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A-Team Insight Blogs

Smart Order Routers: Transparency Clashes with Broker Proprietary Model

How do you strike a balance between providing transparency into smart order routing so that buy-side firms can understand how their orders are routed and can meet their best execution obligations, whilst protecting the intellectual property of the sell side firms providing order routing services? That’s a question that traders, asset managers and smart order router vendors are currently grappling with.

The way that traders navigate the market has been subtly changed by the electronic tools they use says Mark Northwood, global head of equity trading at Fidelity Worldwide.

“It is like driving down the road with a description of the road to guide you,” he says. “You know your speed, you know how many other cars are on the road and what the conditions are like – but you can’t see the actual road.”

The systems that route orders have become the trusted guide for trading desks and yet, with brokers determining where to connect and order routing models, buy-side firms are concerned that a gap exists between their best execution obligations and their order routing.

For some investment managers this issue is crystallised by Aquis Exchange, a subscription-based equity trading venue launched in November 2013. Several major brokers will not connect with it, yet their clients report that on certain days it carries over 10% of trading volume for many major European stocks, such as Pirelli.

How Smart is Your Order Router?

“So how smart is your router when you are missing 1:10 of everything traded?” asked the head of European trading for one of the world’s largest global asset managers. “It’s a very thorny subject, but transparency would be super helpful in a best execution world to understand their prioritisation hierarchy without encroaching on the router intellectual property.”

Gregg Dalley, EMEA head of trading at Schroders, says that while most buy-side firms investigate where smart order routers go and the logic they are based on, the really valuable question is where they do not go and why.

“If they can’t provide us a valid answer then it’s up to us to either pressure the sell side to connect or for us not to trade there,” he says. “For Aquis Exchange we are not getting valid answers at the moment. The frustration is, if they only ever look at historical volume how can anything new be accepted into the routing hierarchy?”

The Trodden Path

SORs were conceived of as a method of tackling market fragmentation that occurred in US markets in the late 1990’s and early 2000s. As firms took advantage of latency differentials between exchanges and undermined other’s trading algorithms, desks looked at new ways to avoid being gamed. In 2007 regulators put in place the Regulation National Market Securities (Reg NMS), which determined that brokers had to look for the best price across eligible markets when trading US securities, and Markets in Financial Instrument Directive (MiFID) which obliged European buy-side firms to set out best execution agreements with their brokers.

David Polen, global head of Electronic Execution at Fidessa says, “At the same time commissions were falling, so a new driver came up on the SOR around execution costs. People said it was not enough to just find liquidity and to not get gamed; now they also have to look at cost because the cost of executing an order was proportionally a much greater amount of the commission.”

As dark liquidity became more common on lit exchanges via hidden orders and iceberg orders – as well as on dark pools – taking advantage of dark liquidity became the next stretch for SOR functionality. A method of tracking this complex routing was also needed.

Jan Jonsson, head of Execution Product at agency broker Neonet Securities says, “In 2007 after MiFID people were focussed on FIX Tag 30, looking at the last market traded, and benchmarking the trade only. Now they can [use FIX] to track every move the order has made, how it has been posted and re-posted, which dark pools have been looked at, in far more detail.”

Flash Boys

‘Flash Boys’, published in April 2014, claimed that routing fed the orders of long-only buy-side traders to high-frequency predators, whose arbitrage strategies pushed up implementation shortfall costs for the investment managers. Routing was seen as a path that regulators began to pay more attention to.

In 2015, US regulators fined Citi Global Markets, Goldman Sachs, ITG and UBS in cases that related to historical opacity or errors in order routing. The providers of smart order routers – both the technology vendors and the brokers – are now seeing increased demand for transparency in the way their systems operate.

Tim Wildenberg, CEO at Neonet Securities says, “There is a drive to greater transparency [in routing hierarchy]; smart routing is an extremely complex art but we are happy to spend time to help clients understand how our SOR takes an order and where it goes – often after six months or so we then see that trust has built up.”

