No one can deny the power of innovative technology to disrupt and transform pre-existing business models. Digitally-enabled platforms have radically altered service provision in recent years. In many cases, the common theme is integrated and intelligent automation. For example, customer experience personalisation tools, such as Netflix or Amazon’s recommendation engines, have already become ingrained in consumers’ psyche as run-of-the-mill offerings.
Similar innovations are starting to take hold across financial markets. From the application of quantitative investment strategies to full advisory services, the industry is rapidly adopting intelligent and automated technology to add scale or identify new opportunities. The same applies to fixed income electronic execution venues like Tradeweb, as we look for ways to maximise efficiency between supply and demand on our platform.
Building on our track record of streamlining the trade lifecycle, we have been steadily expanding automation’s role in trade execution. Our goal is simple; use data intelligently to optimise the trading experience for all of our customers, whether on the buy- or sell-side of the equation.
Data is the key ingredient for digital applications
As a global trading platform, we have access to copious amounts of pricing and transaction related data. We are able to analyse and apply this information to improve clients’ experience and benefits in using our systems, while adhering to strict rules for data governance and compliance. It may sometimes be as simple as sharing aggregated data for monitoring or reporting purposes, though we see the biggest value-add when it is used to drive automation and scale.
Our European credit offering is just one of the many products to benefit from these tools. For instance, our Automated Intelligent Execution (AiEX) functionality allows clients to set static parameters such as trade size and number of quotes received, or to leverage more dynamic inputs such as composite pricing, as the industry is gradually moving towards more automated execution. Either way, users establish the rules and are in charge of the way the trade enquiry is processed.
The evolution of data-led intelligence
Despite recent workflow innovations and the search for the next great protocol, around 95% of cash bond e-trades in the dealer-to-client market are still conducted via a request-for-quote (RFQ)‑based process. We believe this flexible protocol can be stretched even further. Hence, we have invested our time and energy to make RFQ trading more intelligent and flexible. From tools that help clients select the most appropriate dealers at the time of trade, to substitution of dealers mid-enquiry, to “trade at best” execution assistant, we have used data and technology to constantly enhance this tried and tested trading protocol.
While human nature dictates that we retain some control or final say throughout the decision-making process, we could see people’s reliance on trading platforms gradually increase in deciding how their order is executed. At Tradeweb, we therefore felt it was important to give users similar levels of control. We use machine learning to direct trade enquiries towards the most appropriate counterparties, but ultimately the trader is free to make the decision to accept, reject or even tweak the output on a case-by-case basis.
Achieving long term balance
Any ecosystem, including the fixed income world, must consider the needs of all of its constituents in order to develop efficient long term solutions. It must also adopt a transparent and direct approach when doing so, to ensure all parties have a clear understanding of the rules of the road. Just as you couldn’t expect ‘Lane Keep Assist’ to be much use on a road without lane markings, neither should you expect the most effective trading protocol to work in the absence of information and tools available to all parties in a transaction.
In this context, market participants must be aware of how they interact with each other and with new technology. Understanding how these solutions work and how they are used will enable them to optimise their business. With automated trading, for example, it is important to ensure that the sell side is aware of the trades that are subject to automated trading rules, and also what the clients expect from the dealers. If a buy-side trader is looking for fast responses as well as target levels, dealers using some form of auto-pricing system would have a competitive edge over those still relying on manual processes to price and respond to the enquiry. This disparity would not serve the interests of either the buy side or the sell side. Our job as a trading platform is to make sure the protocols and features we develop are useful and well-understood by both sides.
The (r)evolution of credit trading
Trading venues must strive to accommodate the multiple needs and motivations of market participants. The days of simply sending a standard RFQ to a familiar group of dealers for every trade could soon become a thing of the past. Automation will solve many industry challenges, and it has opened the door to the next phase in the evolution of trading, where various degrees of autonomy could be accomplished.
The development and optimisation of trading protocols will continue for the foreseeable future, but their success will ultimately be determined by intelligence-driven automation. When a trading platform is able to leverage data to improve the quality of interaction between sell- and buy-side participants, technology is providing a ‘win-win’ scenario.
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