Overbond, the API-based credit trading automation and execution service, has launched artificial intelligence-driven liquidity and price confidence analytics, that are designed to auto-adapt to trade size and direction.
The firms is targeting buy-side traders’ goals in increasing the speed and quality of execution and manage trading costs, and the potential disparity between prices and liquidity on the bid side and prices and liquidity on the ask side for a given CUSIP/ISIN.
The Overbond data science team has engineered an AI analytic that separately measures inventory for buy and sell trades in order to help execution desk traders executing trades at the size desired, using analytics that provide liquidity discovery and size-sensitive pricing.
The team has used auto-adjusting AI to aggregate data from pre-trade streams and post-trade historical data and applied additional modeling tools such as case-mix adjusted cluster analysis (CMAC) and total market capacity per ISIN/CUSIP. This applies auto-adjusting AI with new mathematical techniques such as CMAC from sister fields such as medical science.
“Adjusting Overbond liquidity and price confidence scores for trade size and direction greatly enhances the powerful edge that Overbond buy-side clients gain from using our AI suite,” said Vuk Magdelinic, CEO of Overbond. “Traders will get increased coverage and precision of our automated execution API feeds.”
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