Small caps, enhanced signals: Our new NextGen ETF

With mega-cap stocks dominating index performance, investors are increasingly looking beyond the obvious – toward the broader ‘long tail’ of opportunity.

While small caps have outperformed large caps over the last 20+ years (see Figure 1), they have been lagging since 2018. However, Figure 1 also reveals that changes in relative valuations between these segments were the main reason for this underperformance.

Historically, small caps have traded at valuation premiums of up to 30% versus large caps. Today, they trade at a 30% discount – a gap not seen over the last 20 years. From an allocation perspective, this creates both cyclical and structural appeal.

“Small caps bring very different return drivers to large caps,” Nick King, Head of ETFs, explains. “That makes them a powerful diversifier, particularly when used as part of a broader equity allocation.” They also tend to be earlier in their growth cycle and less dominated by global macro drivers, making them a complementary allocation alongside large caps.

Structurally, small caps remain one of the least efficiently researched parts of global equity markets. With more than 4,000 stocks worldwide and significantly lower analyst coverage than large caps, the universe combines breadth with informational inefficiency – a powerful combination for quant investors.

“A data-driven approach makes sense given the sheer breadth of the market,” says King. Quant strategies process large datasets, apply consistent definitions, and update signals with discipline, turning the small-cap ‘needle-in-a-haystack’ problem into a repeatable selection process.

As Mike Chen, Head of NextGen Quant, puts it, “As small caps are a very rich and idiosyncratic hunting ground for quant, they are also the perfect environment for machine learning to shine.”

The NextGen Global Small Cap Equities ETF builds on decades of quant research using our range of established and next-generation quant signals as input. And then it takes a step further by selectively applying machine learning and alternative data to capture more complex market dynamics.

Machine learning helps in two key ways:

• Identifying non-linear relationships, where signals behave differently depending on context

• Capturing interaction effects, where combinations of signals matter more than each individually
  

“We let machine learning determine how features should be combined for each stock,” Chen explains. “Some companies are driven more by value, others by quality. The model adapts dynamically.”

This flexibility matters particularly in small caps, where company drivers are more diverse and less correlated. “Machine learning allows us to extract nonlinear, higher-order dynamics that traditional linear models simply cannot capture,” he adds.

While AI enhances stock selection, implementation discipline remains central. The strategy is constructed within clear constraints across sectors, countries, and regions, with controlled turnover and governance oversight. It is designed to remain benchmark-aware while seeking excess returns.

Crucially, the model is fully interpretable. “We don’t see this as a black box. It’s a glass box,” says Chen. “We have tools to attribute performance and understand exactly which signals are driving portfolio decisions.” This transparency extends across both stock-level positioning and portfolio-level outcomes, ensuring the strategy remains explainable for investors and risk teams alike.

Even the most advanced models operate within the limits of their assumptions, which is why human oversight remains integral. “Models are simplifications of reality,” Chen notes. “Portfolio managers monitor assumptions constantly – especially during extreme events like Covid, where market regimes shift suddenly.”

Oversight does not mean interfering with day-to-day model outputs. Instead, it focuses on validating whether the structural assumptions underpinning the model remain intact. This balance preserves the strengths of systematic investing, such as discipline, scalability, and bias reduction, while ensuring resilience during periods of structural change.

The strategy is delivered through an active ETF wrapper, combining a research-driven stock-selection engine with intraday liquidity, transparency, and operational efficiency. From a portfolio construction perspective, the ETF is designed as a diversifying small-cap allocation with controlled active risk.

With tracking error of roughly 3–4%, it sits between enhanced indexing and more thematic exposures on the active risk spectrum, offering structured alpha potential without concentrated bets.

“Investors want innovation that’s usable,” says King. “A strategy that can be explained, fits cleanly into portfolios, and trades efficiently. That’s where the ETF wrapper adds real value.”

He adds: “Small caps are often used as a diversifying sleeve within global equities. Delivering this exposure through an ETF makes it easy to integrate into broader allocations.”