Dark Pool DNA: Improving Dark Pool Assessment

Abstract

This empirical study based on 517,000 observations from 17 dark pools used by ITG trading systems over a one-month period shows the value in predicting execution quality by fill type rather than simply by dark pool. Using the granular approach in this study, one can better identify low-quality trading situations to avoid, without the opportunity cost of cutting out an entire market center. A simulated trading strategy using the conclusions in this research is compared with the typical approach for improving market quality. The simulated strategy has better access to liquidity with lower adverse selection and a better mix of likely contraparties. Given the availability of complex order types in modern dark pools, it is possible to constrain orders ahead of the trade such that they may fill only in a certain way. This means that traders and algorithm designers can make practical use of this research to improve market quality for institutions.