GO WITH THE FLOW.
Chris Hall asks whether brokers can tailor their models to meet customisation demand?
Responding quickly and effectively to client customisation requests is now a base level requirement for today’s execution brokers. According to a Greenwich Associates survey last year, brokers expect almost half of their buyside clients to seek some kind of customisation of order-handling logic during 2019, versus just over a quarter in 2017.
The powerful forces propelling demand for customisation suggest differentiated execution services will only become more important, undermining business models that fail to invest in top-tier capabilities.
“Where they once could simply offer a best-of-breed algo suite, brokers today must also provide a service tailored to the specific requirements and trading styles of their customers,” wrote Richard Johnson, vice president in Greenwich’s market structure and technology practice, adding: “Providing customised algorithms is now a prerequisite for a competitive e-trading offering.”
Client service in general, and algorithm customisation in particular, are becoming more critical as competitive pressures intensify on the buyside. Low-cost alternatives are driving margin compression and a stronger emphasis among active managers on scalability and automation across functions, including trading. Buyside traders told Greenwich they expect electronic trading to account for almost half of US and European equity trading flow within three years, with Asian volumes also surging.
This acceleration toward low-touch execution channels fuels demand for high-quality service. The maturity of electronic trading services, expertise of senior traders and availability of granular and often real-time data analytics allow leading asset managers to deliver top class trading performance at high volumes whilst minimising headcount. But this requires ongoing investment in technology and partnerships with providers that can tailor their algos and other execution tools with speed, precision and scale.
A recent poll of buyside trading heads – Adapting to the New Liquidity Landscape from WBR Insights reported high levels of evaluation and investment in transaction cost analysis (TCA), execution quality monitoring and reporting technology (71% of respondents), greater in-house responsibility for routing and best execution decisions (63%) and an intense focus on ‘getting and managing good quality data’ about execution quality (67%).
Execution brokers are evolving their offerings accordingly. Chris Jackson, head of EMEA equities at Liquidnet, notes growing buyside interest in data visualisation techniques that “open up the blackbox”, offering access to analytics in real time. “Clients want greater levels of transparency,” he observes. To this end, Liquidnet is combining advanced analytics and artificial intelligence with the firm’s existing algo capabilities. “The combination will improve our insight into the performance of an algo in real time, allowing us to tweak it mid-course. Further, we’re using AI techniques to improve our understanding of signal performance, for example to identify when a reversion signal degrades.”
Trading costs must be cut in the battle to deliver differentiated returns to investors, but only whilst also meeting best execution obligations. Paul Squires, head of EMEA equity trading at Invesco, says MiFID II’s best execution requirements have underlined the shift of responsibility for execution performance to the buyside. “The onus is on us to evidence best execution, through oversight, process and outcomes,” he says. “Taking ownership means more than just using the algorithms ‘out of the box’ from top brokers.”
This shift has led to much deeper levels of data analysis at each stage of the investment process. Duncan Higgins, head of electronic products for EMEA at Virtu, agrees that the buyside’s appetite for analysis has sharpened, but says this can have divergent implications.
On the one hand, asset managers are spending more time on alpha profiling, i.e. grouping orders together based on an increasingly precise understanding of how portfolio managers’ investment styles generate alpha to ensure similar orders are executed optimally. Such firms are looking to their brokers to deliver algorithms that can execute according to these niche profiles, perhaps through controlling speed of execution or volume participation levels.
On the other, the comprehensive oversight achieved through enhanced data analytics lets the buyside step back from the coal face in matters of liquidity aggregation. “In the recent past, buyside firms have been very pro-active on venue selection,” explains Higgins. “But today many clients are allowing the sellside to use their knowledge of how and when to interact with particular venues, knowing their analytics will shed the necessary light on execution performance.”
Wheels of fortune
Increasingly, traditional TCA is being overlaid with a wider range of performance indicators, including price reversion, order routing behaviour, and execution volatility. The insights achieved through these analytics provide the basis for constructive dialogue between users and providers.
