in which quant strategies operate today can feel distinctly different from even five years ago: markets are more volatile, policy shocks are more frequent, and artificial intelligence is reshaping how investment teams work.
At the same time, institutional investors are increasingly focused not just on returns, but on managing risk, resilience in stress periods, and transparency in decision-making.
In this evolving landscape, what does quant investing look like in 2026? How much of it is driven by machines and how much by human judgment?
A changed data landscape, familiar market dynamics
From a market perspective, the current environment has similarities to the past. Periods of market concentration, high valuations of leading companies, and narrative-driven trading are not new. We saw similar dynamics during the dot-com bubble, the global financial crisis, and more recently through pandemic-driven market volatility.
What has changed is not the nature of markets, but the tools investors have to navigate them.
The biggest shift in quant investing has been access to far richer, more complex, and more diverse datasets 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 vast quantities 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 quant toolkit, allowing investment teams to process and analyse information that was previously inaccessible or too costly to use at scale.
For example, we recently incorporated global patent filings into our investment process — a dataset comprising 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 might translate into future earnings growth.
Crucially, this is not about replacing fundamental analysis — it is about enhancing it. AI helps us better understand what companies are doing, but investment conclusions must remain grounded in economic reality.
AI as an accelerator, not a stock picker
There is a common misconception that AI is now ‘picking stocks’. That is not how systematic investing works for us — and it is not how it should work.
Our approach has always been to build transparent, explainable models — what we call ‘white box’ rather than ‘black box’ systems. We believe we must be able to trace every investment decision back to specific data inputs and economic rationale. When a stock is added or removed from a portfolio, we can explain exactly what changed in its fundamentals and why that change historically matters.
We believe AI’s role is to speed up analysis, widen the lens, and make it feasible to incorporate new forms of data — not to make autonomous investment decisions.
Human oversight: Essential for three reasons
- Model design: Machines cannot determine how macroeconomic conditions, valuations, and business life 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 poorly constructed. Experience and domain knowledge are critical in mitigating this risk.
In other words, AI enhances the process — humans remain firmly in control.
Systematic investing in a more uncertain world
One area where quant strategies are increasingly valued is managing risk in turbulent markets. Investors today are less fixated on headline returns and more concerned with how portfolios behave during crises. In this respect, systematic approaches can be particularly well-suited.
By design, quant portfolios are highly diversified across companies, sectors, and countries. They aim to avoid overreliance on any single source of risk and instead derive returns from scalable stock selection models applied across global markets.
This structure can help insulate investors from unpredictable shocks — whether geopolitical, regulatory, or macroeconomic. For many investors, this predictability and resilience is becoming just as important as performance.
Part of our job is to help investors ‘sleep well at night’, knowing their portfolios are not overly exposed to unforeseen risks.
ESG: A strength, not a constraint
Another major shift in the investment landscape has been the rise of environmental, social, and governance considerations.
Far from being a burden, these developments often play to the strengths of systematic investing.
We began integrating ESG criteria into our portfolios more than a decade ago, well before it became mainstream. Quant approaches are particularly effective here because they allow us to systematically avoid companies with high carbon emissions or water intensity, while identifying comparable firms with stronger ESG profiles and similar financial attractiveness.
This enables what we call a ‘double bottom line’: delivering both responsible investment outcomes and strong financial returns. ESG is not treated as a trade-off, but as an additional dimension of portfolio construction.
Where human judgment matters most
Despite all the technological advances, human judgment remains central to successful quant investing.
Our primary skill as quant investors is not predicting markets — it is 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, humans still play a vital role in interpreting model outputs, ensuring they align with economic logic and continuously improving the investment process.
Looking ahead: evolution, not revolution
Over the past 40 years, we have remained at the forefront of systematic investing by consistently adopting new techniques — from early neural networks to natural language processing.
The next phase will likely involve even deeper integration of AI, more diverse data sources, and faster computational capabilities. But the core principles remain unchanged: rigorous analysis, transparency, diversification, and disciplined execution.
We focus not on forecasting the destination, but on equipping research and portfolio teams with the best tools available, so they can continue to innovate and deliver for investors.
