The Advantage Few Can Use

The hidden payoff of low liquidity, and why small investors have the best shot at big returns

Note: This post is about a controlled backtest, not a trading strategy or forecast. The goal is to isolate the effect of liquidity on ranking system performance, not to suggest that 70% annual returns are achievable in practice. In real markets, execution, slippage, and scale constraints matter. I’ll explore those realities separately.

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Small money has a massive advantage. When you're managing limited capital, you can access one of the last remaining structural edges in public markets: the ability to invest in thinly traded, overlooked companies that institutional money can’t buy much of.

This advantage disappears as you move into higher liquidity or larger market caps. The pool becomes crowded, and any remaining inefficiencies tend to get arbitraged away quickly by better-resourced competitors.

I’ve been running systematic models since 2010, and this relationship has held across every variation I’ve tested. Smaller, less liquid names consistently deliver stronger results, and the performance gap is significant enough to reshape how you think about research and portfolio construction.

What follows is a practical illustration of that dynamic: the same systematic approach applied across different liquidity thresholds, producing dramatically different outcomes.

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Setup and Parameters

I ran my core ranking system across five different liquidity tiers, holding everything else constant. Same factors, same weights, same rebalancing rules, the only variable was minimum average daily dollar volume.

The seven tiers:

• $3,000

• $100,000

• $250,000

• $500,000

• $1 million

• $5 million

• $10 million

By holding everything else constant, and changing only the liquidity threshold, it’s easy to see the directional effect: performance improves as liquidity drops.

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Results

Low liquid names don’t just beat out the rest, they crush them. The lowest-liquidity group, stocks trading around $3,000 daily, delivered the strongest historical returns. Each step up in liquidity showed progressively weaker results. By $10 million in daily volume, the system’s effectiveness had dropped off sharply.

Important caveats: There's no slippage or transaction cost modeling here, and execution in the lowest tiers would be difficult or impossible at large scale. The goal was to isolate the effect of liquidity on factor performance. Everything (factors, time period, screening rules, rebalance frequency etc) was kept constant except for liquidity. (Just as a reference point: the best long-term returns I’ve seen from serious investors using real money and a strong ranking system in microcaps top out at around 50% per year)

The system is built for small and micro-caps. But even if you used a ranking system designed specifically for large caps, you'd still see stronger returns when applying it to lower-liquidity universes. It wouldn’t outperform a small-cap model that’s been tuned for that space, but the point holds: the same model tends to perform better as you move down the liquidity spectrum. That pattern shows up regardless of how the system is built.

5 Year Performance by Liquidity Tier
at $3K, $100K, $250K, $500K, $1M, $5M, and $10M minimum daily liquidity

$3k Daily Liquidity:

$100K Daily Liquidity:

$250K Daily Liquidity:

$500K Daily Liquidity:

$1M Daily Liquidity:

$5M Daily Liquidity:

$10M Daily Liquidity:

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Why This Matters

If you're managing substantial capital, your investable universe shrinks dramatically. You're forced into more liquid names, and your edge must come from elsewhere, deeper research, execution speed, or structural advantages that don't rely on market access.

But if you're working with smaller capital (practically speaking, anything under a few million) you have access to parts of the market that big money simply cannot. And that's exactly where the strongest returns concentrate, and where this Substack concentrates too.

This doesn't mean every illiquid microcap is a winner. It means that as a group, these overlooked names tend to deliver superior performance, particularly when systematic methods are applied.

Even for discretionary investors, this insight shapes strategy. You can't research 10,000 companies in depth, you need to narrow the field intelligently. Whether you use ranking systems like I do or use another process, it makes sense to start your search where the probability of finding alpha is highest. You want to fish where the fish are. Liquidity constraints offer one of the simplest and most effective initial filters.

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Liquidity Matters More Than Most Think

The performance spread across liquidity tiers tells the story: 70.6% annual compound returns in the lowest tier, declining steadily to just 22.0% in the highest.

Same exact ranking system. Different slice of the market.

Being small is your primary structural advantage. Lower liquidity isn't an obstacle to navigate around; it's what enables excess returns in the first place. And for those willing to fish in these waters, the opportunity remains remarkably underexploited.

In the next post, I'll highlight a specific example from the extreme end of this spectrum, a tiny, overlooked company trading well below typical liquidity thresholds, demonstrating exactly what this advantage looks like in practice.

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Disclosure:
This post includes back-tested results based on a quantitative ranking system I built for small- and micro-cap stocks. Back tests are hypothetical and don’t guarantee future performance. Actual results can vary significantly due to execution, slippage, timing, and market conditions. This content is for informational purposes only and does not constitute investment advice. Do your own research.