Exec Q&A: Petra Wikström, BNP Paribas
What is the buy side talking about these days?
Aside from the usual lamentations regarding not being able to find natural liquidity, lack of blocks and the cost of data feeds, the buy side is looking at new ways of applying artificial intelligence (AI), deploying alternative data and trade cost analysis (TCA) to generate alpha. Some are looking to their brokers as partners in navigating this new business climate.
In an interview with Traders Magazine, Petra Wikström, Global Head of FX Execution & Alpha Solutions, BNP Paribas shared her thoughts on the buy side’s focus on these aforementioned topics and how her firm is trying to assist its clients. She also touched on how automation and TCA applies across OTC, over-the-counters in futures and future TCA technology applications.
What follows is an edited version of the conversation:
Let’s start with the recent buy side focus on AI and using alternative data. What are you seeing?
Bridging quant and sales we naturally interact with a lot of our buy-side clients on technical questions and aspects, and some of their new AI initiatives or uses of alternative data. This is a topic that just over the last year is coming out more and more frequently in discussions. For example, what we see is that execution desks and analytics teams are looking into broader use of artificial intelligence, AI, and how to incorporate that into their processes.
Is this a deep dive by the buy side?
From what we see at this stage, it’s very much exploratory. I think everyone wants to be prepared for when this becomes mainstream, to be able to say we have done all our due diligence and testing and we are technically aware of how to use it. This is relevant across both the active or passive manager community and really all client segments, and they may also be collaborating with academia for specific projects.
Where does the data aspect of AI fit in here?
The AI and the technology is actually one piece of the puzzle, but then you also want to make sure that you are incorporating data in the right way, and know how to process and interpret the results. One of the things we’ve seen from clients is in these projects is that they have some of the data, but they are also in need of a bigger universe of data. This is an area where there needs to be strong coordination of data scientists and practitioners.
You might be at different levels of advancement depending on the buy-side organization, and some of them might have come very far, but overall, we see more and more of these topics coming up on a discussion basis across most clients. They are indeed having advanced internal discussions exploring AI and how they can use it more within the organization. So, I can say the bulk of the buy side community are in the exploratory phase when it comes to AI and alternative data.
How do automation and TCA apply to over-the-counter securities and futures?
TCA is such a broad topic addressing execution performance, and is specifically applied as a tool to comply with regulatory transparency requirements. MiFID II has been a core driver for the employment of TCA across the industry today. So, from a regulatory perspective, naturally, TCA lends itself as a tool to help demonstrate best execution across financial securities, for both the buy and sell side.
Particularly in Europe, we’ve seen the regulatory transparency requirements contribute to an acceleration of the preexisting trend of increased automation and electronification, because TCA really demands an audit trail of execution. To be able to measure it, we need to know the time frame, the timing of the trade, the duration of the trade, and the more we have systems and technology in place to store databases, the more rigorous a TCA process we can do. So, increased electronic trading and automation lends itself well to build that audit trail.
What about on a global scale?
The adoption of TCA is indeed global. The buy-side firms that have a global presence need TCA so they can measure performance consistently across time zones and trading centers. They adopt TCA across the board, across geographies. Also, the continued, increased demand for transparency from the end-client on their managers puts focus on TCA globally.
What is the future of TCA? Is it more technology being incorporated into it or is bringing TCA to more asset classes?
Yes, so a couple of things. TCA has been adopted for a long time in the equity markets, so it’s far from a new topic, but it’s become a priority topic because of MiFID II. Further, automation and data science facilitates the adoption of TCA as it requires an audit trail and data. I think in terms of 2017, this was really the year where we saw that a lot of focus on the buy-side was to get ready for MiFID II in January 2018. And with that, a lot of practical decisions needed to be made. How do we deal with TCA? Do we use it internally? Do we use a third party provider? What additional information can we get from our brokers? So, all of those decisions needed to be mapped out and then the industry could start the implementation of getting systems and processes in place.
With these processes in place, what we’ve now seen already on the buy- side is a core investment focused on quant, data and technology. Firms are figuring out ways of further improving the execution processes across asset classes. So now, when we have the systems in place, and with more data available than ever, how do we utilize that data for improving efficiencies from investment or hedging decision to trade implementation across liquidity providers.
For the future of TCA and technology we see ever increased interest from clients in liquidity analytics, and a demand of flexible access points of external analytics and data that fit into their system. With the growth of electronification, the adoption of algorithmic executions, and especially algorithmic execution when you spread your order over a period of time, the risk has really moved from the sell-side to the buy-side. So, the buy side has a lot of need for real-time TCA and insight of how risk evolves over the course of the order. This is crucial to help institutions pilot there execution. Systems and technology that provide analytics and data feeds are very much in demand. Further AI and machine learning can both be part of the execution strategy as well as the selection process.
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