Emerging market equities outperformed developed markets in 2025 for the first time in several years, prompting investors to reassess the strategic role of the asset class. Yet rising index concentration, both globally and within emerging markets, has made implementation as important as allocation. Swiss-based asset manager RAM Active Investments (RAM AI) has built a €1 billion, benchmark-beating systematic strategy that, since its launch in 2009, has targeted broad, balanced factor exposure to emerging market opportunities while mitigating risks related to liquidity, governance, and market structure.

“Last year marked the first time in five years that emerging markets outperformed developed markets, with more than 10 percent outperformance,” says Nicolas Jamet, Systematic Equity Portfolio Manager at RAM AI. Despite this market rally, emerging market equities remain attractively valued compared with U.S. and other developed markets. The S&P 500’s free cash flow yield has fallen below 3 percent, approaching historical lows.

“Emerging markets offer a rare combination of quality and value.”

Nicolas Jamet, Systematic Equity Portfolio Manager at RAM AI.

By contrast, selective quality exposure in emerging markets offers a compelling alternative: the RAM Emerging Markets Equities strategy currently yields approximately 8 percent, nearly three times the U.S. level. Jamet attributes the compression in U.S. free cash flow yields to the premium investors are willing to pay for anticipated AI-driven growth. “The AI super-cycle is pushing some valuations to extreme levels,” he says. “Emerging markets offer a rare combination of quality and value.”

Improving Macro Backdrop for Emerging Markets

Beyond long-standing demographic tailwinds and valuation support, emerging markets are entering 2026 with improving macroeconomic fundamentals. “Inflation has moderated across many emerging market countries, which has given central banks more room to start cutting rates earlier than in developed economies,” says Jamet. At the same time, the U.S. dollar has weakened after peaking in 2024, a shift that has historically supported capital inflows into emerging markets. “There is still room for further weakening in the coming quarters.”

While the AI investment boom has so far been centered on U.S. companies, emerging markets are also positioned to benefit from the scale of global AI-related capital expenditure. “The first-order impact is clearly in the U.S., but we are seeing meaningful dynamics appearing in emerging markets as well,” says Jamet. “This creates both risks and opportunities for EM.” Much of this impact flows through the semiconductor supply chain. AI-driven capital expenditure is generating demand for advanced manufacturing and memory, benefiting companies in Taiwan and South Korea, including Taiwan Semiconductor Manufacturing Company, SK Hynix, and Samsung Electronics.

“In early 2025, we saw the release of DeepSeek, which demonstrated that China could develop large language models comparable to U.S. and European peers using significantly less computing power.”

Nicolas Jamet, Systematic Equity Portfolio Manager at RAM AI.

At the same time, assumptions that the U.S. would maintain an uncontested lead in AI have begun to shift. Until 2024, markets largely believed that restricted access to advanced chips would permanently limit China’s competitiveness. “In early 2025, we saw the release of DeepSeek, which demonstrated that China could develop large language models comparable to U.S. and European peers using significantly less computing power,” Jamet notes. More recently, additional Chinese models, such as Kimi K2.5, have been open-sourced, reinforcing the view that AI innovation is becoming more globally distributed.

Global investors have been quick to recognize the second-order benefits emerging markets derive from U.S. AI investment, driving substantial capital flows into a narrow group of stocks. The result has been a marked increase in index concentration. “This has created significant concentration not only in the global emerging markets index, but also within regional benchmarks,” Jamet points out. In the MSCI Emerging Markets Index, for example, the five largest constituents now account for roughly 25 percent of the index’s weight, while the same five stocks represent about one-third of total index variance.

Why Does Systematic Investing Fit Emerging Markets?

With emerging markets spanning a wide range of countries, sectors, and companies, the team at RAM AI believes a systematic approach offers clear advantages over both purely discretionary strategies and passive index exposure. While Jamet views discretionary and systematic approaches as complementary, he argues that systematic investing is particularly well suited to the breadth and complexity of emerging markets. “Systematic strategies provide consistency and discipline by removing behavioral biases and ensuring uniform application of investment criteria,” he says. More importantly, they enable access to a much broader investable universe. RAM AI’s strategy typically covers over 4,000 stocks across Asia, Latin America, Eastern Europe, and the Middle East.

“Systematic strategies provide consistency and discipline by removing behavioral biases and ensuring uniform application of investment criteria.”

Nicolas Jamet, Systematic Equity Portfolio Manager at RAM AI.

