By Kristian West, Global Head of Equity Trading, J.P. Morgan Asset Management
Best execution is not merely analytical oversight, but represents the complete order lifecycle, forming the essence of a trading function.
Best execution can mean different things to different people, from a high level policy to a core objective. Whether it is a goal, a benchmark or an ineffable aspiration, achieving best execution is a requirement for the whole industry.
Regulations imposed by the Markets in Financial Instruments Directive (MiFID) II, which take effect from January 2018, will require firms to demonstrate “all sufficient steps” have been taken to provide best execution, which is a strengthening of the previous rules that has created a great deal of consternation about how to create a factor-based approach to supervision.
From our perspective however, nothing changes. The objective of getting the best possible outcome for our clients is a personal, cultural and corporate goal that hasn’t altered over time. If articulated well, it should be a source of competitive advantage and increased client returns.
So how does one create a systematic, quantitatively-robust best execution framework? It is a practical rather than a philosophical challenge. Almost every component that touches an order through its lifecycle needs close attention, ensuring that the correct information is collected, stored and analysed.
Best execution starts with having the right people; specialists building the systems, executing the orders, analysing the flow and managing the overall process. Having a variety of skillsets is key: including people from outside of trading, and simply hiring quantitative experience is not enough. Having a diverse population form part of the best execution process stimulates creativity, perspective and challenge.
To have a robust, structured environment one needs an integrated system where technologists, traders and quantitative researchers work together, using modern techniques, to improve all aspects of order execution. The skills needed to be successful have broadened over recent times.
The role of the high touch trader continues to be critical in sourcing liquidity and enhancing capacity.
However, as markets have become more complex, technological and quantitative skills have become increasingly necessary. Additionally, the separation of these roles is being questioned, as the overlap between the three becomes more profound. Where does a trader’s role end and a technologist’s start?
The trading environment should facilitate a systematic process. Giving traders access to more functionality and data allows them to turn intuition into informed – and importantly, evidential – decisions. Using machine learning techniques in such an environment leads to the creation of real-time “actionable analytics”. This allows one to reduce the variance of outcomes and systematically recommend the most suitable provider, execution strategy and parameters for a given order. This adds science to the art of trading by removing behavioural bias and introducing repeatable, quantifiably measurable actions.
Incorporating machine learning in this way also allows traders to focus on the aspects of the execution process where they are able to add the most value. Robustness to change should be core to the best execution approach, so the processes should be dynamic enough to accommodate market developments, such as tick size changes or the growth of systematic internalisers.
The review process should act as a feedback loop, continuing to improve the execution process. While robust oversight and evidencing is critical to best execution, it should not form the entirety of the framework. Traders, analysts and fund managers need to work together in a data driven process to continually look to improve the full implementation cycle. This needs to be holistic, covering everything from approval workflows to choice of execution venues. First and second line controls should be integrated and monitored by specialists with a deep understanding of the underlying trading processes and market dynamics.
Extensive connectivity, data, transparency and integration into the investment process are vital to driving optimal outcomes. Each event and action needs to be recorded, stored and made available for analysis. Only then will you be able to measure performance improvement. The platform required to build, monitor and enhance such an environment goes far beyond the traditional off-the-shelf order management system and transaction cost analysis platforms.
Traders, quants and technologists need to be able to build, refine and analyse in parallel. Systems need to be adaptable to allow workflows to be optimized and capable enough to handle the strains of modern quantitative techniques. While automation has featured in the trading landscape for some time it was initially leveraged for operational efficiency. Now it forms a key element of the best execution workflow and platform. A trader’s performance is proportional to the number of orders they manage, but this need not apply to automated processes. Combining automation with analytically driven recommendations establishes a controlled environment allowing one to quantitatively improve performance and client outcomes. It creates the ability to measure outcomes versus intention, a key element of “all sufficient steps”.
Best execution is not merely analytical oversight, but represents the complete order execution lifecycle, forming the essence of a trading function. To succeed, a combination of people, processes and platforms making continual, incremental improvements and adapting to market structure changes is required. Continuing to invest globally in these skills, processes and systems to enhance execution and investment performance regardless of what MiFID may bring in 2018 will differentiate the industry leaders.
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