Inferensys

Glossary

Realized Spread

The actual revenue a market maker earns from a round-trip trade, calculated as the difference between the execution price and a future midpoint price, net of adverse selection costs.
Accountant reviewing ASC 606 revenue recognition automation on laptop, financial data visible, casual office setup.
MARKET MAKER PROFITABILITY

What is Realized Spread?

The realized spread measures the actual revenue a market maker captures from a round-trip trade after accounting for the adverse price movements caused by informed counterparties.

The realized spread is the difference between the execution price of a trade and the midpoint price observed at a future time horizon, typically five minutes later. It quantifies the net profit a liquidity provider earns after the market price has adjusted to reflect the information content of the trade, isolating the compensation for providing immediacy from losses to adverse selection.

A positive realized spread indicates the market maker successfully captured the effective spread without being picked off by informed flow. If the realized spread is negative, it signals that the trade was toxic—the counterparty possessed superior information, and the subsequent price movement erased the initial spread revenue, resulting in a net loss for the liquidity provider.

DECOMPOSING MARKET MAKER PROFITABILITY

Key Characteristics of Realized Spread

The realized spread isolates the revenue a liquidity provider actually captures after accounting for the information asymmetry embedded in the trade. It is the definitive metric for separating genuine market-making profit from adverse selection losses.

01

The Core Calculation

The realized spread is formally defined as 2 * d * (P_exec - P_future) for a round-trip trade, where d is the trade direction (+1 for a buy, -1 for a sell).

  • P_exec: The price at which the market maker's quote was hit or lifted.
  • P_future: The midpoint price at a specified future time horizon (typically 5 minutes).
  • A positive realized spread indicates the market maker earned revenue after the price moved back toward the fundamental value.
  • A negative realized spread signals adverse selection—the price moved against the market maker and did not revert.
5 min
Standard Future Horizon
02

Adverse Selection vs. Revenue

The realized spread decomposes the total effective spread into a revenue component and a loss component.

  • Effective Spread: The total double-sided cost paid by a liquidity taker, calculated as 2 * d * (P_exec - P_midpoint).
  • Adverse Selection Cost: The portion of the effective spread lost to informed traders, calculated as Effective Spread - Realized Spread.
  • A wide effective spread is meaningless if the realized spread is near zero; it implies all potential revenue is being lost to toxic flow.
  • Market makers optimize for a high ratio of realized spread to effective spread.
0%
Realized Spread When Fully Toxic
03

Future Price Horizon Selection

The choice of the future time horizon for measuring P_future is critical and non-trivial.

  • 5-Minute Horizon: The industry standard. It balances capturing mean reversion against introducing noise from new information events.
  • 1-Minute Horizon: Captures very fast mean reversion but may miss slower inventory effects.
  • Trade-Time Horizon: Uses the midpoint at the time of the next trade, linking the realized spread directly to the next market event.
  • Selecting a horizon that is too long introduces fundamental risk, contaminating the realized spread with price moves unrelated to the original trade's information content.
1-30 min
Common Horizon Range
04

Inventory Risk Interaction

The realized spread is not a pure measure of information asymmetry; it is entangled with inventory risk compensation.

  • After executing a trade, a market maker holds a non-zero inventory position that is exposed to price volatility.
  • A portion of the realized spread compensates the market maker for bearing this inventory risk until the position is unwound.
  • To isolate pure adverse selection, researchers often control for inventory risk using a cross-sectional regression of realized spreads against inventory holding periods and volatility.
  • In highly volatile markets, a positive realized spread may reflect inventory risk premiums rather than a lack of informed trading.
Inventory + Info
Dual Components
05

Empirical Benchmarking

Realized spreads vary systematically across market capitalization and trading volume.

  • Large-Cap Stocks: Typically exhibit small but consistently positive realized spreads due to high competition and low information asymmetry.
  • Small-Cap Stocks: Often have wider effective spreads but a lower percentage realized, as informed trading is more prevalent.
  • Exchange-Traded Funds (ETFs): Generally show high realized spreads relative to effective spreads because the underlying basket arbitrage mechanism rapidly corrects pricing errors.
  • Academic studies (e.g., Hendershott, Jones, and Menkveld, 2011) show that algorithmic trading tightens effective spreads but can compress realized spreads by competing away the revenue.
Large Caps
Highest Realized %
06

Realized Spread vs. Roll Model

The realized spread is the empirical counterpart to the theoretical Roll (1984) model of the bid-ask bounce.

  • The Roll model implies that under pure transaction costs with no information asymmetry, the effective spread equals the realized spread.
  • Serial covariance of price changes is used in the Roll model to estimate the implied spread; the realized spread directly measures it.
  • A divergence where Effective Spread > Realized Spread empirically validates the Glosten-Milgrom (1985) model of adverse selection.
  • This relationship allows researchers to quantify the breakdown of the pure transaction cost assumption in real markets.
Roll (1984)
Theoretical Foundation
SPREAD DECOMPOSITION

Realized Spread vs. Effective Spread vs. Quoted Spread

A comparative breakdown of the three core spread metrics used to analyze market maker profitability, execution quality, and adverse selection costs.

FeatureRealized SpreadEffective SpreadQuoted Spread

Core Definition

Revenue earned by a liquidity provider after a trade, measured against a future midpoint benchmark to net out adverse selection.

The round-trip cost a liquidity taker pays relative to the midpoint prevailing at the time of the trade.

The instantaneous difference between the best posted bid and ask prices in the limit order book.

Primary User

Market makers and high-frequency trading firms measuring true profitability.

Institutional investors and regulators measuring execution quality.

All market participants observing the cost of immediate execution.

Benchmark Price

Midpoint price at a future time (e.g., 5 minutes post-trade).

Midpoint price at the time of order arrival.

No benchmark; purely the posted bid and ask.

Captures Adverse Selection

Captures Gross Revenue

Captures Net Profit

Calculation Complexity

High

Medium

Low

Typical Value for Liquid Stocks

0.02% - 0.10%

0.05% - 0.15%

0.01% - 0.05%

REALIZED SPREAD

Frequently Asked Questions

Core questions about the realized spread, the definitive metric for measuring a market maker's actual profitability after accounting for the adverse selection costs imposed by informed traders.

The realized spread is the actual revenue a market maker earns from a round-trip trade, calculated as the difference between the execution price and a future midpoint price, net of adverse selection costs. Unlike the quoted spread, which is a static snapshot of the bid-ask difference at the time of trade, the realized spread is a dynamic, ex-post measurement. It accounts for the fact that after a market maker provides liquidity, the price often moves against their position. The formula is: Realized Spread = 2 * D * (P_trade - P_midpoint_future), where D is a direction indicator (+1 for a buy, -1 for a sell). If a market maker buys at the bid and the midpoint price subsequently falls, the realized spread will be negative, indicating a loss on that round-trip due to adverse selection by an informed trader.

Prasad Kumkar

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.