FlexAdvantage Blog

Buy-Side Savviness for Data Analysis Shakes Up Broker Allocations

Data Analysis

By Ivy Schmerken

As asset managers become more data-driven in their analysis of execution quality, they are reallocating order flows among their broker-dealer relationships at a faster pace.

That finding provided the context for a recent Greenwich Associates webinar “Best Execution: How the Buy Side Measures it, How the Sell Side Produces it.” Panelists discussed the causal relationship between quantitative tools, execution quality and the reallocation of order flow among broker-dealers.

“As the quality of the tools the buy side uses to analyze their executions has increased in sophistication, it’s also driving the increase in the speed with which they reallocate flow,” said Ken Monahan, senior analyst on the market structure and technology team at Greenwich Associates.

Data Analysis

Ken Monahan

Based on decades of data on peered relationships, Greenwich examined how the buy side reallocated their flows across two time periods, 2013 to 2014 and 2017 to 2018, and plotted the distribution of changes over time.

It found that reallocations per broker-dealer are quite high.  For the median firm, the absolute reallocation per broker-dealer moved from 22% in the 2013-2014 period to 36% in the 2017-2018 period. “So, the median firm was reallocating a third of its flow per relationship in one year,”  said Monahan.

In a live poll, more than half (or 54%) of webinar attendees said that the use of technology to better analyze execution quality is what’s driving the increased propensity of the buy side to reallocate flows among brokers.  Meanwhile 42% said that increased attention to best execution requirements is behind the trend. Only three percent cited the sales tactics of dealers (discounting capital commitment), and one percent denied that reallocation of flow is actually changing.

PM Profiling & Internal Metrics

Some buy-side firms are developing metrics for their internal trading desks before they evaluate their brokers.

“What we’re trying to do is align our trading strategies with our PM intentions,” said Curt Hurl, Director, Global Trading, Ontario Teachers’ Pension Plan (OTPP), who explained how the fund is profiling portfolio managers to inform its transaction cost analysis (TCA). “We try to dig in to what is the intention of the PM when they send over a trade,” he explained.

Data Analysis

Craig Hurl

“If they’re sending over a basket with regards to a quant program, is there any short-term alpha built in, or is [the trade] alpha agnostic either one or two days after the trade or during the life of the trade?” asked Hurl.

Historically TCA was implemented to ensure compliance with best execution obligations. “But, as the quality of TCA products has improved, it’s being used to inform trading desks to a much greater extent both for internal buy-side trading desks and in dealer relationships,” noted Monahan.

In the case of OTPP, TCA is used holistically to assess absolute trading costs across all asset classes at the parent level. “We use various benchmarks to measure these costs,” said Hurl. He said that OTPP is working on TCA platforms internally for futures and it relies on a third-party vendor for equities.  Specifically, the fund utilizes arrival price as its main benchmark to estimate  slippage. “TCA is used to measure slippage at the parent level, but we try to dive deeper into child order placements as well,” said Hurl.

“Part of what we look at is the liquidity of a stock, how do we source liquidity, is the stock illiquid, did we use blocks, did we use algos, which then goes into measuring that market impact,” explained Hurl.  In addition, it looks at reversion of the stock price using time horizons — whether that occurs in seconds or minutes. It also looks at specific metrics around child placement to build out an overall metric.

At this point in its process, the pension fund is broker-agnostic because it’s trying to figure out how to trade a certain stock or basket of stocks based on the PM profile, said Hurl. “Then we could put brokers in that horse race based on similar types of strategies that we are looking for,” he said.

While buy-side firms are scoring their trades internally based on PM intentions, brokers are facing more savvy clients with metrics.

“Our clients have become increasingly more data-driven and more systematic in their approach,” said Vlad Khandros, Managing Director, Global Head of Market Structure & Liquidity Strategy, UBS, who spoke on the webinar.

Data Analysis

Vlad Khandros

“When TCA, alpha profiling and data sets continue to evolve on the buy side, the sell-side offering needs to be more tailored and the focus on investing in things, like infrastructure, data sets and algo customizations continues to increase,” said Khandros.  “Given that clients are increasingly more data-driven than ever before, and their trading is getting more automated than ever before,  it only further connects the process and the allocation,” said Khandros.

