Currently what are the most common approaches when accessing retail liquidity?
When accessing retail liquidity, we would check the usual sources as for any trade, including agency broker block flows, broker indications of interest, which brokers have traded volume recently, and any potential to trade stock on risk. This would particularly be the case if we wanted to trade quickly.
In the absence of immediate sources, we may place the order in our internally developed retail algorithm wheel. This will agnostically place a UK retail stock with one of the specific algo retail brokers in the wheel and attempt to access retail liquidity within the selected brokers systems, who will also be accessing the retail service provider (RSP) network. Hopefully, this will gain access to retail liquidity which may not be otherwise obvious.
Using retail brokers algo products as above should be a useful tool in the search for such liquidity. However, the challenge for the buy-side is to find out which brokers are more likely to be trading in specific names, and at what times of day retail flows appear.
In an ideal world, this information would be automated into our Execution Management System (EMS), so that the retail wheel itself can either choose or recommend which broker to select to access liquidity in the most efficient manner.
Can platforms be leveraged to access liquidity more effectively?
All of the above refers to UK equity retail flow and the RSP network. We are aware that most major European countries have increasing volume of retail flow, which is currently difficult for institutions to access, or indeed know how to access. This may be due to a number of reasons, for example some brokers may choose to keep retail and institutional flows separate.
As retail volume and especially value increases, it would be useful if these flows were accessible to institutions. Furthermore, for effective and efficient access, it would be useful if this could be traded electronically.
As the brokers in question are possibly smaller regional or national brokers, they may lack the economies of scale or technological capabilities to create similar algo products used elsewhere. Hence this continues to be a challenge to effectively access these retail flows.