Proprietary solutions mask sell-side performance

A lack of standards for a European consolidated price tape and best bid & offer source is impeding the ability of sell-side firms to prove best execution and evidence performance back to the buy-side.
By None

A lack of standards for a European consolidated price tape and best bid & offer source is impeding the ability of sell-side firms to prove best execution and evidence performance back to the buy-side.

Most large sell-side firms, and indeed many high-frequency trading operations, have addressed the lack of a consolidated tape with a proprietary or third-party solution, which is used to power their algorithms, smart order routers (SOR) and make proprietary trading decisions.

But proprietary data solutions are not necessarily the most suitable approach for all brokers. Charles Taylor, partner, execution at UK-based agency brokerage Redburn Partners, notes that inconsistent post-trade data quality prevents his firm from considering a proprietary solution. As a result, Redburn uses Fidessa’s European consolidated tape.

“Problems around the double counting and delayed reporting of trades, as well as the constant updates that would be required after trading venue consolidation, make creating our own consolidated data a complex task,” said Taylor. “Although we do not base our trading decisions on a consolidated tape, we use it to power our SOR, and to evidence to clients that we are participating in the full available volume.”

With almost each broker feeding its SOR and algorithms with data constructed according to a slightly different methodology, buy-side firms have no base measure against which to benchmark algorithmic execution and compare broker performance. For example, two brokers’ SORs might direct the same client orders to different venues with buy-side clients potentially missing out on fills if their broker’s SOR data feed did not include the necessary data. Meanwhile, subsequent reports on SOR performance do not support like-for-like comparisons.

A broad set of basic standards for data consolidation – including guidelines on the criteria for inclusion of trading venues as well as on granularity of information – would help algorithms and smart order routers distribute liquidity to the most optimal venues based on a common methodology as agreed by the industry, and help keep the sell-side to account on their execution decisions.

“The broker community could tweak the agreed standards in accordance with their own best execution policies and the venues they trade on,” comments Steve Grob, director of strategy, Fidessa. “Market-wide standards would become their own self-defining best execution standards as firms would need good reasons as to why they are not adhering to them.”

When devising their best execution policies, MiFID encourages brokers to take price, costs, speed, likelihood of execution and settlement and size into consideration. If a broker decides that any one of these factors should take priority, it could alter the data standards to reflect this.

For example, if a broker decided to trade on a venue that wasn’t included in the universal data standards because it decided the platform offered a better chance of execution there, it would have to explain this to buy-side clients.

“We use our own data to explain to clients the logic of our SOR and algos, such as how we decide what venues to trade on at any one time,” says Brian Schwieger, director of EMEA execution services, head of algorithmic execution, Bank of America Merrill Lynch. “In terms of standardisation across the industry, having a number of different consolidated tapes that don’t work from the same page could affect execution performance. We need to make sure we are all using the same terminology, i.e. when determining what people mean when they talk about transparency of data and what venues would constitute a consolidated tape.”

While most would agree that venues trading over a certain percentage of average daily volume (ADV) – probably around 1-2% – in specific stocks should be included, Grob notes that dark trading also merits consideration.

“If a stock trades over a certain percentage of ADV in the dark, both lit and dark trading in that stock should be included on a data source,” says Grob. “This would give algorithms, which are becoming more and more sophisticated, another level of input to work from.”

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