This article was written by Robert Simek, Head of Real-Time Pricing, and Dan Tsou, Head of ETOMS Product, at Bloomberg.
Rapid technological advances in financial trading platforms, regulatory requirements, and the growing hunger for financial assets globally are pressuring sell-side firms to invest in their own digital transformations.
As buy-side decision-making becomes nimbler and more sophisticated, the sell-side is seeking to keep pace by recalibrating its trading workflows with data-led technology. Banks and institutions are updating their trading, research, and operations teams, understanding that to ignore the opportunities offered by technology is to put themselves at a competitive disadvantage.
The case for digitalization is probably greatest among fixed-income traders, for whom voice trades and manual processes still play a large part of day-to-day business. With a small but growing sector subject to digitalization, it is a part of the global financial infrastructure that has the most to gain from a data-led transformation.
There are only winners
There are many gains that can be had from harnessing the value of the data that trading desks generate. Chief among them are greater efficiencies, better use of resources, and lower operational risks. These all help to reduce operating costs. But by applying artificial intelligence-based analytical tools to their data, companies can also generate greater value and trading income through the creation of sophisticated models and algorithms that can improve critical functions such as price discovery.
At its most basic level, digitalization can help save time and money by automating workflows. In the front and back office, routine tasks take up a large part of everyday activity. It’s essential but laborious. By automating workflows, those tasks can be performed without the involvement of skilled staff, who can use their time more effectively on critical-thinking activities.
Automated tasks can be carried out faster and reduce risks posed by manual data entry errors, slow response times, or other forms of operational friction.
Data can also be analyzed for patterns, trends, and insights that can help guide future decision-making. Whereas certain decisions would have traditionally been made heuristically by managers, automated systems can now do the same things based on a wealth of historical and other data points that even the smartest of human minds would be unable to process.
What success looks like
So how does digitalization look in practice, at a sell-side office trading fixed income assets?
In the digitalized world, data is gold, and sell-side teams are sitting on treasure troves of information that can be gathered, analyzed, and used to substantially improve their operations.
Most apparent, they collect huge pools of information on trades, prices, and counterparty demand during the day-to-day work of servicing their buy-side customers. Because that comprises structured data, it is better understood and digested by companies’ own tech stacks. Less obvious, however, and harder to seize, is the valuable data contained within voice communications between counterparties. This is where some of the most exciting innovations are happening.
Natural language processing (NLP) is a form of AI that can read chats, messages, listen to recordings or trawl through reports and social media posts to identify a mine of information. From these, firms can glean data on anything from customers’ RFQs, their future trading intentions or even public opinion on particular stocks and other assets.
Further interrogation by machine learning (ML) processes can identify patterns – and even predict future trends – in huge swathes of data that can be used by decision makers to create new business strategies and by traders to guide their next transaction.
With this information and these insights in place – and when combined with other enterprise and reference data – sell-side firms can get a 360-degree view across their business and begin making real changes to their operations and strategies.
For instance, trades can be automatically set up, ready for execution, by systems that analyze the swathes of requests for quotes (RFQs) that come into a firm. Instead of manually sending orders and waiting for prices to come back, firms can deploy automated systems to do this, enhancing workflow efficiencies by disposing of the need for traders to go through the time-consuming pre-trade preparations and leaving them to concentrate on the bigger deals or other value-adding activities.
Further, pricing models can be constructed from AI-generated analytics that enable firms to search a huge pool of asset prices so that they can offer customers more trades. And while voice trades will certainly continue for high-value trades, digitization can accelerate trading efficiency by giving traders easy access to trusted sets of prices on a wider array of assets.
Better serving the customer
Data-forward technology can automate almost any part of a business’s operations. It’s being applied to all parts of the economy, but is finding willing adoption within the fast-paced modern financial services industry. The companies that do it best are those that go about it with the understanding that the ultimate goal of any transformation is to improve the experience and outcomes of their customers.
Sell-side financial organizations are responding in this way. They are reacting in lockstep with the digitalization of their buy-side counterparts, putting systems and operations in order to service them. In the process the sell-side is reducing its operational risks, streamlining and making its workflows more efficient and cost-effective, and boosting revenue streams.
Bloomberg offers a full package of tools that sell-side clients can harness to increase the efficiency of their workflows. Our powerful pricing models are providing traders around the world with real-time prices based on an examination of billions of market data ticks. Bloomberg’s real-time pricing products are delivered through our market data feeds https://www.bloomberg.com/professional/product/market-data/. They can be fed at high-speed via APIs into a multitude of different order and trade management platforms or used in conjunction with Bloomberg’s state-of-the-art Trade Order Management System (TOMS) and Electronic Trade Order Management System (ETOMS).
TOMS is integrated with the Bloomberg Terminal so that clients can use it in conjunction with our data analytics and news feeds in order to better inform trades, while also synchronizing operations across the front-, middle- and back-office desks. ETOMS is integrated with over 40 global trading venues, offering credit market makers and private wealth execution desks enhanced electronic trading capabilities with Bloomberg analytics and AI products.
Traders can then send orders to the Rule Builder (RBLD) to execute trades via our Fixed Income Trade (FIT) tool and other trading venues.
As a result, clients can then integrate more functionality to suit their particular needs.