The structure of fixed income markets has changed dramatically over the past decade, making it more difficult for sell-side fixed income desks to remain profitable. The emergence of electronic all-to-all platforms and non- dealer liquidity providers has introduced new competition and the use of algorithmic and high-frequency trading has reduced the time dealers have to respond to inquiries.
Sell-side dealers and buy-side asset managers are rapidly embracing AI applications to price fixed income securities algorithmically in a live trading environment or for end-of-day reconciliation. Overbond analyzed the credit trading process at a typical large European bank and determined how it could be improved by integrating an AI bond pricing model. Under the legacy credit trading process, traders receive RFQs through one of two processes:
These factors lead to low confidence in the suggested prices, and traders must constantly spend a great deal of time and effort manually adjusting prices based on prior knowledge and intuition. The major trade-off is thus accuracy versus time, leading to missed deals and direct downward pressure on desk P&L.
AI modeling techniques share many similarities with classic statistical modeling techniques. Statistics provides the building blocks upon which the machine learning that drives AI is built and both use large amounts of data. But statistics is purely mathematical and primarily descriptive with some ability for inference. AI adds additional programming, made possible with modern computing power, to move one step beyond statistics and become predictive.
The goals of the two methods are different. Statisticians start with a set of known assumptions that are given to the model and best explain the expected behavior of the financial outcome in consideration. With AI techniques, the underlying assumptions are unknown and the aim of the model is to determine itself the method that best predicts the outcome in consideration.
Corporate and Government Bond Intelligence (COBI)-Pricing was created as part of Overbond’s suite of AI algorithms for the fixed income capital markets. It algorithmically finds the optimal indicative new issue bond prices and secondary market bond prices for global investment grade (IG) and high yield (HY) bonds, using machine-learning (ML) algorithms. The ML algorithms analyze millions of data points related to factors such as historical pricing trends between similar bonds and similar issuers, intra-day pricing volatility, trading volume and counterparty composition, company fundamentals, investor sentiment and industry, rating or tenor cluster comparables. Data is aggregated from multiple types of data sources including:
Overbond’s COBI-Pricing LIVE is a customizable AI pricing engine that assists traders in automating pricing and trading workflows for global investment-grade bonds. It generates prices and liquidity scores for more than 100,000 fixed income instruments and builds curves for more than 10,000 issuers in various real-time liquidity scenarios.
The full interoperability of COBI-Pricing LIVE allows its AI algorithms to ingest, aggregate and process data from live and historical vendor feeds, internal historical records, OTC settlement layer volume records, and now voice transactions. As a result, Overbond AI pricing, liquidity scoring, LIVE trading automation and routing algorithms can now capitalise on all primary and agency trading routes, voice or electronic, across all venues and counterparty types.
The Overbond margin optimization model adjusts to the desk's approach by adapting margin based on the availability of a bond in the market and a market risk tolerance.
The model uses case-mix adjusted cluster (CMAC) analysis to separate country, sector, issuer and issue-specific risks in bond prices and isolate market conditions through time.
CMAC is used to group similar observations into a group or cluster. So, for instance, all bonds whose prices rise when oil prices fall might be lumped together irrespective of their other characteristics.
But there are hundreds of dimensions that affect bonds. For instance, bonds sensitive to oil prices could include bonds from airlines, manufacturing or trucking, have multiple maturities and be rated differently —the number of clusters of like attributes can quickly become staggering.
Overbond structured a project implemented for a large European bank into two phases. Overbond first deployed and tested end-of-day data on a smaller universe of ISINs to which ML algorithms were applied to find the best executable secondary market bond price for each bond.
Intra-day pricing was approached as a Phase 2 deliverable of the project because Overbond ML algorithms analyze millions of data points aggregated from multiple data sources and the models are computationally intensive.
A back-test was conducted for a sell-side trading desk that trades in Euro and USD. The trading performance of the Overbond model was compared with the record of the trading desk without AI assistance.
All RFQ volumes traded in Q2 and Q3 in 2022 were compared, which included 7,551 accepted (and accepted but tied) RFQs and 7,190 covered (and covered but tied) RFQs on both the bid and ask sides.
The trades were filtered to include only 9,704 trades that involved senior unsecured corporate bonds, of which the trading desk accepted 5,083 and the model accepted 3,018.
The margin optimization model was optimized for maximum profit capture or minimum distance-to-cover, so the prices produced by the model would be expected to be closer to cover than those quoted by the trader.
The cost of margin for each trade was measured as the distance to cover (in cents) multiplied by the size of the trade (in Euros).
The model additionally looks at the rejected RFQ’s and based on the proportion falling into the Tier 1 category (the most liquid bonds) then performs a scenario analysis to determine the potential opportunistic P&L capture.
The above table shows the scenario analysis for actual Q2 - Q3 2022 data, along with 3 additional scenarios to highlight the potential variance for portfolios with differing numbers of Tier 1 categorized bonds.
Assuming a mid-range 40% increase in hit rate for the book in question, the trader would expect to see an opportunistic annualized P&L gain of over EUR 2 million.
Overbond normally outputs by API but results can also be displayed on the Overbond user interface. The figure below shows how the results would appear to a trader using the Overbond UI front-end platform.
The columns “Margin Bid (M)” and “Margin Ask (M)” are composed of the “distance to Bid (M)” and “distance to Ask (M)” respectively, on top of the COBI priced “Bid” and “Ask” quotes for each bond.
Overbond has created positive business impact for clients around the globe, including sell-side institutions with significant trading volumes (200-500 RFQs+ a day per trader). We work with clients’ innovation groups to actively explore the application of new technologies that can serve as the catalyst for trading automation and improved risk management, trade flow, pre-trade and post-trade analytics. These technologies have direct business benefits.
Overbond is a developer of process-redefining, AI-driven data and analytics and trade automation solutions for the global fixed income markets. Overbond performs market surveillance, data aggregation and normalization, and deep AI quantitative observation on more than 100,000 corporate bonds and fixed income ETFs. Applying proprietary artificial intelligence to pricing, curve visualization, market liquidity, issuance propensity, new issuance spreads, default risk and automated reporting, Overbond enables trade automation and enhances trade performance and portfolio returns. Clients of Toronto-based Overbond include global investment banks, broker dealers, institutional investors, corporations and governments across the Americas, Europe and Asia.
Vuk Magdelinic | CEO
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