Schroders is a 200+ year old global asset manager, managing over £700 billion AUM with a global trading team of 45 professionals spread across 12 trading desks in eight countries.
We have taken a global multi-asset approach to trading over the last three to four years, as we have made key changes internally under one global head of multi-asset trading. There is much more collaboration across regions and asset classes today than ever before.
What are emerging trends on buy-side trading desks, particularly in Asia?
With rising cost pressures and increasing regulation, the biggest focus for most if not all buy-side trading desks has been on achieving scale and operational efficiency. To achieve this, automation of trading workflows has been a key focus. At Schroders, we are embracing automation technology in different forms, such as for automatic order routing from OMS to venue or platforms; for automatic broker selection; for auto-execution, or even for auto-booking of trades upon execution. This automation technology spans multiple asset classes. Similarly, success with algo wheels in equities is leading expansion of their deployment into other asset classes such as listed derivatives and FX.
In fixed income, electronic trading is on the rise, and Asia is now embracing newer execution protocols such as all-to-all trading and portfolio trading, alongside the conventional RFQ protocol, which have been around for a while in US and EMEA markets. We are also seeing the emergence of Asia-focused trading platforms that will further propel this growth.
Lastly, Asian buy-side desks are now making use of data like never before both in terms of execution analysis as well as to set up clear processes to make decisions.
What are some of your primary current initiatives?
In addition to optimizing existing trading workflows by either automating or adopting electronic execution wherever possible, I have been currently working with trading desks on two value-add projects involving our global investment desks.
First is our Liquidity Monitor, which is an in-house data tool that provides real-time market liquidity color to our Portfolio Managers (PMs) in equities and fixed income space from several industry-wide liquidity sources. The Liquidity Monitor also provides some interesting third-party portfolio analytics that help PMs time their orders effectively to target natural and block liquidity thereby reducing execution costs.
Second is a global IPO tool that is being developed to showcase opportunities to our PMs in the primary market space for both equities and fixed income. The tool provides timely information on all upcoming new issues globally across different regions and sectors. It also enables us to monitor our group-level IPO participation across several investment desks showcasing key metrics such as percentage of allocation and performance of the new issues over a period of time, as well as highlight any missed opportunities.
How are buy-side trading desks extracting more value from data? What are the ‘pain points’ in this area.
It has been an ongoing effort for buy-side trading desks to raise the bar on how to collect more and better data and use it to deliver best execution for their clients.
Today, most value is being extracted from our own historical trade execution data, especially in the equity space where we have implemented machine-learning based predictive models for determining the best-suited execution method or strategy for each order. These models take into account important inputs such as PM profiles, order characteristics, stock volatility, etc., in order to systematically indicate to our execution traders how they should execute a particular order arriving in their blotters.
In fixed income and FX, we continuously analyse our trading patterns (data) to implement rule-based automation techniques within the execution workflow. This can include, for example, auto-routing of small and liquid orders onto a trading platform for auto-execution via RFQ method. This workflow automation is then helping us with improved data capture for better execution analysis, allowing trading teams to further adjust the auto-routing or auto-ex rule criteria to achieve scale and better efficiency.
Also, we often find difficulties in getting data back in-house from execution venues, due to lack of integration between OMS and EMS/trading platforms, or due to lack of standardization in how trading platforms send back execution data back to OMSs. For example, different platforms use different FIX tags for reporting back execution venue data — we have to work with them and our OMS provider in order to standardize that.
How are buy-side trading desks adopting ESG in Asia?
As ESG factors have started to play an integral role in the investment process, it is important that trading desks understand the importance of ESG, especially what ESG means to their firm and how it is being incorporated by the investment teams into the investment decision process.
At Schroders, our trading desks not only engage with our client teams in order to understand the evolving client demands around sustainable investing, but also with our PMs and analysts on the investment desks to learn about our proprietary as well as third-party ESG data and its integration into the investment management process.
However, we are seeing clients (in the US, to start with) mandating asset managers in regards to their execution flow, such as to execute a portion of their orders with minority brokers. Hence we are working to ensure that our systems are ready to support these unique requirements, especially communicating this to traders in a systematic fashion.
In the future, there could be demand to analyse transactions costs via an ESG lens, or for that matter, broker reviews based on governance factors such as gender diversity, etc.
What will be the key future trends in buy-side trading?
Achieving scale and even greater efficiencies shall remain priority for buy-side trading desks as they will add more AUM and continue to operate in a highly competitive and regulated environment.
The automation journey of trading desks will continue with better collaboration with investment desks, as well as with adoption of more sophisticated AI/ML driven technology. For instance, the use of static rules today in rule-based auto-execution will be replaced by a set of intelligent real-time order level criteria on the back of predictive pre-trade TCA, and data such as IOIs/Axes, hit-ratios, historical quotes, etc.
We could also expect significant efficiencies from the ongoing technological advancements in the fixed income primary market, i.e. standardization to security issuance process, with centralized and timely access to deal information to the buy side, complemented with electronic trading capabilities for order placements and receiving allocations.
Buy-side desks are also expected to make far better use of data, especially pre-trade liquidity data such as IOIs and axes received from various sources to tap on to natural liquidity as well as block liquidity opportunities to reduce execution costs for large and less-liquid orders.
On the desktop technology side, we should see increased adoption of open systems on the trading desk as desks become more multi-asset and screen space remains limited. The goal is for better interoperability of various systems to efficiently work together on the trader’s desktop.