Case Study: Sell-Side Algo Credit Trading

Credit and Rates Trading Workflow Automation Best Execution Pricing and Liquidity Risk Analytics

Source: Overbond

Overview and Current Process

Sell-side dealers and buy-side asset managers are rapidly embracing artificial intelligence applications to price fixed income securities algorithmically in live trading environment or for purposes of end-of-day reconciliation. 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. 

It is quite evident under time pressure to respond to RFQs that this process entails a trade-off - does the trader let the RFQ expire and sacrifice trading volume as he is not sure of the price or does he sacrifice margins to make sure that he is seen as the market maker for that bond?

Challenges With The Legacy Systems And Processes

When the RFQ is received by the trader via the Bloomberg (or other) terminal or a phone call, there are 4 main methods of pricing the bond:

1. CBBT taken from Bloomberg covering most of the cases

2. Fixed Yield – for the special type of the security

3. Price Range – a certain range is added

4. A certain spread range – this might be quite wide

The decision on which of these methods should be used, or whether the CBBT price is good would have to be taken by the trader on a case by case basis. A loss of RFQ may occur due to the manual and time-consuming nature of checking fixed yields and certain spread ranges. Comparing the bond that is mentioned in the RFQ, with a similar bond from a peer, would provide good insight into the likely price of the bond, however, this would also take time.

Reasons driving the problem

The primary problem that the trader faces is due to the low confidence in prices suggested by Bloomberg CBBT and third-party applications that are currently used:

These factors lead to low confidence in the suggested prices and traders must constantly spend a great deal of time and effort in 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.

Can AI-Powered Bond Pricing be a solution?

AI Advantage over Statistical methods

COBI-Pricing, Overbond AI modeling techniques share many similarities with classic statistical modeling techniques starting from the fact that they both deal with volumes data. However, the key difference between statistical techniques and AI models Overbond applies is the goal of these approaches. While 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, AI techniques rather aim at finding by themselves the method (with underlying assumptions that are unknown) that best predicts the outcome in consideration.

COBI-Pricing was created as part of Overbond’s suite of AI algorithms for the fixed income capital markets. It algorithmically finds the most optimal indicative new issue bond price as well as relative value secondary market bond price for global IG and HY bonds, utilizing 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 trend, investor sentiment, and industry, rating or tenor cluster comparables. Data is aggregated from multiple types of data sources including:

Project Structure

Overbond structures sell-side deployment projects in two phases. Overbond first deploys and tests end of day data on a smaller universe of ISINs to algorithmically find the most optimal best executable secondary market bond price for each bond utilizing machine-learning (ML) algorithms. As mentioned before, Overbond 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 trend, investor sentiment, and industry, rating or tenor cluster comparables. Data is aggregated from multiple types of data sources and models are computationally intensive. That is why intra-day pricing is approached as a Phase 2 deliverable of RFQ automation projects.

How Overbond COBI-Pricing Algorithm works

The diagram below and the following paragraphs provide a description of ho the Overbond COBI-Pricing algorithm works.

COBI-Pricing Data Intake

Successful data pre-processing is the key stage and pre-requisite for the COBI-Pricing algorithm operation. The precision of the algorithm output is critically dependent on the accuracy, timeliness, and relevance of the pre-processed input data. Overbond sources raw data from major data suppliers in the financial sector, including Refinitiv, Ice, The Six Group, EDI, MarketAxess, Tradeweb, Euroclear, Clearstream, DTCC, CDS, S&P Global Market Intelligence, major credit rating agencies, as well as other sources. The data COBI-Pricing algorithms uses includes the following:

COBI-Pricing Model Training for Different Liquidity Profiles

COBI-pricing is an advanced three-phase AI algorithm engineered to measure best-fit correlations with respect to company fundamental valuation and secondary market pricing for their bonds across sector peers and market conditions at large. Models are tuned for different liquidity scenarios. A variety of pre-processed inputs flow into COBI-Pricing’s algorithm, to generate bond pricing output. 

COBI-Pricing handles the problem of sparse data sets, by filling the data gaps using credit-matched peers with pricing levels to arrive at best fit or best executable prices for securities. Illiquid Companies with only minimal trading activity will now have modeled and relative-value prices for secondary market securities across all tenors. Their sparse data sets are enhanced with data from their peers, as determined in phase two of the algorithm. 

