Sell Side Credit Trading Case Study
Dealers and investors are rapidly embracing artificial intelligence applications to price fixed income securities algorithmically in live trading environments. The current fixed income capital market data flows are inefficient in many respects to enable robust coverage and precision for AI bond pricing. Through integration with Overbond COBI-Pricing LIVE, Overbond partners with large European sell-side credit trading desks to fully automate live bond trading workflows by having precise, executable pricing in up to 30% more situations while preserving an optimal hit ratio, 5 cents precision, and doubling the daily response volume of RFQs. This would not have been possible without the integration of AI-powered analytics Overbond COBI-Pricing LIVE provides.
Interoperability Case Study
Overbond’s COBI-Pricing LIVE uses three layers of interoperability to automate trading: it ingests and aggregates data from multiple sources, it integrates with the legacy systems on the desk, and it communicates with electronic trading venues. Interoperability of COBI-Pricing LIVE with legacy internal systems means trading desks can make COBI-Pricing LIVE their own by accessing recorded transaction history to train the model according to the trading style of the desk and significantly increase P&L. Interoperability with electronic trading venues allows for the ability to trade on multiple platforms and opens the door for smart order routing, allowing desks to optimize execution.
Buy Side Case Study
Buy-side asset managers are rapidly embracing artificial intelligence applications to price fixed income securities algorithmically in live trading execution environment or for purposes of end of day reconciliation and portfolio construction. The current fixed income capital market data flows are inefficient in many respects to enable robust coverage and precision for AI bond pricing. Markets remain heavily reliant on segregated and manual data operations between counterparties creating disparate data sets. These disparate data sets cause the market to suffer from information asymmetry and decentralization. As a result, insight from available data is fragmented and disseminated through manual exchanges between counterparties, which furthers the creation of disparate data sets.
Hydro One Case Study
With CAD$12.445 billion of outstanding bonds as of April 2020, Hydro One had several challenges when it came to monitoring its credit spreads and those of its peers in order to issue bonds in the most cost effective manner. By working with the Overbond Treasury Debt Capital Service and automating its data intake and analytics, Hydro One can now identify opportunities to reduce the cost of funding faster and more efficiently. In short, “price tension” in markets, benchmarks, historical data and investor sentiment can now be identified more quickly. Going forward, Hydro One will be looking to incorporate Overbond’s COBI Liquidity scoring feature to strengthen understanding of the liquidity of their own bonds as well as their peers’ bonds in the secondary market. The COBI Liquidity module utilizes metrics such as bid-ask spreads, trade count and volume, and intraday price volatility for the wide spectrum of bonds to derive a daily liquidity score for each bond.