Small caps: There for the taking

Institutional investors paying for active equity managers always face a trade-off. They must weigh the potential for and the confidence of generating outperformance after active management fees against the certainty of index returns at almost zero cost from passive funds. The less efficient and more opaque the market, the greater the opportunity for active managers to shine. 

So, if the potential for outperformance among all equity sub-divisions was ranked, which one would come out on top? “Emerging market small caps for sure,” says Gavin Smith, head of equity research at QMA. Few would argue against him.

For quant managers such as QMA, focused on factor investing approaches, a key question with emerging market (EM) small caps is whether any of the traditional factors are applicable. The evidence appears to be that they are, and this not only comes from global equity managers but also from local country managers. 

India’s Union Mutual Fund, for example, found in its recent research, that the widely used developed-market factors of growth, quality, value and momentum can all produce outperformance. However, the forward-looking prospective data which involved incorporating fundamental analysis and human intervention delivered consistently better results than purely historical data analysis. Increasingly, both quant managers and fundamental managers such as Union Mutual, are exploring ways to supplement historical financial data with forward-looking analysis to create a ‘quantamental’ approach.

What many quant firms seek in EM small caps, in addition to the traditional factors, is a combination of an illiquidity premium and a small-cap premium. However, Warren Chiang, head of quantitative EM equity at GMO, points out that they are often confused. There is, he argues, a group of EM stocks that are large but illiquid and another that is small but liquid. This disparity is a function of the fact that many firms are closely held or family-controlled. “What you have is a weird phenomenon in EM equities where you have an illiquidity premium that isn’t necessarily reflected in small-cap stocks,” he says. 

GMO finds that trading volumes in the EM large-cap universe of about 1,300 securities is less than $10m (€8.5m) a day for the 400 lowest-traded stocks, while comparable figures for developed markets would be 10 times larger. Most managers, Chiang says, are buying EM small-caps that are found to be cheaper to trade as they are so liquid. “You are not getting the illiquidity premium by buying that group of stocks,” he says. 

In recent years, GMO has adjusted its trading strategy to take account of episodic bouts of liquidity seen in the most illiquid part of the EM small-cap universe. 

But any EM strategy cannot be a purely bottom-up stock selection. A fundamental characteristic of EM small-cap stocks in particular, is their closer correlation with their country performance rather than their global sector. As a result, managers whether quant or fundamental, are occasionally forced to adjust holding sizes in response to issues around country risks and local currency valuations.

RBC GAM, for example, reduced its Brazilian exposures in 2013 as a direct result of currency overvaluation and perceived political risk, says Guido Giammattei, the lead portfolio manager of RBC GAM’s EM small cap equity strategy. He noted that this was an extreme scenario, rather than a common route taken by the asset manager.

Large universe
The problem for EM small-cap active managers is the sheer number of stocks and countries. The MSCI EM Small Cap index has over 1,800 constituents across 27 countries, yet accounts for only 14% of the free float-adjusted market. That contrasts with the main MSCI EM index which covers 85% of the market cap. The full universe of potentially investable EM small caps is closer to 9,000 stocks. 

Providing full coverage of the MSCI EM small-cap universe using a purely fundamental approach would be beyond the capabilities of most firms because of the staff costs involved in undertaking analysis. The only way possible is through having a strong thematic framework to narrow down the universe. 

At Candriam, for example, EM portfolio manager Galina Besedina, has a three-pillar process. The first is ESG; the second is screening-based on trying to identify companies that can show sustainable earnings; and the third essentially identifies market leaders. On top of that, the process focuses on pockets of growth – ‘clusters’ based around developments such as the whole supply chain for electric vehicles and fintech. More recently, it has added a ‘COVID cluster’ encompassing companies that produce vaccines, testing and packaging companies. 

RBC GAM, alongside an ESG focus, uses five themes, says Giammattei. They are domestic consumption, health and wellness, green infrastructure financialisation and digitisation.

Where quant funds have a clear advantage is in providing consistent analysis across an enormous universe of possibilities irrespective of numbers of stocks. 

Quant strategies are, of course, ultimately reliant on the data that can be obtained to create insights on relative valuations. The quality of reporting has increased over the past couple of decades, but GMO’s Chiang says what is important is not data quality, but data relevance, as there is not consistency in, for example, calculating inventory. “The real issue in EM small caps is that reporting standards are so different across countries and the auditing is often not that reliable,” he says. “So companies publish numbers that they believe are correct, but look odd when compared to every single other company in that country or sector.” As a result, even quant firms like GMO rely on industry specialists to check reported data. 

QMA finds that having an overlay of top-down country analysis is particularly valuable in ensuring data quality. There is a lot of noise and errors in stock-level forecasting and if a particular stock in a large industry group like Chinese software services companies looks really unattractive, peer group overlays can help distinguish between what could be an artifact of data errors and the likely reality, says Smith.

It is not just published financial data, though, that quant firms can now use. QMA is exploring the use of artificial intelligence (AI) approaches using natural language processing in large markets such as China. Such analysis, Smith explains, can give insights into whether new products being launched and other key corporate developments are being discussed in a positive or negative manner. 

Strong signals
It can also provide a sense of whether the overall direction is becoming more positive. “This is important because as you start to go down this route, you find increased linkages with real company fundamentals that can be tested,” Smith says. “If the signals produced are strong, we have some insights into how fundamentals are expected to change in the future.”

For investors and asset managers, EM small caps are a potentially huge source of opportunity for active management. But neither relying on historical financial data alone, nor narrowing the universe dramatically by focused thematic approaches, can fully exploit the potential. 

The outstanding managers of the future are likely to be those that can incorporate disparate data into processes that can tackle the whole universe of opportunities on a consistent basis. This will include increasingly important ESG data as well as impact ramifications.