European dark pool volumes continued to grow in Q1 2011, with Rosenblatt Securities Analyst Justin Schack estimating that activity of the 15 pools that he follows reached a record proportion of about 3.5-4.5% of consolidated turnover. Despite the increased volatility which is typically negatively correlated to dark pool activity, Schack observes that the growth and behaviour in dark pools, such as brokers and banks preferencing their own pools prior to orders reaching exchanges and MTFs is consistent with the equivalent period of market development that occurred in the United States. Schack's figures take into account the figures from broker crossing networks that report to him or Markit BOAT and his estimated volume for pools that do not report to either Rosenblatt or Markit BOAT. Dark pool activity will continue to increase as brokers integrate algorithms and streamline their execution facilities. The Tabb Group estimates that dark pools will likely rise to 7 per cent of the consolidated market volume for the major European markets this year.
As part of their best execution responsibilities, buy-side traders are starting to demand greater order-handling transparency and faster more detailed execution analytics. Controlling the cost of execution directly impacts broker's profitability. To increase broker profitability on the execution (commission less trading fees), many brokers (even those that provide agency-only execution) may bias passive orders toward their own pool of liquidity or venues that offer high rebates or zero cost to post.
If the broker selects venues based on cost and only represents the order in one venue, the buyside client may incur an opportunity cost - increased potential for an order to be traded around, thus increasing the risk of inferior average prices and information leakage. Some (not all) brokers will search for additional liquidity in dark pools. When searching for liquidity, brokers may first search in venues with the lowest access fees rather than first going to venues where they have had success and large size executions. This model is best suited for the buyside, but with many brokers claiming an unconflicted model, proof is needed.
Mid Cap Funds, Low Tech Process
The buyside participants that currently face some of the greatest challenges in European equities are mid cap funds, because mid cap stocks represent a substantial portion of dark pool activity. It is not uncommon to see mid cap funds dealing in a universe of European stocks where 50% or even 60% of total activity is traded in the dark. While this could (should) be leveraged to enhance execution performance, it is also not uncommon for these funds to be trading through just a handful of broker dealers, which often results in an extremely manual and highly inefficient trading process.
The fund's traders will often start by placing liquidity into one of its broker dealers' dark pools and then just waiting for a response. If nothing happens, they will then have to retrieve it and resubmit to another broker dealer pool, and so on. At each stage of this iterative switching process there is further leakage of information, which increases the risk of predation, as well as a greater opportunity cost in terms of timing delay.
These funds typically also have little or no quantitative data or analysis available about the relative performance of their broker dealers' pools, which means that the order management process contains a large element of guesswork. Worse still, the fund is completely blind to the opportunities currently available in other dark venues. The impact this has on execution performance is not hard to imagine, so it is particularly in the European mid cap sector that the dark liquidity aggregation and intelligent/silent execution tools provided by Tradebook can deliver both immediate and tangible benefits.
Real-time, venue specific analytics will help the buy-side become much more aware of where they are finding liquidity and therefore help them take more ownership of their order flow. Some progress has been made on this front. Some brokers are providing real-time support for FIX Tag 30, defined in the protocol as the "Market of execution for last fill, or an indication of the market where an order was routed" in execution drop copies.
Largely it is up to the buyside to make sense of the data; few brokers have created analytics to make sense of the data and even fewer have created real-time dashboards. Having that report at the end of the day or week, doesn't help traders respond to the immediate situation. Real-time venue-based analysis of executions enables the buyside to understand where the liquidity really is. Traders can then map and distribute orders more efficiently and ultimately drive down the implicit costs of trading.
High Frequency Impact
High frequency trading (which we define as market making and statistical arbitrage algorithms) is estimated to be about 35-40 percent of European volume. And, while HFT appears to be having a beneficial impact on liquidity and price improvement in the US, more and more buyside firms are asking themselves, first, to what extent are they interacting with it and second, if they are interacting with the flow is there a significant amount of information leakage and corresponding impact.
In the U.S. market, data analysis suggests that HFT is increasing the BBO depth and narrowing spreads and thus, on balance, is contributing to lower transaction costs. In Europe, perhaps the best example of the benefits of HFT was found in the December 2008 study conducted by Matthew Clifton and Mark Snape of the Capital Markets Cooperative Research Centre that examined the effects of the short sale ban of 15 financial stocks in September and October of 2008.
The short-sale ban effectively removed HFT firms from operating in those stocks. Clifton and Snape found that the average spread increased 140% from 15 to 36bps vs. a control group of 78 FTSE 100 stocks whose average spread increased only 56% from 13 to 20bps over the same period. Market depth deteriorated with an average drop of 59% vs. 43% in the control group. Turnover in the short-sale banned stocks dropped 21% vs. an increase in 42% in the control group.
Despite the fact that on balance HFT appears to be positive, there are many predatory algorithms that focus on large order identification and momentum surfing. Of course, "anti-gaming" techniques such as order rotation, price-prediction models, heat-mapping, statistical-based venue liquidity, quality/quantity evaluation/ranking and order representation can seek to counteract HFT. Real-time analytics such as venue-specific analysis shed light on the order handling practices of brokers and the factors underpinning their order-router decisions when directing client orders to particular venues. Bloomberg Tradebook Trade Analytics (BTTA) seeks to provide the buyside with advanced transparency (Figure 1). All venues and activity are specifically identified. Advanced audit trails provide the buyside with a running commentary of the decisions that were made and the different order representation techniques that were used in representing the order. With this data and analytics, the buyside themselves or in a partnership with Tradebook's Execution consultants can map order flow to its proper destination using appropriate technology to seek lower implicit costs through better execution results.
For further information, contact: Gary Stone, Chief Strategy Officer, Bloomberg Tradebook +1-212-617-2297, firstname.lastname@example.org