COBI-Pricing Intra Day User Interface

COBI-Pricing AI output (data-feed) can be refreshed real-time or on an end-of-day basis depending on the user need. COBI-Pricing runs on the big-data set sourced from Overbond data lake. Overbond product team works with clients to customize coverage baskets based on trading style, models are then trained and backtested utilizing all data sources. The COBI-Pricing output can be integrated into a data feed via API, presented as custom visualization, or viewed on the Overbond Platform as a downloadable table.

User Interface – Live Market Pricing:

1. Primary Market - Standard tenor live visualization

2. Secondary Market - ISIN pricing live visualization

3. Issuer curve live visualization

COBI-Pricing Intra Day Data Output

COBI-Pricing AI output via data-feed, API access, can be also refreshed real-time or on an end-of-day basis depending on the user need. COBI-Pricing runs on the big-data set sourced from Overbond data lake. Overbond product team works with clients to customize coverage baskets based on portfolio strategy or trading style, models are then trained and back tested utilizing all data sources. The COBI-Pricing output can be integrated into a data feed via API, presented as custom visualization, or viewed on the Overbond Platform as a downloadable table.

Output Schema for Secondary Market Pricing

Output Visualization for Designated Portfolio

Backtest Approach

To establish that Overbond's COBI Bond Pricing algorithm deterministically constructs a fair value curve for a set of coverage issuers and can accurately price ISINs that are within the client’s coverage and with satisfactory precision and coverage. The set of coverage issuers and ISINs has been selected to represent a diversified universe across issuers and ISINs with a liquid day-trading pattern, different ratings/risk profiles, and bonds across curve. Backtest description as per below.

In order to test the yields suggested by COBI, the desk defines various metrics and tests that would compare the prices generated from the yields to various reference prices from legacy proprietary systems and Bloomberg. The benchmark set by the sell-side shop is that prices suggested by COBI built curves should be within 10 cents of trader price and ideally within trader price and legacy system suggested price.

Back Test Results

The primary goal of the backtest is to compare prices generated by the pricing engine that uses COBI yields as input and compare them to:

1. Prices in the trade book (trader adjusted price accepted by the buyer/seller)

2. Automatic legacy system price (Bloomberg CBBT + Automatic Legacy Adjustment)

The expectation is that prices based on COBI yields should effectively correct the automated system price: i.e. Modeled price – Trader price < Automatic system price – Trader price

In order to visually compare prices, all prices are scaled using automatic system price and scored accordingly. As you can see in the sample graph below displaying results for 18 ISINs, in most cases COBI-Pricing level is within desired blue line range (BBG BID vs. BBG ASK), which is the category that indicates that COBI price is in line with trader’s price.

Business Impact

Over the past couple of years, we have witnessed profound changes in the fixed income marketplace with counterparties increasingly adopting quantitative investing and liquidity risk monitoring techniques to automate their trading workflows. These include systematic algorithmic trading and liquidity risk management automation, merging of fundamental discretionary and quantitative investment styles, consumption of increasing amounts of alternative data, and adoption of new methods of pricing analysis for fixed income instruments such as AI analytics like Overbond COBI-Pricing algorithm.

Specific use cases for the COBI-Pricing algorithm application are examined to identify business objectives and key benefits below. Overbond client organizations include sell-side institutions with significant trading volumes (200-500 RFQs+ a day per trader). Their innovation groups actively explore new technologies that can serve as the catalyst trading automation and improved risk management, trade flow, pre-trade and post-trade analytics.

Implementation Considerations

Institutions considering AI predictive analytics implementation and big-data transformation projects, can employ acceleration utilizing externally calibrated models and market signals. Below are several key considerations and questions for executives in charge of AI roadmap:

1. What is the current state of our fixed-income in-house data?

2. What are our data science and engineering capabilities?

3. Are we building AI capabilities to grow revenue or cut cost?

4. How can we redefine the boundaries of our data universe or identify alternative data sources necessary to feed AI engine?

5. Given that AI learning curve is steep where do we begin?

6. How do we create and execute AI proof of concept use cases rapidly?

7. What are key success factors for our AI roadmap?

About Overbond

Overbond specializes in custom AI analytics development for clients implementing trade automation workflows, risk management, portfolio modeling and quantitative finance applications. Overbond supports financial institutions in the AI model development, implementation and validation stages as well as ongoing maintenance.

Contact

Vuk Magdelinic

Chief Executive Officer

+1 416-559-7101

[email protected]

Adam Anozy

Sales Associate

+1 (647) 973-4391

[email protected]