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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.
TOPICS: Exchanges/markets/clearinghouses, statistical methods, performance measurement
Footnotes
I am grateful to Jeff Bacidore, Kathryn Berkow, Phil Pearson, and Jamie Selway for their support, comments and suggestions.
Disclaimer These materials are for informational purposes only, and are not intended to be used for trading or investment purposes or as an offer to sell or the solicitation of an offer to buy any security or financial product. The information contained herein has been taken from trade and statistical services and other sources we deem reliable but we do not represent that such information is accurate or complete and it should not be relied upon as such. No guarantee or warranty is made as to the reasonableness of the assumptions or the accuracy of the models or market data used by ITG. These materials do not provide any form of advice (investment, tax or legal). The positions taken in this document reflect the judgment of the individual author(s) and are not necessarily those of ITG.
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