Flows into EM strategies picked up meaningfully in 2025 and 2026, and market performance has been strong, with the asset class once again commanding the attention of investors from institutional to retail. However, the recent rally has not solved the deeper structural issue: emerging markets remain significantly under-owned by both institutional and retail investors relative to their economic weight in the world.

A valuation advantage and a rich investable universe

There is a persistent valuation gap between developed and emerging markets, one that has historically moved in long multi-year waves. EM now offers growth exposure, including to technology, at valuations well below what investors would pay for comparable growth in developed markets.

What makes this opportunity particularly rich for systematic investors is the sheer breadth of the universe. While most investors experience emerging markets through a small set of headline names in the benchmark, a quantitative approach can range across hundreds of individual EM stocks. Every one of these companies, small or large, becomes a potential source of a modest overweight or underweight position. A quant process built to scan thousands of companies, rather than a shortlist of familiar large caps, is structurally positioned to find value where narrower, benchmark-hugging strategies cannot look.

Concentration is real, but it isn’t the whole story

It would be naive to pretend concentration isn’t a genuine issue in today’s emerging market indices. Technology made up more than 40% of the benchmark at the end of June 2026, and three individual stocks accounted for over 30% of the index – a level of concentration that echoes the ‘Magnificent Seven’ phenomenon observed in developed markets. The concentration has created regulatory side-effects, with some stocks growing so large that active funds are constrained by rules limiting the size of the exposure to individual companies, forcing managers into structural underweights regardless of their opinion or level of conviction.

But the more important insight is that not all technology stocks behave the same way. Each occupies a different position in the broader technology and AI value chain, from software platforms to memory chips to hardware assembly, and each responds to different drivers. A systematic strategy with a wide investment universe can diversify across that value chain rather than simply focusing on or, or avoiding, the largest companies.

A model that learns, without losing its foundations

Perhaps the most important message for investors evaluating a quantitative approach to EM is how the model itself evolves. Quantitative investing is, by its nature, built on historical data and back-tested relationships. This invites an obvious question: how can a backward-looking model keep pace with a world, and an asset class, that is changing at an accelerating pace?

The answer is that quant is not a static black box. The 2026 version of Robeco’s EM quant model is only 40% correlated with the 2016 model, even though its underlying philosophy and core definitions have remained consistent. The difference has come from continuous innovation in signal construction, alternative data sources and techniques like natural language processing and machine learning to extract fresh information from markets, before that information advantage is arbitraged away by competitors chasing the same data sets.

This adaptability shows up in real portfolio decisions, not just theory. In September 2024, the model held an overweight position in major Chinese internet platform names as momentum, earnings revisions and quality metrics all pointed positively. When a subsequent price war eroded profitability across that sector, earnings revisions and sentiment turned negative – and the model systematically shifted from overweight to underweight, well ahead of much of the continued pain in those names. Around the same time, it began recognizing a positive turn for Korean memory chipmakers, building an overweight. When circumstances change, the rules-based process forces the portfolio to change with them.

None of this happens without accounting for emerging markets’ distinctive practicalities – governance risk, currency exposure, inconsistent data quality, cross-shareholding structures, and dual listings such as China’s A-shares versus Hong Kong-listed shares, or US-listed ADRs versus local listings. These are the areas where experienced human portfolio managers work alongside the model turning apparent complications, such as listing discounts, into additional sources of value.

For investors weighing whether now is the moment to lean back into emerging markets, the message from Robeco’s quantitative investment team is clear: the opportunity is not just about riding a handful of dominant names back to new highs. It is about accessing a genuinely broad, evolving universe of thousands of companies, powered by a systematic process built to keep adapting as the market itself changes.

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