Pricing Insights: Why pricing is foundational to automation

Bloomberg Professional Services

September 9, 2024

This article was written by Robert Simek, Head of Business, Real Time Pricing – Global Fixed Income and David Krein, Head of Real-Time Pricing Research at Bloomberg.

Automation is transforming trading workflows and protocols on both the buy-side and sell-side of fixed income trading. But this leap forward requires accurate and reliable pricing data. Acting on that data isn’t just about having the right numbers; it’s about understanding and responding to the market dynamics that those numbers represent.

Pricing for corporate bonds has three core principles: It must be consistent, it must be readily available, and it must be high-quality.

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What pricing means in bond markets

The bond market is over-the-counter, and there’s no centralized exchange such as the Nasdaq. In the bond market, counterparties are speaking with each other, making agreed-upon trade reference prices even more critical.

However, the sell-side usually requires more context around the price. Sell-side players want to know how the price is generated, along with contextual data points and other insights to evidence the pricing. On the other hand, the buy-side is more concerned with pricing’s consistency, availability, and quality. Thus, demystifying what goes into pricing a bond, can be a complex undertaking.

Four phases of implementing pricing in automation

Buy-side and sell-side market players can benefit from automation that helps to bridge the gap in pricing information. In fixed income trading automation can be thought of as a four stage model. At each stage, high quality data becomes more important — and more of it is needed. Importantly, this data must be high-quality, clean, normalized, enriched with metadata.. Here’s those four phases in order:

Streamlining: This initial phase focuses on improved efficiency by streamlining operations. Automation tools integrate pricing data to simplify tasks like order ticketing, where natural language processing can pre-populate fields to reduce manual input and human error.
Static rules: At this stage, automation relies on set rules that trigger actions based on specific pricing data inputs. These rules are straightforward, but they aren’t flexible. When market conditions change, these rules must be adjusted accordingly. Pricing data is typically used as a guard rail.
Dynamic rules: As automation evolves, it incorporates dynamic rules that adapt to changing market conditions. This phase implements advanced algorithms that factor in recent or real-time pricing data and data that can explain price movements, for example news or corporate actions. for more responsive and strategic trading decisions.
Predictive analytics: The most advanced stage of automation relies on machine learning models to analyze historical pricing and trading data, forecast future market trends and inform trading strategies. This predictive capability is pivotal for proactive decision-making in automated trading.

Desks will have to evaluate their current state and future needs to determine which level of automation is right for them. High-touch and low-touch desks have different needs, for example, while desks trading different asset classes will have different automation requirements. Another factor is geography, as the regulatory environment for a desk can affect its approach to automation.

When clients assess data pricing solution, some of the questions they ask include:

Does it cover all the bonds you want?
Is it consistently available during trading hours and even off-hours?
Is it available in price spread and yield terms?
Does it provide transparency metrics and relative value metrics?
Is it available for new issues as they come out?
Is pricing information available everywhere we need it and in the forms we need it?

Whatever the stage of automation, Bloomberg works with clients to offer solutions that enable workflow applications and data solutions that make the most sense for the specific situation.

Solutions for improved pricing

Bloomberg’s approach to pricing mirrors the precision and reliability needed in today’s trading environments. IBVAL Front Office assists this need by striving to provide a high-quality pricing source with better data and training transparent methodologies.

Traders start their day in Bloomberg, and they have certain expectations for the information they view and the user experience they receive. IBVAL Front Office builds on those elements. Clients on the buy-side and sell-side know that what they see elsewhere in the ecosystem is also in IBVAL. Notably, IBVAL is live in two of Bloomberg’s major automation tools – Rule Builder (RBLD) and Portfolio Trading Basket Builder (PTBB).

Bond prices are the only inputs, offering a consistent experience with the assumption that all available information about the bond market is in the bond market. By offering consistent pricing data which seeks to be representative of available market data for a wide range of bonds during trading hours and off-hours, Bloomberg gives financial entities continuous availability of data that is crucial for maintaining the fluidity and responsiveness of automated trading platforms.

Looking for more information on fixed-income pricing? Check out our IBVAL solutions page here.

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