Jay Mehta is COO of Seldon Capital, advising hedge funds on capital raise, ops, hiring & trading.
In 2016, Netflix launched globally, extending the platform to 130 countries, including Indonesia. With 250 million people, a rising middle class and surging mobile penetration, on paper, it may have looked like a perfect match.
But within weeks, Netflix was blocked. Indonesia’s state telecom pulled the plug due to unresolved regulatory issues, citing censorship concerns. Local competitors moved in and positioned themselves strategically.
For active investors, situations like this can provide an opportunity. In markets where data is sparse, non-standardized or simply doesn’t exist, I’ve noticed algorithms are more likely to miss important signals. They can’t flag local cultural dynamics or political sentiment buried in off-platform discussions. That’s the reality of many emerging markets and, increasingly, of small-cap niches in developed ones.
How Algorithms Struggle With Sparse Data
In U.S. large-cap equities, algo trading works because the inputs are rich and reliable. Financial statements typically follow generally accepted accounting principles (GAAP), and analyst coverage is deep and widely distributed. The inputs are clean, and the outputs can be trusted, turning systematic signals into a consistent edge.
Step outside the S&P 500, and the algorithmic advantage weakens. Emerging markets, frontier economies, as well as small-cap stocks, all present exactly the conditions where algorithms struggle.
Sparse coverage on top of non-standardized data and latency lead to poor output. Introduce currency volatility, political risk and local regulatory changes, and the layers of complexity multiply—none of which are easily quantifiable. Without consistent inputs or historical context, algorithms have a hard time generating reliable signals, and success begins to hinge more on qualitative input.
Why Emerging Markets Stay Hard To Read
By definition, emerging markets start under-explored.
Consider Indonesia again. While algorithms can track Jakarta’s listed companies, without the data, they miss the thousands of private enterprises that drive the country’s real economy. According to LSEG’s analysis, the lack of data and consistency in emerging markets remains the biggest barrier to sustainable investment adoption. Data scarcity can stem from language barriers, to non-standardized disclosure requirements, to relationship-based business practices and limited digital infrastructure.
Finding reliable information and uncovering reliable insights in Vietnam’s mid-cap sector, or India’s regional manufacturers, often means conducting site visits and speaking directly with suppliers. That boots-on-the-ground work is expensive and time-consuming—precisely why few investors do it, and why those who do often find themselves with a significant information advantage.
Look at 2023 PMI data: While developed economies dipped into contraction, several emerging markets held firm above 52.7. But many models missed that divergence until it showed up in lagging quarterly data.
Of course, this information asymmetry won’t last forever. As seen with China’s decade-long digital transformation, once the information gaps close, often so do the opportunities. But the cycle renews itself. As one market becomes efficient, new pockets of inefficiency emerge elsewhere, often in smaller or overlooked segments.
The Small-Cap Parallel
Even in developed markets, small-cap stocks share many of the same characteristics as emerging economies. They’re often underfollowed and influenced by local conditions that don’t show up in macro data and consequently can’t be efficiently modeled.
Wellington Management notes that nearly 85% of small-cap stocks have fewer than 10 sell-side analysts. Only 6% of large-cap stocks are that underfollowed. This kind of information asymmetry is dramatic and persistent.
Where Active Investors Still Win
What makes these markets difficult for machines is exactly what makes them attractive and viable for active investors. Success depends less on processing more data and more on sourcing the right data before it’s priced in. There are a number of ways to find these gaps in practice.
1. Scouting For Low-Algorithm Zones
Frontier markets and under-analyzed sectors are less algorithmically saturated or may not offer sufficient data for automation. These can be opportunities for bottom-up analysis and traditional security selection. I’ve noticed sectors or geographies where data is inconsistent or unstructured and are less likely to be efficiently priced.
2. Keeping Cash Ready
Some investors find success in developing conviction in multiyear trends that algorithms miss while chasing daily momentum. When machines sell during liquidity crunches or risk-off events, active managers with conviction may find opportunities to step in. Algorithmic forced selling can create attractive entry points, provided the investor has done the work ahead of time.
3. Owning A Niche
In opaque environments, knowing how a business operates on the ground often matters more than its last reported EBITDA margin. This frequently means speaking the language, both literally and figuratively.
4. Mining Alternative Signals
Key insights often surface in channels that algorithms can’t parse, such as:
• Permit and zoning filings: Local government databases may reveal expansion plans months before companies announce them.
• Job postings and hiring patterns: Workforce expansion can signal new capacity or growth initiatives.
• Public comment windows: Regulatory bodies often require disclosure for key events like environmental reviews or tax rulings, providing free forward guidance to those paying attention.
• Patent filings and R&D partnerships: IP filings and R&D collaborations often indicate a company’s direction ahead of commercial results.
Zooming In When Everyone Zooms Out
With passive strategies dominating and capital chasing standardized signals, I’ve noticed fewer investors are doing the primary research. That’s a distinct advantage and an opening.
As systems digitize and markets converge on uniform inputs, capital allocation often becomes more correlated and more reactive. When algorithms are trained on the same datasets, they tend to behave similarly, especially during periods of stress. This creates feedback loops where volatility spikes, liquidity evaporates and assets are mispriced in both directions. In that environment, information that doesn’t flow through conventional channels becomes more valuable.
Structured data now defines the boundaries of market efficiency. Investors able to work beyond those boundaries could be more likely to uncover overlooked value. And for now, much of the investable world still sits outside the algorithmic map.
The information provided here is not investment, tax or financial advice. You should consult with a licensed professional for advice concerning your specific situation.
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