“For each of these stocks, we rely on roughly 500 alpha inputs,” Jamet explains. “Handling this volume of data simply isn’t feasible through discretionary analysis.” The team processes large amounts of raw data daily from multiple sources, transforming them into structured investment signals. To do so efficiently, RAM AI has built a proprietary deep-learning infrastructure capable of integrating both structured and unstructured data. “This includes daily news flow on all 4,000 stocks, which we receive in text format,” Jamet says. “We use fine-tuned large language models to extract timely investment signals from this information.”

A Disciplined, End-to-End Investment Process

As a systematic manager, RAM AI follows a clearly defined and disciplined investment process, beginning with universe construction. Starting from a broad emerging-markets universe, the team applies strict liquidity and sustainability screens. “This step is especially critical in emerging markets, as it helps exclude companies with weak transparency or questionable financial and non-financial reporting, thereby increasing confidence in the underlying data,” Jamet notes.

From there, the team builds a large set of alpha inputs using only raw, internally processed data. Core inputs are derived from company fundamentals, with country-specific accounting adjustments applied to ensure comparability, such as minority-interest adjustments in Korea or the capitalization of R&D for innovation-intensive sectors. These fundamental inputs are complemented by market-based data, including price action, volume, momentum, volatility, and beta; positioning indicators such as securities lending, borrowing activity and insider transactions; sentiment measures derived from earnings revisions, news flow, and earnings calls analyzed using large language models; as well as sustainability data capturing long-term quality, governance standards, and environmental impact.

Sustainability is fully embedded in the investment process, with around 50 alpha inputs linked to ESG-related factors. These include carbon intensity, environmental impact indicators, governance metrics, and social factors such as diversity policies. “All of these inputs feed into our deep-learning models to help predict future returns,” Jamet explains. Combined with exclusion criteria and engagement efforts, this approach has earned the strategy an Article 8+ classification under the Sustainable Finance Disclosure Regulation. (For further information on ESG, please refer to https://www.ram-ai.com/en/regulatoryinformation and the relevant Sub-Fund webpage).

“For each selected stock, the models generate an alpha forecast that is passed to a proprietary, deep-learning-based allocation and trading engine.”

Nicolas Jamet, Systematic Equity Portfolio Manager at RAM AI.

These alpha inputs feed directly into the stock-selection and portfolio-construction stages. A suite of quantitative models – combining neural networks and decision trees, both style-agnostic and style-specific factor frameworks – systematically select stocks, which are then blended into a diversified model portfolio. “For each selected stock, the models generate an alpha forecast that is passed to a proprietary, deep-learning-based allocation and trading engine,” explains Jamet.

“This engine dynamically determines position sizes by allocating capital to stocks with the highest expected alpha, while explicitly accounting for liquidity, transaction costs, and portfolio risk,” he adds. Rather than relying on large, infrequent trades, the system continuously adjusts exposures by evaluating whether to initiate, increase, reduce, or maintain positions. The result is a highly disciplined, adaptive, and fully systematic investment process.

Portfolio Characteristics and Risk Profile

RAM AI’s systematic emerging markets equity strategy, launched in 2009, typically holds 500 to 600 stocks. The objective is to achieve balanced exposure with strong quality characteristics across sectors, countries, market capitalization and styles such as value, low risk and momentum, while avoiding the concentration risks inherent in passive indices.

“This asymmetry creates a convex return profile, which is a core design objective of the strategy.”

Nicolas Jamet, Systematic Equity Portfolio Manager at RAM AI.

This approach has produced a distinctive return profile, with a beta close to 1 in rising markets and around 0.7 in down markets. “This asymmetry creates a convex return profile, which is a core design objective of the strategy,” says Jamet. While the strategy may lag slightly during highly concentrated, single-theme rallies, it has historically captured the upside while significantly reducing drawdowns during risk-off periods.

A Diversified Alternative to Passive Exposure

Ultimately, the RAM AI team aims to provide investors with a highly diversified, liquid portfolio of around 500 stocks spanning the full breadth of emerging markets. “For investors who do not want to build portfolios country by country or time individual markets, our strategy offers broad diversification with significantly lower idiosyncratic and concentration risk than the index, while maintaining high liquidity,” Jamet concludes. Since inception in 2009, the strategy has delivered compelling performance, while maintaining lower volatility than the benchmark and a stable tracking error of around 5 percent.

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