What Is the Size Factor? The Small Cap Premium and Its Caveats

The size factor is a rule based tilt towards smaller listed companies, measured by market capitalisation. It became a named factor because long run studies in several markets found that portfolios of smaller companies produced higher average returns over some extended periods than portfolios of larger ones. That pattern is usually called the small cap premium.

The honest headline, though, is that the size factor is the most heavily qualified of the classic factors. Later work showed much of the measured premium shrank or vanished once researchers removed the tiniest and least tradeable companies, corrected for companies that disappeared from the data, and subtracted realistic trading costs. Size remains useful as a descriptive control and as a portfolio design choice. It is a weak thing to lean on as a standalone return engine.

What the factor measures

Market capitalisation is the share price multiplied by the number of shares outstanding. It is the market’s price tag on the whole company, and it is the entire input to the size factor.

Most serious implementations use free float market capitalisation rather than full market cap. Free float counts only the shares genuinely available to trade, excluding promoter holdings, government stakes, strategic holdings and other locked blocks. In India this distinction is large. Many listed companies carry high promoter ownership, so full market cap can be several times the amount of stock that actually changes hands. Indian index construction uses free float for exactly this reason, and a size screen that ignores it will systematically mis rank companies. See free float market cap explained.

Notice what the factor does not measure. It says nothing about profitability, growth, valuation, leverage or governance. A company is small because the market says it is small, which can mean a young business with room to grow or an old business that has shrunk. The screen cannot tell them apart.

How the size tilt is built

The mechanics are simple, which is part of why the factor is so widely studied.

1. Define the universe. A listed universe, usually with exclusions for very recent listings and for instruments that are not ordinary equity.
2. Apply a liquidity floor. Screen out companies below a minimum average traded value or minimum trading days. This step is not optional in practice, and it is where most of the difference between an academic result and an investable one lives.
3. Rank by free float market cap. Sort the surviving universe from largest to smallest.
4. Cut into buckets. Common splits are deciles, quintiles or index defined bands. In India the standard bands follow the regulatory classification: the largest 100 companies by full market cap are large cap, the next 150 are mid cap, and everything from the 251st onwards is small cap.
5. Weight and rebalance. Equal weighting expresses the tilt more strongly than market cap weighting, which by construction pushes even a small cap portfolio towards the largest names within the small bucket. Rebalance frequency sets turnover and cost.

Some approaches skip the buckets and use the natural logarithm of market cap as a continuous score. Log scale is used because market caps span several orders of magnitude, and a raw linear score would be dominated by the extremes.

Why a size premium might exist

The proposed explanations are worth knowing because they tell you where the premium is most likely to be fragile.

Compensation for illiquidity. Small companies are harder and more expensive to trade. Part of any measured excess return may simply be payment for accepting that friction, in which case a real investor paying real costs does not keep it.

Neglect and information cost. Fewer analysts cover small companies, filings are thinner, and the work of understanding them is heavier. Underfollowed businesses can be mispriced in both directions, and the reward is for doing genuine work rather than for smallness itself.

Higher fundamental risk. Small companies are typically less diversified, more dependent on a few customers or one product, more sensitive to a funding squeeze, and more exposed to a single key person. A higher average return can be straightforward compensation for a higher chance of permanent loss.

Growth base effects. A small company can double revenue from a modest base far more easily than a large one. That optionality is real, and it is also priced.

Each explanation implies a caveat. If the premium is payment for illiquidity, costs eat it. If it is payment for risk, the risk shows up eventually. If it is payment for research effort, a mechanical screen does not earn it.

The Indian context

India has a deep small and micro cap tail, so the size factor is unusually easy to construct here and unusually easy to construct badly.

Liquidity is the binding constraint. A screen can rank thousands of names, but a portfolio can only be built in those that can absorb an order without moving the price. The gap between a model portfolio and a filled one widens sharply as market cap falls, and it widens further when you try to exit under stress. This is the practical subject of slippage and impact cost and liquidity constraints in backtesting.

Circuit filters and thin trading distort the price series. Names that hit price bands regularly produce return series that do not represent achievable trades.

Data quality is thinner down the market cap curve. Smaller companies file less, hold fewer analyst calls, and provide less segment detail. A factor score that needs several fundamental inputs will have more missing values in the small bucket, and how those gaps are handled changes the result.

Survivorship is a serious trap. Small companies delist, get suspended and go through resolution processes far more often than large ones. A backtest run on today’s list of small caps has already deleted the failures, which flatters the result badly. This is the core point of survivorship bias in backtests and of why point in time data matters.

What it does not tell you, and the drawdowns that come with it

Small is not the same as cheap, good or growing. The factor is one number, and one number cannot describe a business. Size screens are almost always combined with quality or value filters, precisely because raw size is such a blunt instrument. Some research finds the premium largely concentrated in smaller companies that are also profitable and not heavily indebted, which is a different claim from a premium for smallness.

Drawdowns are deeper and recovery is slower. Small cap segments fall harder than large cap segments in broad market declines, and they can stay depressed for years. That is not an anomaly to be explained away, it is the defining characteristic of the exposure. An investor who cannot sit through a fall of a third or more, sustained over a long period, should not hold the tilt at size.

Costs are asymmetric to the tilt. Every basis point of extra spread, impact and turnover falls hardest exactly where the claimed premium lives. A result computed without honest cost assumptions is not a result. See transaction costs in backtests.

The premium is period and definition dependent. Move the start date, change the liquidity floor, switch from equal weight to cap weight, or use full instead of free float market cap, and the measured premium moves materially. When a conclusion is that sensitive to reasonable choices, it should be held loosely.

Long droughts are the norm. Extended stretches where large caps beat small caps are common, and they can run for many years. Factor exposures need to be sized for those droughts in advance rather than reconsidered halfway through one. See factor cyclicality and drawdowns.

The reasonable way to hold the size factor is as a deliberate structural choice about what kind of company risk a portfolio carries, made with eyes open about liquidity and drawdown, rather than as a mechanical route to a premium that the evidence itself treats with suspicion.

Related reading

• Portfolio Metrics Explained: the hub for risk, return and factor measures, and how they connect.
• Factor Investing in India: what factors are, the evidence base, and the Indian market context.
• Free Float Market Cap Explained: why indices and factor screens use free float rather than full market capitalisation.
• Survivorship Bias in Backtests: delistings and failures, and why a study on today’s universe overstates the past.
• Factor Cyclicality and Drawdowns: the long droughts that every factor exposure has to be sized for.

This article is educational. Altys Labs is not a registered research analyst or investment adviser, and nothing here is investment advice or a recommendation to buy, sell, or hold any security.