“In the last 12 months, we’ve seen quite a significant demand within the large brokers but also exchanges and other organizations responsible for electronic trade execution, to provide a much more deeply sophisticated inquiry as to why a given outcome occurred,” said Donal Byrne, CEO of Corvil who  spoke on the webinar.

In particular, the buy side has dramatically upped their level of sophistication in understanding how electronic execution occurs on given venues of disparate IT systems, and how they are related to execution outcomes, said Byrne.

Data Analysis

Donal Byrne

Algo Wheels Offer Statistics

The rise of algo wheels is helping the buy side to automate workflows and more systematically allocate orders to different broker algo buckets. OTPP utilizes algo wheels to rank different brokers based on five different metrics: market cap, spread capture, percentage average daily volume (ADV) volatility and time of day, said Hurl. When the algos are broken down into different buckets,  the trader may find that an algo performs well for large cap stocks in a low volatility time period, but not so good for small cap or high volatility, said Hurl.

By capturing all of this data, and looking at many types of metrics, buy- side traders can try to pre-allocate based on these types of characteristics, said Hurl.

“If we had more granularity on a specific stock at a certain time of day under a certain volatility regime, we may want to favor one algo over another algo,” said Hurl.

Venue Analysis & Broker Scorecards

OTPP also sends out its own quarterly scorecard to its top brokers to get a picture of where trades have been routed and where they have been filled, both in lit and dark venues, said Hurl.  It also asks for fill-to-route ratios, duration of orders, and how long its orders were posted on venues and how much of its flow has been routed to brokers’ own dark pools.

When it receives the template back from brokers, Hurl said he looks for patterns and anomalies. It is used for a follow-up discussion and to question whether the broker is aligning with the pension fund’s strategies. For instance, if the fund is executing passive strategies, but notices it is crossing the spread a lot, the fund will question the broker about whether that aligns with its expectations on TCA.

While measuring execution quality, the buy side still relies on its sell-side partners to provide data for venue analysis and order routing.

For example, UBS is helping clients by offering standard TCA and highly customized TCA around algorithmic performance and where fills are occurring, said Khandros. The broker will look at certain FIX tags to figure out if orders are “making” or “taking” liquidity.

On the US equities side, the buy side is “looking at the economics around a route,” said Hurl. “They are looking at the balance between maker taker vs. inverted venues, all the different ecosystems that exist and how economics play a factor in potential trade performance,” he said.

Given that the SEC  has approved and expanded the institutional order handling rule,  this means that  asset managers will have access to more data from broker-dealers when the rule takes effect in October.

Access to data on fill rates,  latency and cancellation rates are all  important to  venue analysis, according to the Greenwich survey.  A live poll asked attendees what would most meaningfully improve their profitability:  60% picked fill rates, 27 % latency, and 5% chose cancellation rates.

The buy side needs a minimum of three FIX tags — 29, 30 and 851 — to create its own TCA for venue analysis, according to Hurl. Even if firms don’t use a third-party vendor, they can probably build a basic TCA internally, said Hurl.

On its broker scorecard, OTPP utilizes algo wheels in equities to provide a venue breakdown of where the flow is going.  “The way we think about it is how long are brokers resting in some of these venues. Are they resting in taker/makers,  or they resting in high rebate type of venues,” said Hurl,  adding that he looks for anomalies in where order flow is being sent.

About 20% of institutions utilize algo wheels to choose and evaluate venues, according to Greenwich’s survey.  But the majority (60%) said they use TCA/venue analysis, 53% use brokers/third party, 33% use fill rates, 20% use best execution reports and 7% use fees.

As the buy side continues to adopt algo wheels, TCA and other metrics to evaluate brokers and measure execution performance, these metrics are bound to play a role in their reallocations of order flow to brokers.

“This is evolving into an arms race known as execution alpha, which is the idea that through pulling together all of these components, alpha profiling, trade effectiveness and in-flight analytics, clients can achieve a superior execution,” said Byrne.

 

 

 

 

 

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Past blog posts related to Data Issues

Avoiding the Herd in Overcrowded Alt Data

Consolidated Market Data Feeds Gain Traction in Algo Trading and Fixed Income

Alt Data: A Work in Progress

BIG Data: Getting Granular with ESG Factors

Data Science Platforms Help the Buy-Side Integrate Alternative Data

Algo Development 2.0 Looks to Open Source, Cloud & Big Data

Alt Data on the March

Buy-Side Delves into Mobile Data

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