There is more fixed income data available to leverage in bond pricing than ever before. While the right data can drive improved execution, the wrong data can hurt the bottom line. We are seeing the best returns coming from firms that have the right balance of data.

Firms need to understand how market data and modeled data play together. To understand what balance is right for your firm, think through these critical questions:

• What types of objective data should be considered?

o Market data provides real-time insight into prices, spreads and yields of active and often inactive securities. It has the highest correlation to supply and demand characteristics. This indicative data is typically aggregated from parsing technology and other third party contributors.

o Executable data, gathered from executable trading platforms, provides real-time pricing data that is actionable with the click of a button without human intervention. Platforms like MarketAxess and TMC provide these important indicative inputs.

o Trade data is disseminated daily through internal and external trade recaps and back office reporting. TRACE is commonly leveraged in date specific bond pricing, and MSRB provides similar insight for muni bond pricing.

• What is the right mix of historic and real-time pricing data?

o Trade data shared minutes after a trade is the best indicator of value. That said, market data is more robust than trade data. This indicative data covers both the traded universe as well as the non-trade universe. It can be used for analysis, trends, relabeling, back testing models and algos. It should be used when it is available for a specific security, product or sector. Not all securities trade every day or week or at institutional size, so, having market data to fill gaps is important.

• What is the right mix of historic and real-time pricing data?

o Relying exclusively on objective data can lead to false conclusions. Bringing in human judgments to balance data is critical. Subjective data is typically based on market and industry conventions for bond pre-payment and default expectations. Where your firm has invested in accurate data, leverage it. Where bond data may be questionable, consider greater weight on subjective inputs.

• How are subjective pricing decisions made across the firm?

o Subjective data are a set of assumptions about the future cash flows of securities. Those assumptions will vary based on market conditions and include probabilities and severity of delinquency and pre-payment. Independent Pricing and Valuation (IPV) teams will share the responsibility for prices, spreads and yields. They also manage the risk across securities on the balance sheet.

• How clean is your market data?

o As mentioned, accurate market data is the best indicator of a bond’s true value. But like with all pricing models, garbage in – garbage out. Real-time prices are coming from different messages across channels of messaging traffic. Parsing messaging traffic can be tricky without language processing and machine learning solutions. Ensure that the market color your firm is viewing is accurate.

Relying on indicative data alone limits coverage and insight across securities. Relying on trade data alone restricts overall price transparency. Understanding the interplay between the two will allow your firm to execute at the best levels.