Weighting is the engine that converts an eligible list of altcoins into an index. Two benchmarks can hold exactly the same constituents and still tell different stories because one gives the largest assets most of the influence while the other resets every asset to the same share. Market-cap weighting and equal weighting are the clearest endpoints, but many practical indices use caps, liquidity adjustments, or nonlinear formulas between them. Choosing among these methods is not a cosmetic decision. It changes concentration, turnover, capacity, sensitivity to smaller assets, and the meaning of the return series.
How market-cap weighting works
A market-cap-weighted index assigns each constituent a share proportional to price multiplied by the selected supply measure. If one asset represents 30% of the eligible market capitalization, its starting index weight is roughly 30% before caps or other adjustments. The approach adapts naturally as prices change: a rising constituent becomes a larger part of the benchmark without requiring a trade solely to recognize the increase. This makes market-cap weighting attractive for market-representation objectives. It also places heavy importance on supply data. Total, circulating, and free-float supply can produce materially different weights, especially when large locked allocations or future unlocks exist.
Strengths and limits of market-cap weighting
The main strength is economic scale. Larger assets have more influence because the market values their available units more highly. Turnover between scheduled rebalances can be lower than equal weighting because price moves are allowed to change weights. The main limitation is concentration. A few dominant networks can cause a “broad” index to behave like a small basket. Market-cap weighting can also allocate more to an asset after it has risen and less after it has fallen, although that is a consequence of representation rather than an active trading signal. Supply errors, thin free float, and inaccessible large holders can make reported capitalization overstate practical investability.
How equal weighting works
An equal-weight index assigns the same weight to every constituent at each rebalance. A 20-asset index begins with 5% in each asset. Between rebalances, winners grow above 5% and laggards fall below it. At the next rebalance, the benchmark sells relative winners and adds to relative laggards in order to reset. Equal weighting therefore embeds a systematic rebalancing effect. It gives a smaller network the same initial influence as a much larger one, which can reveal breadth that a cap-weighted benchmark hides. It also makes index performance more sensitive to the reliability and tradability of smaller constituents.
Strengths and limits of equal weighting
Equal weighting reduces single-name dominance and makes the average constituent more important. That can be useful when the objective is to measure participation across a defined set rather than represent aggregate market value. The trade-off is turnover and capacity. Resetting weights creates more activity, and smaller assets may require proportionally larger trades. Equal weighting is not automatically “more diversified.” If every constituent responds to the same risk factor, equal allocations may still be highly correlated. It can also create an unintended small-cap tilt because a modest asset receives far more weight than its share of the eligible market.
Compare the methods across five dimensions
| Dimension | Market-cap weight | Equal weight |
|---|---|---|
| Primary interpretation | Represents aggregate market value | Represents the average selected constituent |
| Concentration | Can be high without caps | Lower at each reset date |
| Turnover | Often lower between reviews | Often higher because weights reset |
| Liquidity demand | More weight in larger assets | More relative demand in smaller assets |
| Data sensitivity | Highly sensitive to supply definition | Highly sensitive to constituent eligibility |
The table is a starting point, not a verdict. A concentrated cap-weighted index can still be the correct benchmark when the market itself is concentrated. An equal-weight benchmark can still be unsuitable when smaller constituents cannot support the implied exposure. The method should be judged against the stated objective and intended use.
Capped and hybrid methods
Many indices use market capitalization for ranking and initial weights, then limit the largest constituent or group. Excess weight is redistributed among the remaining assets, sometimes through several iterations. A square-root market-cap formula compresses size differences more gradually. Liquidity weighting can limit an asset based on observed trading capacity. A blended method may combine market-cap and equal-weight components. These approaches can improve balance, but each adds complexity. The methodology must explain the exact formula, order of operations, redistribution rule, and treatment of a constituent that hits multiple constraints. “Capped” is not a complete description without the cap mechanics.
Rebalance frequency changes the result
An equal-weight index rebalanced monthly can behave differently from the same index rebalanced quarterly because the contrarian reset happens more often. A capped market-cap index can drift above its cap between scheduled rebalances unless the methodology uses daily capping, which adds further assumptions. Faster maintenance keeps weights closer to targets but increases turnover and makes price observations more consequential. Slower maintenance allows larger drift and can leave the index concentrated after a strong trend. The rebalancing guide explains how schedules, buffers, and event rules work together.
A simple public methodology example
An official Coinbase derivatives index methodology describes an equal-weighted multi-asset index and a quarterly rebalance. It is a useful illustration of how a provider connects a weighting choice to a maintenance schedule. The example does not establish that equal weighting is best for altcoins; it shows that “10% each” is only complete when the effective date and rebalance process are also specified.
Choose by objective, not by the best historical chart
For market representation, start with market capitalization and decide whether concentration controls are needed. For constituent breadth, consider equal or nonlinear weighting, then test liquidity and turnover. For a potential product, include custody coverage, venue access, spreads, and the cost of rebalancing. For research, publish both a cap-weighted headline index and an equal-weight breadth index; the gap between them can reveal whether performance is concentrated. Avoid selecting a method simply because it won a back test. Weighting should be defensible before the test period is chosen.
A worked three-asset example
Imagine three eligible assets with market values of 70, 20, and 10 units. A pure market-cap index begins at 70%, 20%, and 10%. An equal-weight index begins at roughly 33.3% each. If the smallest asset doubles while the others are unchanged, it contributes about 10 percentage points to the cap-weighted portfolio before recalculation, but roughly 33 percentage points to the equal-weight portfolio. At the next equal-weight rebalance, part of that gain is sold to restore one-third weights. The example shows why equal weighting is not merely a concentration cap: it changes the economic importance of each move and creates a recurring reset trade.
Capacity should be estimated at the target weight
A smaller asset can pass an eligibility screen yet remain unable to support an equal target weight at meaningful scale. Capacity testing should convert each proposed weight into notional exposure, compare it with order-book depth and normal trading volume, and apply a conservative participation limit. Market-cap weighting often aligns weights more closely with liquidity, but not always; reported capitalization can be large while free float is small. Weight selection and liquidity screening should therefore be tested together rather than in separate spreadsheets.
Conclusion
Market-cap weighting answers, “How did the value of the eligible market move?” Equal weighting answers, “How did the typical selected constituent move after periodic resets?” Neither answer is inherently better. They measure different things. Capped and hybrid methods can bridge the gap, but only when the formula is transparent. The right altcoin index weight is the one that best matches the benchmark’s objective while respecting data quality, liquidity, and maintenance reality.
