Why consider factor investing in emerging markets?

This article draws on the paper Factor Investing in Emerging Markets: From Theory to Practice, by Georg Elsaesser, senior portfolio manager; Tarun Gupta, Co-CIO and head of research; Viorel Roscovan, director and senior researcher and Hao Zou, research analyst at Invesco Solutions.

Emerging markets have long offered investors a compelling proposition: faster growth, broader diversification and exposure to economies that are becoming increasingly more important in global indexes. However, they also come with a familiar set of challenges. Country, sector and currency risks can dominate returns, liquidity can be uneven, transaction costs can be high, and market inefficiencies, while attractive in theory, are not always easy to capture in practice.

Broadly, three approaches dominate emerging market equity investing: passive, active, and factor-based strategies. Passive investing is scalable and low cost, yet it often leaves investors exposed to large unintended bets in a few countries, sectors or mega-cap names. Traditional active management on the other hand may exploit inefficiencies but results can vary widely and fees can be high.

Factor investing offers a middle path. It is a transparent, rules-based method of targeting sources of return while retaining more control over portfolio risks. Value, momentum and quality — three widely researched equity factors — have shown meaningful historical return characteristics across emerging economies. In fact, these markets can provide a useful stress test for factor investing because they are typically less liquid, more heterogeneous, and more exposed to macro and political shocks than developed markets.

To analyze factor performance in emerging markets, we construct a comprehensive dataset of investable equities spanning from December 2000 to April 2025. The dataset covers monthly returns, accounting data, and firm-specific characteristics for listed companies across 33 emerging economies (as defined by MSCI classifications). The final sample comprises 4,594 unique stocks with more than 370,000 stock-month observations.

Why are generic factors not enough?

Generic factor definitions used in academic research can often be too blunt for real-world emerging market portfolios and may embed unrewarded country or industry exposures. A simple value screen, for example, may end up overweighting resource-heavy companies. A momentum strategy may tilt toward export-oriented economies or a handful of sectors. These exposures can drive short-term results but are not necessarily the intended factor premium.

Our research instead adopts a three-step framework for constructing value, momentum, and quality factors in emerging markets. Based on the constructed dataset we first define each factor using a single, academically validated signal or generic factor. These serve as benchmarks for the enhanced versions that follow. 

Next, we construct multi-signal factors. Instead of relying on a single measure of value, momentum or quality, we combine several complementary signals within each factor category. Our research finds that this multi-signal design can help reduce noise, smooth out data issues and improve the reliability of the factor definition. This is particularly useful in emerging markets, where accounting standards, liquidity conditions and market structures can vary widely across countries.

The next step is to control for risk by using an enhanced approach. We construct enhanced factors that build on this multi-signal design while neutralizing unwanted exposure to market beta, country and industry effects. The goal is to target the rewarded components of factor premiums and improve their robustness through time rather than letting the portfolio’s results depend on whether a particular country, sector or macro theme happens to be in favor.

Cleaner design, more controlled exposures

For the purposes of this study, we adopt an equal-weighting approach for all three factors to highlight the baseline diversification benefits of combining value, momentum, and quality. Beyond individual factor performance, combining value, momentum, and quality into multifactor portfolios yields substantial diversification benefits. Our research finds that cross-factor correlations are low or negative, especially between value and momentum. An integrated multifactor portfolio can therefore help reduce overall portfolio risk relative to single-factor allocations while benefiting from diversification.

We see that when combining factors using equal weights, Sharpe ratios improve across all construction variants. Figure 1 shows that a generic multifactor portfolio, which combines value, momentum and quality, recorded a Sharpe ratio of 0.84, the multi-signal version recorded 1.32, while the enhanced version recorded 1.43. 

The analysis doesn’t simply consider how to achieve relatively higher returns but also how to build a cleaner portfolio. Enhanced factor construction can help to reduce volatility and eliminate unintended country and sector tilts in the research. That matters because emerging markets are often shaped by shocks that investors may not be compensated for taking such as currency swings and commodity cycles to changes in policy, regulation or geopolitics. Better design can help ensure portfolio outcomes are more closely linked to the intended factor exposures, rather than accidental macro bets.

Implementation is where theory becomes real

The previous section highlights how multifactor portfolios, particularly those constructed using enhanced specifications, deliver favourable performance and cleaner exposures in emerging markets. However, these results are largely theoretical as the resulting long–short model portfolios do not account for real-world considerations that institutional investors typically face, including liquidity, transaction costs, turnover limits, benchmark-relative risk and long-only mandates.

Our research uses a model portfolio framework to bridge that gap. First, a theoretical model portfolio defines the desired factor exposures. Then, an investable portfolio is built to track that blueprint as closely as possible while respecting real-world constraints. We showcase a simulated long-only emerging market portfolio benchmarked to the MSCI Emerging Markets Index (Figure 2).

The generic multifactor strategy delivers a net active return of 0.9%, with an information ratio (IR) of 1.05. The enhanced multifactor implementation achieves a net active return of 1.1%, with a higher IR of 1.30. Volatility remains similar across both portfolios (∼20.3%–20.5%), but the enhanced version achieves relatively higher return with lower tracking error (1.1% versus 1.2%), confirming the benefits of cleaner signal construction and risk control. The enhanced portfolio consistently delivers value added while maintaining tightly controlled and low tracking errors, demonstrating that factor premiums can be harvested without significant deviation from the benchmark, which is key for institutional mandates.

Perhaps most importantly, the enhanced strategy not only delivers stronger performance but also enables more precise and reliable implementation. Roughly 92.3% of the total active return comes from factor contributions and only 7.7% from the residual component compared to the generic implementation which saw factor contributions at 60.7% and residual contributions are as large as 39.3% (Figure 4). In other words, the enhanced portfolio was less dependent on residual unrewarded country and sector risks. 

The takeaway for investors

Emerging markets do not require a completely different factor investing playbook. The same broad principles that work in developed markets — clean signals, diversification, risk control and disciplined implementation — still apply. Yet the margin for error is smaller. Since emerging markets are more heterogeneous and more exposed to unrewarded risks, weak design can quickly overwhelm the intended factor premium.

Our research finds that factor investing is not only viable but also rewarding in emerging markets, offering diversification benefits relative to developed markets. This can be achieved through careful attention to factor construction—moving beyond generic definitions toward multi-signal and risk-controlled approaches that are essential for extracting clean and robust premiums. Finally, implementation matters: Without addressing liquidity, costs, and constraints, theoretical alpha cannot be reliably realized.

Explore the full paper for more detail on the research and methodology.

Investment Risks

The value of investments and any income will fluctuate (this may partly be the result of exchange-rate fluctuations), and investors may not get back the full amount invested.