This brief article introduces the first in a series of case studies for the practical deployment of the BestX execution analytics software. To receive the full case study please contact BestX at firstname.lastname@example.org. Please note these case studies should be read in conjunction with the BestX User Guide, and/or the online support/FAQs. The focus of this first case study is the identification of outlier trades, and the management of the workflow around identification, explanation and approval of such exceptions. We have explored this topic conceptually in an early article, ‘Red Flags and Outlier Detection’, whereas this case study further explores how to implement the concept in a practical context using the BestX software.
There is a clear consensus across the industry that the key element of any best execution policy is the process, and not necessarily individual outcomes of specific trades. The objective is to implement and monitor a best execution policy, and then refine it over time based on the iterative process of reviewing performance on an ongoing basis. A core component of this process is the identification of trades which are exceptions to the policy to help provide insights into where the policy may need refining. The BestX product allows an institution to ‘codify’ its specific best execution policy, allowing user defined thresholds to specify exactly which trades should be identified as exceptions.
At BestX we have now observed many different use cases for the exception reporting functionality, and not all are for compliance purposes. For example, systematic identification of particular algos that create significant market impact in a chosen group of currency pairs, may be a useful input into refining the best execution policy around algo selection. Common examples of exception reports include:
1. Notification of any trade where the actual spread cost incurred is greater than the estimated BestX expected cost
2. Identification of any trades that breach agreed commission rates to, for example, the WMR mid benchmark rate
3. Identification of trades generating significant pre or post trade market impact
4. Identification of any trades falling outside the trading day’s range
5. For a matched swap portfolio, identification of any trades where the cost arising from the forward points exceeds either the BestX expected cost, or a defined threshold
6. Identification of algo trades creating potential signaling risk, or significant market impact
Clearly, identification of outlier or exception trades is a critical component of best execution. It is also essential that when implementing such a process, flexibility is required, both in terms of which metrics you wish to monitor, and also the thresholds you specify for these thresholds, above which defines an outlier trade to your policy. We have learnt at BestX that there really isn’t market consensus or a ‘one size fits all’ approach to defining outliers across the industry. To receive the full case study, please contact us at email@example.com