Market Impact and Transaction Costs
Why trading moves the price against you, how that cost scales, and how to account for it honestly.
Leads to: 16.4 optimizes a trade schedule against exactly these impact and risk costs.
Learning Objectives
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- Distinguish temporary from permanent market impact and give the mechanism of each.
- State the empirical square-root law of impact and contrast it with linear models.
- Enumerate the components of implementation shortfall (transaction costs).
- Implement a linear temporary/permanent impact cost model and compute total cost.
- Explain why impact makes execution a trade-off rather than a ‘trade now’ decision.
Key Vocabulary
- Market impact
- The adverse price move caused by your own trading; the dominant cost for large orders.
- Temporary impact
- A transient price concession for demanding immediacy that decays after you stop trading.
- Permanent impact
- A lasting shift in the efficient price because trading reveals information (Kyle’s lambda).
- Square-root law
- Empirical rule: impact scales roughly as \(\propto\sigma\sqrt{Q/V}\), sublinear in size.
- Implementation shortfall
- Total cost = execution price minus the decision (arrival) price, across the whole order.
- Participation rate
- Your trading as a fraction of market volume; a key driver of temporary impact.
- Slippage
- Realized execution price minus a benchmark (arrival mid or VWAP).
Intuition & Motivation
Two kinds of impact
Model the price you pay when trading at rate \(v_t\) (shares per unit time) as an efficient price plus a temporary concession:
where \(X_t\) is cumulative quantity traded, \(\gamma\) is the permanent-impact coefficient (a Kyle’s-lambda-like information term), and \(\eta\) the temporary-impact coefficient. Permanent impact moves the reference price for everyone and persists; temporary impact affects only your own fills and decays when you stop.
| Impact | Depends on | Persists? | Mechanism |
|---|---|---|---|
| Temporary | Trading rate \(v_t\) | No (decays) | Demanding immediacy; consuming resting depth |
| Permanent | Total quantity \(X\) | Yes | Information leakage; the market reprices |
How impact scales: the square-root law
A robust empirical regularity across markets is that the cost of executing a metaorder of size \(Q\) over a day scales as
where \(\sigma\) is daily volatility, \(V\) daily volume, and \(Y\) an \(O(1)\) constant. The sublinear square-root shape (not linear) is one of the most reproduced facts in microstructure: trading twice the size costs only about \(\sqrt2\approx1.41\) times as much per the metaorder, so cost per share falls with size - but total cost still rises.
Transaction costs: implementation shortfall
The honest accounting benchmark is implementation shortfall (Perold): compare the final booked P&L to a paper portfolio that transacted instantly at the decision price \(P_0\). It decomposes into:
- Spread / immediacy cost - crossing the bid–ask (16.1–16.2).
- Market impact - temporary + permanent (16.5).
- Timing / opportunity cost - price drift while you wait, plus the cost of any unfilled residual.
- Fees and taxes - commissions, exchange fees, stamp duties, minus rebates.
Interactive: measure slippage as you split an order
Slice the same parent order more finely and watch temporary impact fall while execution stretches out.
Interactive: a linear impact-cost model
- Confusing temporary and permanent impact: only temporary decays when you stop, and only it depends on your rate.
- Assuming impact is linear in size for calibration: empirically it is sublinear (square-root), so linear extrapolation overstates big-order cost per share.
- Benchmarking against the final price instead of the arrival price: implementation shortfall uses the decision price \(P_0\).
- Ignoring opportunity cost of unfilled shares: a ‘cheap’ passive strategy that under-fills can be the most expensive one.
- Quote costs in basis points of notional so they compare across names and sizes.
- Calibrate the square-root constant \(Y\) from your own fills; it is stable but venue/name-dependent.
- Separate alpha decay from impact: if your signal decays fast, paying more temporary impact to trade quickly can be optimal.
- Always report implementation shortfall with all four components, including fees and unfilled residual.
Knowledge Check
Practical Exercise
You must buy \(200{,}000\) shares (arrival price \(\$50\)). Using the linear model with \(\gamma=1.5\times10^{-6}\) and \(\eta=8\times10^{-6}\) (\$ units), (a) compute the impact cost of executing over \(T=1\) day vs \(T=4\) days. (b) Which term changes and why? (c) Name one reason you would not simply pick the slowest schedule.
(a) Permanent \(=\tfrac12\gamma Q^2=\tfrac12(1.5\times10^{-6})(2\times10^5)^2=\tfrac12(1.5\times10^{-6})(4\times10^{10})=\$30{,}000\) (independent of \(T\)). Temporary \(=\eta Q^2/T=(8\times10^{-6})(4\times10^{10})/T=320{,}000/T\). For \(T=1\): \(\$320{,}000\), total \(\$350{,}000\). For \(T=4\): \(\$80{,}000\), total \(\$110{,}000\).
(b) Only the temporary term changes - it scales as \(1/T\) because a longer horizon means a lower trading rate \(v=Q/T\) and temporary impact is proportional to rate. Permanent impact depends on total quantity, which is fixed.
(c) Stretching to 4 days quadruples your exposure to price/timing risk: the stock can drift away from \(\$50\) while you wait, and any alpha in the order decays. Minimizing impact alone ignores this variance - the reason optimal execution (16.4) balances impact against timing risk rather than just trading as slowly as possible.
Lesson Summary
Formula Sheet Additions
- Did I separate temporary (rate) from permanent (quantity) impact?
- Did I use the square-root law for calibration, not linear extrapolation?
- Did I benchmark against the arrival price (implementation shortfall)?
- Did I include opportunity cost of unfilled shares and fees?
Retrieval Practice
Close the lesson and answer from memory before checking. This is deliberate, effortful recall - the single highest-yield study action.
A: Temporary impact is a rate-dependent immediacy concession that decays after you stop; permanent impact is a quantity-dependent, lasting repricing from information leakage (Kyle’s lambda).
A: Impact \(\approx Y\sigma\sqrt{Q/V}\): it is sublinear in order size, so cost per share falls with size - a highly reproduced empirical regularity.
A: Total execution cost measured against the decision/arrival price \(P_0\); it sums spread, market impact, timing/opportunity cost, and fees.
Flashcards
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Completion Checklist
- I can explain the core ideas in my own words
- I worked the derivations/examples by hand
- I completed the interactive workbench(es)
- I passed the knowledge check