The Transparency Effect

SOR technology providers to both buy- and sell-side are working to provide the maximum visibility over routing without compromising their own intellectual property.

“We give the buy-side complete control, complete transparency,” says Max Palmer, head of Algorithmic Trading and Analytics at FlexTrade a provider of trade execution technology. “They know where all of their orders are routed because we control that. They may also allow a market or a broker to perform optional handling of their orders, but they may have less control and transparency as a result.”

Whether supplied directly to the buy-side or supplied to brokers for use in their own platforms, technology providers have to offer a way for the user to access information on order routing for their own or for their clients’ use.

Mark Palmer, senior vice president and general manager of Engineering at TIBCO Software says that about four years ago the firm developed a live data mart to support real-time insight into market activity. It acts like a data warehouse but operates on streaming rather than fixed data, to support functions like smart-order-routing pricing decisions. Users can make queries that the mart remembers and can push out matches to the queries as the systems run.

“It might be ‘Tell me of every order that is over US$50,000 and is stuck in a router for more than five seconds’,” he says. “And then the server remembers that and monitors billions of events a day, but constantly updates that list of what is stuck. Because it can scope it down to individual clients, a lot of our customers use it to provide routing transparency, to know and be alerted in real time when specific clients’ orders exhibit certain characteristics.”

smartTrade, which provides trading systems across different asset classes, gives its users a dashboard so a trader can understand what is going on using visualisation of the task. David Vincent, CEO at smartTrade asserts that to explain a routing decision you need to have two types of information.

“You need to know the state of the market when you made your trading decision, which needs underlying aggregation technology to provide data on the depth of the order book and so on,” he says. “Where once a smart order router simply went right or left there are now more complex routing technologies and the trader needs to understand the logic of the router’s decision. We give them a map to show the trader the path to execution.”

Challenges of Running an In-House SOR

With trust in some sell-side firms being weakened, taking control of the SOR function in-house is one option. However it has many challenges.

Jonsson says, “Crucially, firms struggle to get stable latency using SOR – most buy-side firms don’t have their own membership of the marketplaces and use secondary data sources rather than a direct feed from the exchanges or MTFs.”

A buy-side developed SOR platform launched in 2011 is offered by Pohjola Asset Management Execution Services, which uses infrastructure from agency broker ITG and a routing methodology controlled by its buy-side users to give them both direct access and transparency.

CEO Simo Puhakka says that buy-side firms are often shorthanded and therefore getting resources to address the issue can be a problem. Furthermore the lack of technology on the desk can limit the ability to analyse data around market microstructure, venues and execution quality.

“That is what is needed to consider where to route an order,” he says. “First you need accurate data and not everyone is capable of making that investment – that is where we come in.”

The firm has built up several years of data to support its clients routing methodology which is accessible by investment managers looking for an alternative routing channel.

“By taking us as one of their execution channels, our users learn from us and the other clients in our user group, and that gives them leverage with their brokers,” he adds.

Dalley says that non-conflicted approach offered by third party routing providers is “positive” but is not without challenges as the latency can be greater because of the step between going to that third party, then the broker and then the exchange as well as any data latency.

“You need to be aware of that when using a third-party provider,” he says. “Then if the broker is going via their BCN before the market, which can introduce a delay in which time something can happen to the order book in the market. We have to weigh up probability of price improvement in the BCN or other destinations and the potential for size improvement verses a slight delay in the order book.”

Furthermore, when trading with a broker it is obligated to provide best execution for the investment manager.

“If you take smart routing into your own hands then you take on that responsibility and you need to make sure you have the resources and data to do so,” warns Dalley. “You have to look at more than price data; you have got to take size into account, the likelihood of execution, timing of the orders that are sent into market to capture the majority of what is shown. You need to back this up with data to justify the SOR logic and to continuously recalibrate it.”

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