Todays buy-side firms are rapidly adopting automated electronic trading. The benefits are numerous: reducing errors, compliance with best execution, capturing market opportunities and, most significantly, freeing up trader bandwidth to focus on higher-value activities. In this regard, automation is both a natural evolution of performing a process electronically and a manifestation of the efficiencies that the buy-side is always looking to gain.
Greater adoption of e-trading in fixed income has improved large-scale data aggregation and interpretation. As a result, there has been an increase in analytical activity on the buy-side to better understand counterparty relationships. With automation, traders are able to apply these analyses to trade execution, ensuring that each future trade leverages this data intelligence.
However, buy-side firms should approach automation implementation with strategic consideration. It is important to choose the right solution to support a businesss trading strategy and to have confidence in its execution logic. This requires equipping it with the numerous data sets a human trader uses to make decisions, and allowing the tool to map this data to flexible rules that respect the nuances of the traded security. Additionally, the solution needs access to real-time counterparty pricing information and any metadata around that price, such as time to quote and characteristics around firmness. It would also want to understand which dealers are axed on that position, the quality of the axe and that dealers general activity in that sector. Firms internal compliance policies which may impose hard restrictions in counterparty inclusion can also be programmed into the rule.
Tools like Bloombergs Rule Builder gives firms the flexibility to compose their own rules against all market data points available on the Terminal. Asset class, rating and order size are key criteria to determine order automation, and traders can also consider overlaying those with more advanced measures in the same rule such liquidity scores unique to Bloomberg and even a price volatility check. These tools also guarantee compliance with best execution requirements by providing comprehensive audits detailing what decisions were made and why, based on the established rules.
Upon implementation, an automated trading solution like Rule Builder becomes an extension of the human trader, complementing people with machines to help drive measurable productivity gains. Significant new mandates may no longer require a major investment in more traders as firms can instead leverage automation to optimize the trading process. This reallocation of resources affords traders the freedom to redirect their time to high-touch orders and more profitable relationship-building tasks.
For example, among private banks and wealth managers, Bloomberg has seen a seamless integration of automated trading in the fixed income space. It is especially beneficial for odd-lot trades, typically viewed as low-touch trades, which tend to be executed at high rates. As these trades executed via automation, traders are able to spend more time on round and block orders as well as increasing their capacity to take on more clients.
The skill sets of execution desks are evolving as the demand for data analytics and technological capabilities to manage automated trading grows. The low-touch and high-touch trading roles are not separating but rather shifting to lessen the manual work across the board.
Automation tools will always be informed by data analytics while leveraging intelligence from traders to execute against what is programmed, and only what is programmed. This technology enables the future of buy-side trading to profit from zero-touch execution without compromising trader control.
Ravi Sawhney,Head of Automation & Analytics, Bloomberg LP
