Kristian West, head of JP Morgan Asset Management’s investment platform, explains how the asset manager is driving greater scale, efficiency and standardisation across the organisation.
What were the drivers behind the new investment platform?
We wanted to introduce scale and greater efficiency across our technology infrastructure, which includes research, the development of investment ideas, portfolio construction and trading. As a large and complex organisation, historically the asset management group was divided into silos with different systems and processes in each region. This meant that if portfolio managers wanted to send a US order, they would do it through the US or a Taiwanese order through Taiwan. However, our CEO George Gatch wanted to redefine the way the group did business and create a global experience and bring greater efficiency, scale and standardisation across the organisation. He wanted to develop an environment where everyone used the same tools, engines and data whether they are sitting in Hong Kong, London or New York.
How did you get involved and how did you develop the platform?
I have worked at JPMAM for 14 years mainly on the equity trading front and became head of the platform in February 2021. However, in 2018 I was made head of equity data science along with my role as head of global equity trading, so I had experience in using artificial intelligence and machine learning tools. We also built an autonomous technology team which sat alongside our trading business and implemented a much more data driven process. We created a predictive machine-learning platform, Stars, which took orders, categorised them, and chose the best way to execute the orders. Scaling this approach across our entire Asset Management business is the challenge at hand.
What functionality does the new platform entail?
The platform is supported by 1,200 technologists and they have helped us pivot from a vertical to horizontal integration which is a huge organisational change but especially one across over 7,000 employees. It has meant condensing several functions and establishing six core groups – equity trading & analytics, derivatives, broker relationship management, investment data, data science and product ownership – under one roof. While the first three are more traditional, the other three involve more modern practices and principles. Overall, the aim is to focus on scale and efficiency through automation as well as create centres of excellence.
Can you go into more detail about the traditional pillars first?
On the traditional side we have equity trading and analytics which is where I have worked for most of my career. It covers all asset management businesses and works very closely with technology to streamline processes, automate trading flow, simulate trading and run historical analysis in order to continually improve trading strategies. The team is centred around achieving efficiency and performance at a lower cost. Trading analytics is a subset of the broader trading space and is focused on delivering front-to-back analytic, reporting and systematic trading capabilities to the global trading desks. The team delivers industry analytics and systematic trading systems which not only cuts execution costs for our clients but also drives trading efficiencies for our trading desks through automation. It also supports the regulatory obligations of best execution.
Derivatives is an area where we have seen a great deal of exponential growth. This is because although equity markets have done very well over the past ten years, we are at a point of inflexion and people want to protect their portfolios. We are building out our derivatives capabilities and have a dedicated derivatives portfolio management team who come from both the buy and sellside. Together they have around 93 years of experience in equity, rates, currency, credit and volatility derivatives. The team acts as a centre of excellence for all aspects of derivatives at the firm, helping improve investment outcomes in existing portfolios and working with clients on complex derivative implementations. This is all built on a best-in-class risk-analytics and middle-office architecture powered by our proprietary infrastructure.
The aim of our broker relationship management team is to ensure that there is one single contact for the Street. The team works with management across all products to create a holistic view of these relationships. The team liaises with major counterparties to best align asset management priorities and identify opportunities where we can improve partnerships.
Can you provide more information on the last three functions?
Product is a dedicated, cross-asset product owner & management team covering the investment management space. As we pivot from vertically integrated technology solutions to horizontal, this team manages the technology engagement to ensure we have consistency and best in class technology capabilities. This team is responsible for delivering cross-AM functionality across Research, Portfolio Construction and Trading. With their focus, they infuse agile delivery principles across the organisation.
Agile is an iterative approach to project management which generally requires teams to work in small, consumable increments. Requirements, plans, and results are evaluated continuously rather than incrementally so teams have a natural mechanism for responding to change quickly. Our key points of emphasis for agile within the investment platform are to value individuals and interactions over documented processes and tools, customer collaboration over a contract negotiation, and the main area is responding to change over following a plan.
We are also building out our data capabilities. We have a great deal of proprietary data as well as data that we buy in, but it has been fragmented. To maximise its use and accessibility we have established a team to transform the business and build an asset management-wide data environment where data is accessible and accurate in any format for decision making.
We created a data science team around four years ago which has given us an early mover advantage. The platform has 26 dedicated data scientists who use artificial intelligence, machine learning and data science techniques within the asset management investment lifecycle including research, portfolio management & trading. This team supports efforts across Asset Management.
How does this dovetail with JPMAM’s sustainability objectives?
Our data science team partners with our sustainability team to help close some of the gaps in ESG information. Although there are ESG metrics, scores and information from corporates, it is still fragmented because not all sectors are mandated to disclose information to the standard we require. We have helped develop a proprietary framework that uses machine learning that uses traditional and alternative data to produce scores and metrics to better identify trends, opportunities and risks.
What have been some of the challenges in building out the platform?
As I mentioned, moving from a vertical to horizontal structure is a big undertaking from an organisational as well as psychological perspective. For example, if you want to trade FX in Hong Kong and London, it may have been done in two ways and now you are asking people to do it in the same way. What we did was to set up working groups across research, portfolio construction, trading and data that sit within the investment platform and with product owners and managers to help drive the agenda of standardisation and convergence. They help define and prioritise the key criteria from the asset managers and ensure that the process is transparent, well understood and goals are aligned and met.
How do you hope to develop the platform in the future?
The last three years has very much been about implementing the right organisational structure, teams and technology. Going forward we will continue to build upon what we have done using agile principles and innovation to help better service our clients.
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