Beyond the Benchmarks: A New Era for the Buy-Side Desk
By Rob Laible, Head of Americas, BMLL
I recently had the privilege of moderating a truly insightful panel at the Equity Leaders Summit in Miami. We gathered a group of ‘agents of change’ from the buy-side to discuss the modernization of trading infrastructure and the strategic integration of data.
While we often get bogged down in the technical weeds of market structure, this conversation struck a different chord. It wasn't just about faster pipes or better algorithms; it was about the fundamental evolution of the trader’s role, the plumbing of our data costs, and the critical link between Portfolio Manager (PM) intention and execution. We also explored how data analytics and technology are reshaping execution strategies.
Here are the five key themes that defined our conversation.
Data Infrastructure and Management
One of the central discussion points was the sheer complexity and administrative cost of managing market data, particularly the distinction between real-time and historical data.
The Burden of Data Administration: Panel participants highlighted the high costs and administrative burdens associated with maintaining direct relationships with exchanges for historical data. They described the challenges of audits, citing an example where an exchange demanded 18 years of back fees because a contract allowed for storing data but not explicitly using it.
Outsourcing Historical Data: To mitigate the high costs and risks associated with data administration, firms are increasingly transitioning post-trade analysis to vendors like BMLL. This strategic shift significantly reduces administrative overhead, as the vendor assumes the responsibility for complex data cleansing and inclusion logic, allowing internal teams to focus on higher-value analysis
Decoupling Live and Historical Systems: Separating historical data processes from live trading systems was seen as a major system-wide success. The decoupling enables research and history work to move at different speeds than live trading infrastructure. This shift allows firms to outsource the heavy lifting of data cleansing and inclusion logic so that high-value quantitative talent can focus on long-term price trends and alpha rather than cleaning data.
The Feedback Loop: PM Intention and Execution
Perhaps the most spirited part of the discussion centered on the feedback loop between the PM and the trading desk. The panel emphasised the necessity of tighter integration between portfolio construction and trade execution.
Aligning PM Intention with Trading: One of the speakers argued that the most important element of execution is the link between the PM’s intention and the trading desk. The goal is to calibrate execution based on whether the trade is driven by long-term alpha or beta, rather than just focusing on spread capture.
Changing PM Behaviour with Data: Another speaker noted that while some PMs are indifferent to transaction costs because they are focused on long-term returns, showing them data on reversion (the cost of urgency) can successfully alter their instructions to the desk. This data-driven approach helps PMs understand that slowing down execution could save significant basis points.
Questioning Benchmarks: The panel also challenged artificial benchmarks such as VWAP (Volume Weighted Average Price), suggesting that traders and PMs should not be measured merely by how close they get to a VWAP line. Instead, execution should be judged by how well it preserves long-term capital and growth targets.
Standardisation and System Integration
We also discussed the need to standardise foundational technologies (because they do not necessarily drive differentiation) and free up talent for higher-value tasks.
Standardising Common Foundations: Panellists advocated for standardising the ‘annoying things that everyone does’ across the market. By establishing a common technological foundation, firms can retain their competitive ‘moat’ on their specific logic rather than on basic infrastructure.
Automating Workflows: The goal is to create an execution ecosystem where the intention of every operator is translated into algorithmic behaviour. This system aims to automate the majority of the workflow, allowing traders to focus on finding blocks and managing complex liquidity events.
The Evolution of the Trader’s Role
Looking ahead, the panel painted a vivid picture of how the human element of trading will evolve. The discussion touched upon how the role of human traders is shifting from manual execution to high-level oversight.
From Operator to Supervisor: While the fundamental role of the buy-side trader hasn't changed much in 25 years, it is about to shift significantly. Traders are becoming like operators in an emergency control centre, watching automated systems execute the majority of the flow and only intervening during exceptions.
Focusing on Alpha, Not Data Cleaning: There is a push to stop using expensive quantitative talent for nitty-gritty tasks like data cleaning. Instead, these resources should focus on long-term price trends and translating PM instructions into market strategy.
Future Technologies: AI and Automation
Towards the end of the panel, the conversation turned to the potential of Artificial Intelligence (AI) and Large Language Models (LLMs) to solve complex trading problems.
AI for Alerting Systems: One of the most promising areas is in revolutionizing alerting systems. Currently, designing alerts involves complex programming to avoid false positives. AI agents could learn over time by observing human interaction (e.g., clicking ‘meaningful or not’ on an alert), thereby rewriting their own rules and reducing the programming burden. This could turn a complex, expensive programming problem into a manageable, evolving system.
Increasing No-Touch Trading: The panel consensus was clear: the volume of no-touch (fully automated) trading is set to increase significantly. By leveraging these future technologies to handle the bulk of the work, we free up our human traders to do what they do best: manage risk, interpret complex market signals, and add value where algorithms cannot.
I am sure the conversation will not stop here! In the meantime, my thanks go to our ‘agents of change’ for offering their insights towards this panel discussion.
Michael Steliaros, Global Head of Portfolio Implementation, Research and Trading, ADIA
Tobias Unger, Senior Quantitative Trader, Norges Bank Investment Management
Timothy Stark, Equity Market Structure and Transaction Research, Capital Group