For Squires, this often centres on matching (and if necessary customising) generic algorithms to precise execution requirements, dictated by investment strategy. Like several large buyside houses, Invesco has adopted the algo wheel approach, whereby orders are divided up and executed by a handful of preferred brokers. For single stock execution, the firm uses different wheels for orders with low, medium and high average daily trading levels. These three liquidity-based ‘buckets’ are then further divided into neutral and alpha order types, meaning a large, non-urgent order might be executed via six algorithms suited to this profile.
Squires and his team then examine performance over set periods, or when sufficient sample data has been gathered, providing feedback to algo providers, who can tweak their strategies to address the reasons for inferior performance. Or risk being replaced by a rival provider on Invesco’s reserve algo wheel. “It’s important to communicate our objectives clearly, letting algo providers know how we measure performance, to help steer their efforts,” he explains. “In terms of customisation, we recently had to ask one broker to dial down aggression levels on what had been offered as a neutral strategy.”
MiFID II has not only impacted best execution obligations, but also liquidity dynamics, via the redefinition of systematic internalisers (SIs). According to TABB Group, addressable SI daily notional stood at Ä7.5bn in January 2019 (13% of overall activity). With SIs operated by electronic liquidity providers (ELPs) accounting for roughly 40% of below-LIS SI activity, TABB Group European research analyst Tim Cave suggests banks and buysiders must develop new capabilities to tap this liquidity safely and efficiently. “Finding a consistent method to rank ELP SIs is likely to be a focus in the early part of this year,” he wrote in a recent commentary.
Pursuit of liquidity
However, it is not just in Europe where appetite for customisation stems from the need to access liquidity efficiently in a fragmented equity market structure. In the US, new platforms such as IEX and the Members Exchange are broadening choice, alongside the continued growth of an off-exchange landscape dominated by alternative trading systems and ELPs.
“When there are so many ways to access and interact with liquidity, buyside traders want to take full advantage of available opportunities, and are increasingly likely to switch tactics ‘in-flight’, for example to complete the last 10% of an order quickly,” says Curtis Pfeiffer, chief business officer at Pragma Securities.
Pfeiffer sees scope for customisation across both ‘first generation’ schedule-based algos (VWAP, TWAP, etc), and more advanced liquidity-sourcing strategies. “There are lots of ways to customise schedule-based algos to match the alpha characteristics of a client. But algos that access liquidity across venues have to be highly adaptive and responsive to changes in liquidity conditions,” he says.
James Doherty, head of equity product at Dash Financial Technologies, says dark liquidity cannot be ignored. “But you can manage the information leakage arising from pursuit of liquidity. Buyside firms are measuring performance much more precisely and are highly focused on identifying and refining tactics to optimise outcomes, i.e. how and when to split orders across venues.”
This is leading to greater popularity of ‘I would’ functionality, for example, which allows traders to capture liquidity on additional venues, whilst working the lion’s share of a large order on a block-friendly platform.
“Investors are willing to take the opportunity to grab liquidity wherever it arises, but they are also using pre-trade analytics to get a better idea of what it might cost them,” says Doherty. “It’s now possible to detect patterns that might only last a few seconds, which still may be sufficient for an algo to capture much-needed liquidity.”
Time to tailor your model
With machine learning also now being used to refine execution techniques, it is clear that execution service providers need to invest continually to compete. But that does not mean only specialists and bulge brackets have a future. Chris Monnery, global head of low touch order management business development at ION Markets, asserts that OMS platforms have the flexible order-handling capabilities to support a high-touch service approach to low-touch orders. “Mid-tier brokers can use their market structure expertise and client knowledge to help buyside clients to access and select the precise combination of tools that will meet their specific needs,” he explains.
In the context of heightened cost and competition pressures, the market for white label algorithm suites is also growing, as fewer sellside firms can justify providing execution services via in-house resources. “There are fewer algo providers and more users and distributors,” says Virtu’s Higgins. “Providers need to build their algos to be easily customisable by sellside customers that need to tweak them in response to their clients’ needs without diving into the code.”
To thrive in the future, brokers’ service models may need to be as flexible as their algos.
©Best Execution 2019