Overbond has added a rich-cheap model to its suite of AI fixed income analytics, allowing buy-side desks around the globe to generate systematic return with a real-time, fully back-tested methodology that is part of a scalable and interoperable trading system.
Overbond has added a rich-cheap model to its suite of AI fixed income analytics, allowing buy-side desks around the globe to generate systematic return with a real-time, fully back-tested methodology that is part of a scalable and interoperable trading system.
We’re in a long-running environment of low-rates, tight credit spreads and low secondary-market liquidity. In this new landscape, electronic trading and the use of AI for trade automation have become the new standard and investors are increasingly turning to quant trading and AI in fixed income to increase alpha.
This increased electronification of the markets has created a large amount of useable and accessible real-time and historic trade data that can be aggregated by clients to use for analysis by cutting-edge AI tools. This new generation of AI is being used to perform sophisticated relative value analysis, including enhanced rich-cheap analysis
“Overbond has harnessed AI and the wealth of new transaction information to bring quantitative trading to buy-side desks through an enhanced rich-cheap model. This new model provides insight well beyond the traditional rich-cheap analysis and is more powerful than what can be created through spreadsheet methods or factor analysis alone,” says Vuk Magdelinic, CEO of Overbond.
Combining both static and dynamic analysis of multiple factors with AI, Overbond’s rich-cheap model provides a quantitative method for screening for mispriced fixed income securities. It’s a mean-reversion valuation model designed to pre-identify bonds as rich ‘sell’ and cheap ‘purchase’ candidates based on proprietary Overbond valuation metrics and AI non-linear optimisation.
