Bloomberg Professional Services
This article was written by Robert Simek, Head of Product for Sell-Side Analytics at Bloomberg.
Rapid developments in technology and data management are enabling sell-side traders to scale their business by reducing friction in workflows and relying on analytics to improve the decision-making process.
“As competition for trades intensifies and margins narrow, analytics and automation have become must-haves, rather than nice-to-haves.”
Automation doesn’t happen overnight, however. The move to fully automated workflows is a journey that requires firms to take a number of developmental steps. With no one-size-fits-all solution, each phase requires a different approach depending on the type and size of trading desk.
As we shall see later, the stage at which a desk begins its journey will depend on a number of factors, in particular the volume of data it consumes and generates.
Four phases
The automation journey has four stages. At the start is the Streamlining phase, in which time-saving shortcuts are introduced, such as the pre-population of order tickets through the use of natural language processing technology.
The next two phases employ the use of rules to govern actions based on data feeds, such as communications between traders.
The first of these, the Static Rules-Based phase, introduces rudimentary directions that can initiate actions based on the presentation of triggers. One realistic example would be an instruction to use current prices every time a trade size smaller than 50 is received.
Static rules, however, are rigid and will only respond to specific instructions. They also require manual adjustments as market conditions change.
The next phase, based on Dynamic Rules, is typified by technology that brings greater flexibility and adaptiveness to the mix. This technology uses more sophisticated inputs that can harness recent market data to make important trading decisions.
In the final, Predictive analytics, phase software such as machine learning systems mine historical data to identify patterns that can help traders predict future trends at given levels of probability.
Progress through the automation evolution is marked by an increasing need for – and generation of – data as processes become more complex and sophisticated.
Indeed, data is the fuel that drives automation. Firms will find that each stage of their transition will create ever-greater volumes of data that will propel them further along the maturity model. What is most important is the focus on clean, normalized, enriched data sets that can be trusted.
Read Unlocking value from trading data won’t be easy – But it will be crucial by Robert Simek for more insight on the data journey.
A question of scale
Before embarking on their journey, desks will need to ask themselves a number of questions to establish what level of automation is suitable for them. One of the first questions they’d need to answer is what kind of desk they operate. Zero-, low- and high-touch desks can all benefit from more streamlining processes, but will need to apply technology in different ways.
The asset classes they trade will also have an impact. For instance, the high-trade volumes seen on rates desks will have completely different workflow and data requirements to mortgage traders, whose throughput will be substantially lower.
A desk’s location will matter too, bearing in mind that trading is subject to separate regulatory controls in different parts of the world. The type of client desks trade with also will have a bearing.
Taking data and these other questions into account, we can see that the various stages of maturity are likely to be dominated by certain types of firm. High-touch, relationship-focused desks will tend to be found predominantly in earlier stages, focused on capturing data from voice trading.
Bloomberg Solutions
Bloomberg works with clients at different stages of automation, giving us the expertise to offer solutions that enable workflow applications and data solutions necessary to any specific situation.
Clients can choose the level of automation at which they operate and we can suggest solutions to help them grow.
We have built and continue to enhance products that enable firms to reduce the time it takes to arrive at trading decisions. Among them, tools to help clients automate the structuring and creation of trade negotiations directly from communications, saving time, reducing errors and capturing valuable data.
Our sell-side enterprise analytics suite can help desks better understand liquidity and find trading opportunities by aggregating trading flow across the desk.
Data is key to the effective operations of financial institutions but it is often misunderstood and requires effort to produce data sets to drive automation. There are huge challenges for companies in managing and storing data safely and effectively. And having fast access to a broad range of structured and unstructured content has become a basic requirement.
Bloomberg has solutions for these issues and more. For all desks, our industry-leading robust data sets, analytics and hosting services are providing the intelligence vital to driving the automation transformation.