Vanguard’s global head of equity execution consulting explains how his team works to optimise trading practices and minimise costs; and discusses the future of equity execution.
What does your job title mean, and what does your role entail?
As the global head of equity execution consulting at Vanguard, I oversee the strategic direction and operations of the Equity Execution Strategy and Analytics team. Our team comprises individuals with diverse backgrounds in quantitative fields, data science and engineering and our primary focus is on leveraging execution technology, data, and research to advance Vanguard’s execution strategies.
In essence, my role entails leading a team that collaborates across global markets to optimise trading practices and minimise costs for our clients. We utilise cutting-edge analytics and innovative technologies to inform our decision-making processes, with the overarching goal of empowering our traders to achieve the lowest trading costs and unmatched efficiency. By continuously refining our strategies and staying at the forefront of industry trends, we strive to improve shareholder outcomes and uphold Vanguard’s commitment to delivering value to our investors.
You were an algo trader on the sell-side before moving to Vanguard – how has your approach to algos evolved since moving to the buy-side?
I have been trading for 20 years and in that time, I have been lucky enough to trade for all different types of market participants. Early on in my career I traded on behalf of retail clients back when we were using paper dealing tickets and fax machines, so there wasn’t a lot of algo trading necessary! I then moved to a hedge fund where the trading objectives were very different – algos had evolved significantly and usage had picked up, which started to change the dynamics of the market. To stay in front of the ever-changing market environment, we implemented transaction cost analysis (TCA) in order to review our executions and make tweaks to try and improve trading going forward. My career moved on and I started trading as a market maker – these algos are very different to what you see on the sell-side as again the objectives are very different – you have a risk book to consider, and this can change your perception of a security and help add to price discovery.
On the sell-side you have access to a lot of data – these large data sets make it ideal for analysis to try and gain insights into how you can evolve algo strategies, however they don’t necessarily see the parent order information, which can limit the effectiveness to the buyside and the data can be biased by the sell-side’s own setup. For example, how or when they sweep different venues or even what venues they are connected to. The buy-side on the other hand have access to parent level information and they can see the difference between broker setups on the same type of order flow. However, they don’t see a lot of data that the sell-side have access to further upstream – such as all the interactions at the venue level, particularly those that don’t end up with a fill. So, the buy-side/sell-side relationship has evolved as a partnership to help solve the complete picture and overcome the pain points.
You do a lot of work around TCA for Vanguard – why is it so important?
This is a question I get asked a lot, I always use a quote from Vanguard founder John Bogle – “Where returns are concerned, time is your friend. But where costs are concerned, time is your enemy.” Transaction costs will compound over time and slowly erode shareholder value. That’s why TCA is so important, if we can reduce our footprint when trading, ultimately the end investor benefits from tighter index tracking. TCA is used to measure trade effectiveness, identify outliers and to meet reporting and oversight requirements. The biggest impact our team has had is measuring and increasing trade effectiveness. We talk a lot about the trade feedback loop – the insights we gain through data analytics are relayed back to the desk to enable better execution. We hold regular feedback discussions with our brokers, venues, and other market participants to increase innovation in technology, market structure and regulatory reform in order to improve the overall market ecosystem.
The tools we have access to for TCA are constantly evolving – the industry as a whole has been using machine learning techniques for a while now which has given us different insights into execution data and have proven successful, however as technology evolves there will be a bigger focus on real time execution signals produced from large data sets – which will enable us to retrain models on the go.
How can traders best leverage data insights, and how are you feeding pre- and intra-trade data analytics into your dealer selection and execution in order to improve outcomes?
Traders can leverage data insights by incorporating them into their decision-making processes at various stages of the trading lifecycle.
- Pre-trade – before executing a trade, data can inform potential opportunities and risks, which in turn can lead to a recommended trading strategy. Analysing historical market data, fundamental factors, macroeconomic indicators and other potential alpha signals to anticipate price moves. By conducting comprehensive pre-trade analysis, traders can develop well informed trading strategies that align with their investment objectives and risk tolerance.
- Intra-trade – during the execution of a trade, traders can continuously monitor market data and performance metrics in real-time to make timely adjustments and optimise trade execution. This involves leveraging data analytics tools to track trade quality and monitor liquidity conditions that can ultimately reduce trading costs. By performing intra-trade analysis, traders can adapt their trading strategies dynamically to changing market conditions to maximise efficiency.
The EMS is important for intra-trade analysis – it can help leverage visualisation tools to track performance or detect anomalies. It can be used to set up alerting or automation based on changing market conditions – that could be momentum, spread, volatility or volume metrics.
What is the future for equities execution – what’s top of your list for 2024?
The future of equities execution is poised to be shaped by several key factors – here is my list for 2024.
- Data quality and availability
- Model interoperability
- Computational efficiency
- Market dynamics
- Cybersecurity
I hope to witness in 2024 a concerted effort to collaborate and take tangible action on key items that have been discussed at length over the past few years. Recognising the collective benefit of shared standards, best practices and collaborative initiatives – market participants will increasingly come together to address common challenges and drive positive change in equities execution. Through collaborative forums and industry associations, market participants can drive progress and ensure that equities execution in 2024 is characterised by resilience, innovation and integrity.
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