The performance of conditional orders has been a growing point of discussion as more Alternative Trading Systems now support the order type and nearly every algorithmic provider uses it within their offering.
Gaming and other forms of information leakage are the primary concerns. These concerns stem from the very function of conditionality: two or more potential contras are notified of an opportunity to trade but have the choice to not trade. While there are legitimate reasons why an invitation on a conditional order may not be accepted (aka firmed up), the reason is not disclosed to the ATS or to the counterparty.
In the following report Liquidnet attempts to explain the mechanics of the conditional order and analyze the performance of two scenarios. First, the liquidity pool looks at the unfilled and filled conditional orders across several strategies within its two Alternative Trading Systems. Second, they compare the performance of orders resting only at Liquidnet with orders resting in multiple pools (through its Liquidnet Dark aggregation strategy). The statistics cover Q1 2018.
The key findings are as follows:
Interactions with at least one manual counterparty were 1.5x times larger than interactions between two conditional algo orders. We believe this is largely due to the loss of optionality resulting from committing shares to any algorithmic strategy.
Conditional orders represented only at Liquidnet outperformed conditional orders within a dark aggregation strategy by 75% when measured against the impact adjusted arrival price. However, the dark aggregation strategy had nearly twice the fill rate.
External algorithmic strategies2 responded to a firm-up request nearly 97.7% of the time when the contra was a manual trader; compared to 84.8% when the contra was another algo.
The mid-price movement one second after one party fades on a conditional interaction is equally distributed across three categories: Favorable, Neutral and Negative. The mid-price movement after five minutes is nearly equally distributed across Favorable and Negative.
