As bond markets evolve, Eugene Grinberg, chief executive of SOLVE, explores how the introduction of innovations such as AI and machine learning are being incorporated into predictive pricing, to reshape the fixed income landscape. 
Bond markets have always been complex, but today’s environment adds new layers of difficulty. Investors and traders must navigate economic uncertainty, liquidity challenges, and shifts in interest rates while pricing securities that often do not trade daily. In this setting, confidence in pricing is more than a convenience. It is a necessity.
In response, recent studies and market behaviour point to a shift toward predictive pricing models powered by real-time data and artificial intelligence as a promising path forward.
Take the municipal space for example. A 2024 paper, ‘machine learning-based relative valuation of municipal bonds’, shows that similarity‑based models trained on large datasets outperform rule‑based heuristics and human approximations when estimating yields and spreads. Growing academic focus on model-driven pricing reflects the challenges of the municipal sector, where only 1-2% of securities trade daily despite more than one million bonds outstanding.
This is not isolated to the municipal market. A 2025 study from the University of Pittsburgh and Stanford University found that deep learning models significantly improve pricing accuracy for corporate bonds as well. The researchers evaluated models trained on issuer fundamentals, macroeconomic indicators, and bond-level characteristics across both investment grade and high yield securities. They compared models designed to minimise pricing error with those optimised for portfolio-level performance metrics like the Sharpe ratio. Models using the latter approach delivered more accurate pricing signals, particularly for less liquid bonds. This study reinforces how machine learning can close persistent gaps between observed market prices and traditional valuations in the corporate bond space.
Taken together, these findings point to a larger reality. Many firms still rely heavily on rigid models or evaluated pricing, which, for the front office, can fall short in today’s fast-moving and fragmented markets.
The rise of predictive pricing in practice
Traditionally, bond pricing has been opaque. Traders rely on indicative prices or evaluated pricing data that may not reflect real-time market conditions. These estimates are often built on historical trades or loosely comparable securities, which means they can lag behind actual market movements. When markets are volatile or bonds are illiquid, that gap between an evaluated price and a realistic trade level can widen quickly.
Forward-looking firms are beginning to address this lack of transparency by incorporating AI and machine learning algorithms, leveraging real-time quote data with executed trades to provide a more accurate view of where a bond might trade next. These advances are not theoretical. In some cases, back-tested models covering millions of trades have demonstrated median yield errors as lower than three basis points and median price errors of just over 20 cents. When live quote data is included, those yield errors improve even further.
This level of precision matters. Traditional pricing methods can be inaccurate, especially for bonds that trade infrequently or in smaller sizes. In fact, smaller retail trades show significantly higher error rates than larger institutional blocks. Errors also vary by rating, maturity, callability, time of day, and even geography. In states with deep liquidity and active quoting, predictive accuracy is measurably higher than in regions with lower trade volumes.
Quote data plays a powerful role here. While trades provide ground truth, there are far more observable quotes in the market on any given day. In the municipal bond market alone, there may be several times more quotes than trades. When that quote data is parsed and incorporated into AI models alongside reference and trade data, it significantly enhances prediction accuracy and market visibility.
That visibility is valuable across the trading lifecycle. For the buy-side, predictive pricing supports more informed decision making, portfolio construction, and relative value analysis. For the sell-side, it helps generate trade ideas, manage client relationships, and improve quote accuracy. For both, it can serve as a real-time check on internal models, a signal for price discovery, and a critical reference point when liquidity is thin.
Predictive pricing is not meant to replace human judgment. Traders will always bring critical context and intuition to the table. But when machine learning models are updated frequently using hundreds of features and millions of data points, they can provide a reliable pricing signal that cuts through noise and guesswork. Predictive pricing becomes especially useful when markets are moving quickly, and firms need to act with confidence and speed.
Redefining the future of fixed income
The future of fixed income will not be one-size-fits-all. Predictive pricing must account for trade size, side, credit quality, and dozens of other factors that influence how bonds actually change hands. Some firms are responding by delivering separate prices for retail and institutional lot sizes while continuing to expand their datasets and refine their models.
Over time, predictive pricing is expected to become standard practice. As asset managers form strategic partnerships with software and data providers, predictive pricing will be embedded in workflows, referenced alongside dealer quotes, and used as a benchmark in client conversations. As more market participants embrace predictive pricing, we will see a meaningful shift toward greater transparency, consistency, and efficiency in the bond markets.
Confidence in pricing is not a luxury. It is the foundation for better trading, stronger portfolios, and a more resilient market. In a market that rewards precision, predictive tools will define the firms that lead.