Fixed income pricing has always involved making sense of imperfect information. Liquidity is fragmented, many instruments trade infrequently and establishing fair value can require considerably more judgement than in more transparent markets. But the demands being placed on pricing and valuation infrastructure are changing.
Firms increasingly need valuations that are not only accurate, but timely, explainable and defensible. That shift is being accelerated by the growth of private credit and securitised products, greater use of artificial intelligence and increasing regulatory scrutiny of how valuations are produced.
Those pressures emerge strongly from a new A-Team Group white paper, commissioned by LSEG, “Fixed Income at a Turning Point: The Data Foundations Redefining Markets,” based on a survey of more than 20 buy-side valuation specialists across North America and the UK.
Moving beyond end of day
One of the clearest indications of change is the declining relevance of end-of-day pricing. According to the research, more than half of firms surveyed have moved from EOD to hourly or sub-hourly updates, while only one respondent considers EOD sufficient for its requirements.
There is also a geographic divide. The UK firms surveyed have already completed their migration to intraday data, while 60% of US and Canadian respondents remain on EOD delivery but are planning to make the transition.
The requirement extends beyond prices. Corporate actions, new issuance and other supporting datasets increasingly need to arrive at a speed consistent with intraday investment and risk decisions.
Complexity puts pressure on the data
At the same time, the assets that institutions are buying are becoming more difficult to value.
Private credit provides perhaps the clearest example. Some 90% of respondents are already active in the asset class or planning to expand their exposure. But private markets bring significant data challenges, from accessing borrower financials and monitoring covenants to independently verifying manager-supplied valuations and maintaining a clear pricing lineage.
Securitised markets present similar problems. CLOs, MBS and ABS require detailed underlying data and sophisticated cash-flow modelling, while fragmented liquidity makes reliable comparables harder to identify.
Technology is increasingly being used to fill those gaps. AI-assisted cohort analysis, for example, can identify comparable liquid bonds to provide additional evidence when valuing less liquid instruments.
Cross-asset information is also becoming an established part of the process. The survey found that 90% of respondents incorporate ETF price signals to some degree when valuing underlying bonds, alongside signals such as CDS indices and macro futures. What may once have been primarily a tool for stressed markets is increasingly becoming part of the everyday valuation toolkit.
AI raises a governance question
Greater use of AI introduces another dimension to the pricing challenge. Algorithms can help cleanse market data, remove noise, find comparable securities and process much larger quantities of information than traditional approaches. Natural language processing can also make complex datasets more accessible across millions of instruments.
But the survey suggests that firms haven’t yet reached a consensus over how much autonomy those systems should have. Some 40% require active human sign-off before an AI-assisted valuation is published, while another 40% allow algorithmically generated prices to be published before undergoing retrospective expert review. Just 10% are comfortable with fully autonomous valuations.
For illiquid or judgement-based instruments, a model may struggle during unusual market conditions. 75% of respondents said recent geopolitical instability had required significant adjustments to valuation model parameters.
From a price to the evidence behind it
As pricing becomes more automated, firms are simultaneously demanding greater visibility into how valuations are constructed. The research found that 85% of respondents regard pricing transparency fields as either highly important or critical. Inputs, assumptions, redemption expectations and the rationale behind a valuation are increasingly expected alongside the price itself.
Auditability and data lineage therefore become part of the pricing infrastructure rather than downstream compliance considerations. This is particularly important as regulators and internal risk teams increase their scrutiny of valuation methodologies and AI governance.
Supporting that model requires data infrastructure capable of connecting pricing, reference data, market signals and analytics across workflows. The white paper points towards cloud-based delivery, APIs, consistent identifiers and governed data pipelines as the foundations for doing that at scale.
The investment priorities for 2027 suggest firms recognise the scale of the task. Investment-grade corporate bonds received the most mentions for additional technology and data spending, but private credit was almost level, followed by high-yield and leveraged loans, emerging-market debt and securitised products.
Fixed income pricing, in other words, is becoming less about delivering a number at a particular point in time. Increasingly, firms need a continuously updated and auditable body of evidence explaining why that number can be trusted.
The white paper “Fixed Income at a Turning Point: The Data Foundations Redefining Markets,” is available for free download here.
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