Institutional Trading

Market Impact Is the Cost of Wanting Too Much of Something

A large order moves the price against itself. That cost usually exceeds commissions and spreads combined, and it is the reason execution is a specialist discipline.

↩ Looking BackPart of the 2020 to 2026 retrospective, written in July 2026. The date below marks the 2021 events this piece revisits, not when it was published, so it draws on everything known through mid 2026.
Nathan Xiang·May 21, 2021

The Cost Nobody Invoices

Trading costs divide into explicit and implicit. Explicit costs are commissions, fees, and taxes, and they appear on a statement.

Market impact is implicit. It is the amount the price moved because you were buying, and nobody bills you for it because it is embedded in the price you paid.

For any institutional order of size, impact is the dominant cost. Optimising commissions while ignoring impact is optimising the small number.

Why It Happens

Two mechanisms, and they are different.

Liquidity consumption. Buying more than is available at the best offer means taking progressively worse prices up the book. This is mechanical and temporary, and the price generally recovers once you stop.

Information leakage. Other participants infer that a large buyer is present and adjust. This is permanent, because the market has updated its view.

The distinction matters for strategy. Temporary impact can be reduced by trading more slowly. Permanent impact is caused by being detected, and trading more slowly increases the time available to detect you.

The Trade Off

Trade fastTrade slow
High immediate impactLower impact per unit
Low exposure to price driftHigh exposure to price drift
Less time to be detectedMore time to be detected

This is the central problem of execution. Trading quickly costs impact. Trading slowly costs risk, because the price may move for unrelated reasons while you wait, and that movement can dwarf any impact saving.

The optimal speed depends on how urgent the trade is, how volatile the security is, and how large the order is relative to normal volume. Formal frameworks exist for this and the practical answer is always a judgement.

The Rough Scaling

Empirical work suggests impact grows roughly with the square root of order size relative to average daily volume, rather than linearly.

That means doubling the order size does not double the impact, and it also means impact rises steeply for orders that represent a large share of normal trading. An order equal to a full day of volume is very expensive to execute regardless of how carefully it is worked.

The practical implication is a constraint on strategy. A fund large enough that its positions represent many days of volume cannot trade nimbly, which limits what strategies it can pursue at all.

How Execution Desks Manage It

Algorithmic execution splits a large order into small pieces distributed over time, targeting a benchmark such as volume weighted average price or a participation rate.

Randomisation matters, since predictable slicing is detectable. An algorithm sending identical amounts at identical intervals announces itself.

Venue selection spreads the order across public books, dark venues, and bilateral counterparties so that no single venue reveals the full size.

And where possible, finding a natural counterparty with the opposite interest avoids impact entirely, which is why block trading desks exist.

The Capacity Consequence

Market impact is why strategies have capacity limits. A strategy generating good returns on 100 million may generate nothing on 5 billion, because the impact of establishing and exiting positions consumes the edge.

This is one of the more reliable reasons that successful funds see returns decline as they grow, and it is a structural constraint rather than a failure of skill.

The Bottom Line

Market impact is the price movement your own order causes, split between temporary liquidity consumption and permanent information leakage. It typically exceeds every explicit cost and scales roughly with the square root of size relative to volume. Managing it is a trade off between impact and price risk, and it is the reason investment strategies have capacity limits.

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