Episode 1 introduces the rise of predictive pricing in fixed income markets and explains how SOLVE Px uses generative AI to improve transparency by predicting where bonds are likely to trade, based on real-time market data.

Episode 2 explores how SOLVE Px generates forward-looking bond prices using quote data, executed trades, and bond reference information and cvers the machine learning process that underpins the model’s pricing predictions.

Episode 3 details the datasets that power SOLVE Px, including curated quotes, trades, and reference data and explains the model’s use of over 300+ distinct feature inputs and how data quality impacts prediction accuracy.

Episode 4 covers how SOLVE Px is trained, validated, and retrained over time and discusses backtesting, model updates, and the release of the confidence score as part of the system’s ongoing improvement.

Episode 6 outlines SOLVE Px’s expansion beyond municipals into corporates and other asset classes and discusses how the model scales across sectors, credit types, and market conditions.