Markout

Markouts are a versatile tool and can be employed in a variety of use cases. They're widely used in transaction cost analysis (TCA) as a measurement of fill quality. They may also be used for alpha research and for determining optimal order execution strategy.

Markouts are usually calculated on mid-to-mid, trade-to-mid (fill-to-mid), or trade-to-trade (fill-to-cross) basis. They may also be calculated on VWAP basis, as seen in a study on exchange fees by Maggio et al (2020).

They're usually computed at different time intervals and then averaged over multiple occurrences of the same type of event to create a single curve. A cumulative total of markouts over signal or feature values can also be taken to evaluate its predictive power.

Markouts may be measured in different units for normalization. In equities, markouts are often share-weighted or notionally-weighted and measured in units of mils/share or mils/dollar respectively. In derivatives like futures and options, markouts could be measured in units of ticks/contract.

Exchanges often use markout curves to measure the price improvement that a participant may get on their exchange or from using a specific order type.

Liquidity providers on ECNs may use markout curves to determine the toxicity and adverse selection from price takers.

Markout curves can also help estimate the amount of time that a market maker has to hedge or scratch out of a position.

An agency broker may also use markout curves as a benchmark of execution quality.

Quant researchers sometimes use markout curves to determine the strength of a predictor or alpha signal, or the performance of order placement and cancellation strategies.

See Databento's documentation for an example of calculating markouts to study execution slippage and adverse selection.