Fund operations: The explosion of algorithmic trading
09 Nov 2022
Ryan Guichon, product head for fund administration and accounting services at RBC Investor & Treasury Services, provides his insights on how fund operating models are evolving to align with today’s dynamic market environment
Image: dennisvdwater/stock.adobe.com
As financial markets become increasingly efficient and faster-moving, fund managers are having to adapt their operating models accordingly. Technology plays an enabling role in this evolution, as does the human element. Both need to be managed optimally and rebalanced to get the best results.
Algorithmic trading and passive strategies
The popularity of new investment vehicles driven by algorithmic trading systems is exploding. These system-driven investment strategies are bringing about significant change to the fund operating model.
This change requires the development of new inter-connected systems to keep execution of the investment strategies ‘between the lines’ — much like how a self-driving car keeps passengers safely on the road. A big part of the change requires an understanding of data science within middle- and back-office functions, enabling fund managers to take a step back from execution and focus on oversight. Index funds, for example, have a relatively simple coded strategy: “This is the list of instruments that we will be investing in, given to us by the index engineering team, and we will rebalance these positions on a set cycle.”
The investment strategy has been simplified to such a degree that complexity has arisen in other parts of the fabric — the trading strategy. With the efficiency of today’s markets and increasing liquidity challenges, the window to get in and out of investment opportunities is shorter, which is driving a change in how funds oversee their models. The market no longer provides time to oversee each individual trade, so the models require built-in risk-governance mechanisms that you then oversee.
Managing the human element
While much is made of the importance of technology in driving these changes, the human element cannot be overlooked. As change occurs, people will be asked to apply their expertise in different ways and settings, and work with people who have unfamiliar competencies. Clear communication is needed to provide employees with context for the change: why is it happening? What will it deliver? How will I fit into the new system? It is important for employees to understand that change is about applying their tools in a different setting. This involves helping them to understand that the nature of their work is going to change, and why that is a good thing.
For example, take someone involved in fund accounting, who historically enjoys getting into the ledger entries and reviewing them for valuable insights. If this is no longer a requirement, because the ledger entries are happening so quickly that the review process needs to be automated, then the person’s role may become overseeing the exception system that runs the review process. Or they may get pulled into a new area where they can apply their skills and experience in a slightly different context. Making the effort to clearly communicate the rationale and benefit of change will make it easier to sell that change and get buy-in.
Adopting disciplined data management
In an environment of increased automation, effective management and documentation of data transmission and lineage are more important than ever. As you change your systems, you need to understand how data moves between your front, middle and back offices, and institute best practices for documentation, control mechanisms and data and system architecture. At the core of this is detailed documentation of every critical process and system that touches your data. Over time, the people who developed the systems will move on to other projects or jobs, so you require the documentation to understand why a system was set up the way it was, and how any changes will impact your data and other systems within the overall architecture. Otherwise, you run the risk of projects that never end and stall other impactful investment opportunities.
To support the increased pace of change within fund operations, a modular approach to system development and data management is recommended.
I am a big proponent of purpose-built architecture, where you have individual systems built to execute specific functions. This allows you to measure success more easily — with a data model built over time to power future learning applications.
You can also pull old systems and slot in new ones more efficiently, providing greater freedom to adapt to new demands on your operations, whether it is a new regulatory requirement or an investment product with unique data requirements.
Knowing where and how to find value
Tackling operational change can be overwhelming — a clear understanding of a project’s value and success metrics goes a long way toward selecting the right one. It can be challenging to know where to start. Markets are changing very quickly, and there is an expectation of responsiveness and adaptability, but change for the sake of change is a misguided approach. It is important to evaluate projects based on the value they will bring.
Understanding a project’s value and communicating how to measure the value is key. There need to be clear gates and success criteria for a well-directed process of change.
Algorithmic trading and passive strategies
The popularity of new investment vehicles driven by algorithmic trading systems is exploding. These system-driven investment strategies are bringing about significant change to the fund operating model.
This change requires the development of new inter-connected systems to keep execution of the investment strategies ‘between the lines’ — much like how a self-driving car keeps passengers safely on the road. A big part of the change requires an understanding of data science within middle- and back-office functions, enabling fund managers to take a step back from execution and focus on oversight. Index funds, for example, have a relatively simple coded strategy: “This is the list of instruments that we will be investing in, given to us by the index engineering team, and we will rebalance these positions on a set cycle.”
The investment strategy has been simplified to such a degree that complexity has arisen in other parts of the fabric — the trading strategy. With the efficiency of today’s markets and increasing liquidity challenges, the window to get in and out of investment opportunities is shorter, which is driving a change in how funds oversee their models. The market no longer provides time to oversee each individual trade, so the models require built-in risk-governance mechanisms that you then oversee.
Managing the human element
While much is made of the importance of technology in driving these changes, the human element cannot be overlooked. As change occurs, people will be asked to apply their expertise in different ways and settings, and work with people who have unfamiliar competencies. Clear communication is needed to provide employees with context for the change: why is it happening? What will it deliver? How will I fit into the new system? It is important for employees to understand that change is about applying their tools in a different setting. This involves helping them to understand that the nature of their work is going to change, and why that is a good thing.
For example, take someone involved in fund accounting, who historically enjoys getting into the ledger entries and reviewing them for valuable insights. If this is no longer a requirement, because the ledger entries are happening so quickly that the review process needs to be automated, then the person’s role may become overseeing the exception system that runs the review process. Or they may get pulled into a new area where they can apply their skills and experience in a slightly different context. Making the effort to clearly communicate the rationale and benefit of change will make it easier to sell that change and get buy-in.
Adopting disciplined data management
In an environment of increased automation, effective management and documentation of data transmission and lineage are more important than ever. As you change your systems, you need to understand how data moves between your front, middle and back offices, and institute best practices for documentation, control mechanisms and data and system architecture. At the core of this is detailed documentation of every critical process and system that touches your data. Over time, the people who developed the systems will move on to other projects or jobs, so you require the documentation to understand why a system was set up the way it was, and how any changes will impact your data and other systems within the overall architecture. Otherwise, you run the risk of projects that never end and stall other impactful investment opportunities.
To support the increased pace of change within fund operations, a modular approach to system development and data management is recommended.
I am a big proponent of purpose-built architecture, where you have individual systems built to execute specific functions. This allows you to measure success more easily — with a data model built over time to power future learning applications.
You can also pull old systems and slot in new ones more efficiently, providing greater freedom to adapt to new demands on your operations, whether it is a new regulatory requirement or an investment product with unique data requirements.
Knowing where and how to find value
Tackling operational change can be overwhelming — a clear understanding of a project’s value and success metrics goes a long way toward selecting the right one. It can be challenging to know where to start. Markets are changing very quickly, and there is an expectation of responsiveness and adaptability, but change for the sake of change is a misguided approach. It is important to evaluate projects based on the value they will bring.
Understanding a project’s value and communicating how to measure the value is key. There need to be clear gates and success criteria for a well-directed process of change.
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