Temporary Impact

The defining characteristic of temporary impact is its mean-reverting nature. When a large buy order consumes resting limit orders, it creates a temporary vacuum in the order book. Resilience measures the speed at which new limit orders replenish the depleted price levels. A highly resilient book sees impact decay within seconds; a fragile book may take minutes. This replenishment is driven by latent liquidity providers who step in to capture the artificially wide spread, causing the price to revert toward its pre-trade equilibrium.

Temporary impact is the transient cost of demanding liquidity, while permanent impact is the lasting information cost. Key differentiators:

• Duration: Temporary impact decays post-trade; permanent impact persists indefinitely.
• Cause: Temporary impact stems from order book inventory imbalances; permanent impact arises from signaling private information.
• Functional Form: Temporary impact is typically modeled as a linear or concave function of participation rate; permanent impact follows the Square Root Impact Law.
• Reversibility: The temporary component fully reverses, contributing to the realized spread of liquidity providers.

In the Almgren-Chriss framework, temporary impact is modeled as a function of trading speed. The model decomposes total cost into:

• Permanent Impact: γ * σ * (X/T) — proportional to average trading rate.
• Temporary Impact: η * σ * (X/T)^β — a power function of trading intensity. Where η is the temporary impact coefficient, σ is volatility, and β typically ranges from 0.5 to 1.0. This separation allows optimal execution algorithms to explicitly balance the trade-off between urgency cost (temporary impact) and timing risk.

Temporary impact reflects the price elasticity of latent liquidity. When an algorithm increases its participation rate:

• Low elasticity: Small increases in trading speed cause large temporary price dislocations (illiquid names).
• High elasticity: The order book absorbs aggressive demand with minimal transient distortion (liquid large-caps). This elasticity is time-varying and regime-dependent, spiking during macroeconomic announcements or index rebalancing events when market-making capacity temporarily withdraws. Pre-trade models must account for this conditional liquidity to avoid underestimating costs in stressed markets.

Post-trade price paths reveal distinct decay signatures that quantify temporary impact:

• Exponential decay: Price reverts as e^(-λt), where λ is the resilience parameter. Common in electronic limit order books.
• Power-law decay: Slower reversion observed in fragmented or opaque markets.
• Overshooting: In some regimes, prices temporarily reverse beyond the pre-trade level as liquidity providers overcompensate. Transaction cost analysis (TCA) platforms estimate decay half-lives by comparing effective spread to realized spread over multiple post-trade horizons (e.g., 1 second, 10 seconds, 1 minute).

Understanding temporary impact directly shapes execution algorithm design:

• Schedule front-loading: When temporary impact is convex (β > 1), algorithms should smooth trading to avoid punishing marginal costs.
• Dark pool routing: Shifting flow to non-displayed venues reduces temporary impact by avoiding signaling in lit order books.
• Minimum fill thresholds: Iceberg orders and discretionary limit orders exploit latent liquidity without revealing full size.
• Dynamic participation: Algorithms like POV (Percentage of Volume) adjust child order sizes in real-time to maintain a constant temporary impact profile as market volume fluctuates.

The lasting, non-reverting component of price movement caused by a trade that signals new fundamental information to the market. Unlike temporary impact, this shift persists after the order book replenishes. It is linearly proportional to trade size in Kyle's model and represents the information content of the order flow. A buy order that permanently raises the equilibrium price suggests the market interpreted the trade as informed.

The comprehensive cost of executing a trade, defined as the difference between the decision price (when the investment idea was formed) and the final execution price. It decomposes into explicit costs (commissions, fees) and implicit costs (delay, spread, and market impact). This is the gold-standard benchmark for institutional execution quality, capturing the full friction of translating an alpha signal into a realized position.

An empirical market microstructure model stating that the expected price impact of a trade scales with the square root of the trade size relative to volume. This non-linear relationship means doubling the order size increases impact by only ~41%. It is a cornerstone of modern optimal execution algorithms, allowing traders to model the marginal cost of increasing participation rates without assuming a naive linear impact function.

The rate at which the temporary price distortion caused by a trade dissipates as the order book reverts to equilibrium. This decay is driven by liquidity providers replenishing depleted limit orders and arbitrageurs correcting the mispricing. The half-life of temporary impact varies by asset class and market conditions—highly liquid equities may decay in seconds, while less liquid instruments can take minutes or longer.

The cost incurred when trading against counterparties who possess superior information. A market maker filling a buy order from an informed trader will see the price immediately move higher, forcing them to sell at a loss. This cost is embedded in the bid-ask spread and is a primary driver of temporary impact. VPIN (Volume-Synchronized Probability of Informed Trading) is a real-time metric used to estimate this toxicity.