Case Study: Buy-Side Algo Credit Trading

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

Overview and Current Process

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.

Overbond performed a detailed study of the current credit trading execution processes and how they could be improved with AI bond pricing model capability. 

Example Of The Current Credit Trading Execution Process

Two variations on how the buy-side trader initiates RFQ for a particular bond:

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Challenges With The Legacy Systems And Processes

When the RFQ is initiated by the execution trader via the Bloomberg (or other) terminal or a phone call, there are 4 main methods of validating the pricing responses for pre-trade best execution purposes:

  1. CBBT taken from Bloomberg covering most of the cases
  2. Fixed Yield – for the special type of security
  3. Price Range – a certain range is observed and validated
  4. A certain spread range is confirmed – this might be quite wide (depending on the RFQ responses received from electronic venues and/or over the phone)

The decision on which of these methods should be used to confirm best-execution, or whether the CBBT price is good would have to be taken by the execution trader on a case by case basis. 

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Reasons driving the problem

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

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These factors lead to low confidence in the suggested prices and execution trading desks have hard time proving best execution to their clients based on the third-party impartial data feeds and pricing source. 

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Can AI Powered Bond Pricing be a solution?

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.

Clients can automate trading workflows using Overbond AI bond pricing feeds

Overbond COBI-Pricing, AI bond pricing feed can price bonds automatically with live refresh rates measured in sub 5-second range enabling automated trading workflow. Models can price 30% more bonds with low liquidity profile greatly increasing coverage and pre-trade best execution capabilities. Price is assigned for each bond under the issuer company and spread or yield curve is constructed. COBI-Pricing can systematically price a large number of liquid and illiquid securities, issuer names, and identify pricing tension metrics across large coverage book systematically

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 best-executable secondary market bond price for global IG issuers 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:

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Project Structure

Overbond structured the implementation of the buy-side best execution pre-trade pricing engine implementation project in two phases. Overbond first deployed and tested 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 was approached as a Phase 2 deliverable of the project.

Phase 1 Deliverables (Duration: 1 month) 

  1. Test web access to Overbond’s COBI pricing platform for a predefined list of 100 corporate issuers
  2. Delivery of EOD Mid-Spreads as per the COBI Engine Output for agreed ISIN universe in the form of a table in a flat CSV file
  3. Delivery of calculated Bond prices based on the COBI output indicative of the mid-price
  4. Error/Difference – Analysis (COBI Price, BBG I-Spreads, Quoted Prices, Traded Prices) 

Phase 2 Deliverables (Duration: 3 months) 

1. AI Model Pricing:

  • Front end interface developed to incorporate side by side visualization of the output of the finetuned and calibrated intra-day COBI pricing model
  • Intermediate results delivered with all ISINs/securities considered for intra-day pricing, confidence level of the modeled price and liquidity score for each ISIN
  • Input variables, such as aggregated transaction levels, fundamental data, rating and other

2. AI Model for TCA (Transaction Cost Analysis) and Best-Execution Pre-Trade

  • Bond Buyer Matching module output to serve as an input for the Best-Ex Optimization
  • Modelling trained on the trading book, executed trades in the past 2-year period
  • Results of AI model analyze the Distance-to-Cover for every RFQ initiated, ISIN/CUSIP pricing tension per counterparty, trading venue, and on top of the best executable price outputted by COBI-Pricing, model suggest best execution counterparty based on the past executed trades 
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How Overbond COBI-Pricing Algorithm works

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

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Data Intake & Pre-processing

The Overbond platform sources raw trading live date and fundamental data via Refinitiv’s live platforms. Our data sources include Refinitiv, S&P Global Market Intelligence (company level fundamental data) as well as various other sources. Overbond AI models have the ability to incorporate dealer quotations/axes and investor preferences through direct feedback loops.

This raw data is then structured in the Overbond databases. Trading data and fundamental data are structured and mapped to the appropriate issuer ID. The data is systematically scrubbed for anomalies and null values. Finally, a set of key input factors are generated based on the raw input. These include but are not limited to factors that measure secondary market spread movements, recent issuance pricing levels, nearest neighbor credit ratings, and fundamental financial metrics. These factors are divided between sector and company-specific and are used as inputs to the machine learning models.

Model Training

The subsequent stage for the machine learning algorithm is to train and apply several models to calculate the output pricing levels. An Ensemble Learning strategy is used in three phases, meaning multiple models are combined to elevate overall robustness at each training stage. These models are each trained using a subset of the past data, ranging from one day, one month to a maximum of ten years. Advanced sampling techniques were used to account for data gaps for illiquid issuers in order to construct yield curves for all tenors and all issuers in coverage universe.

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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 COBIPricing algorithms uses includes the following:

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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 markets conditions at large. Models are tuned for different liquidity scenarios. A variety of preprocessed inputs flow into COBI-Pricing’s algorithm, to generate bond pricing output.

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The first phase of the algorithm generates pricing curves for a list of companies specified by a domain expert with the highest liquidity profile. This list contains companies from diversified sectors and are frequent issuers with liquid outstanding secondary market bonds (High Issuers). The Issuer should have a minimum number of bonds outstanding, bonds outstanding across the curve, and the minimum number of trades and daily volume in secondary market for the algorithm to build a High Issuer curve using SVM algorithm.

The second phase uses a Nearest Neighbors algorithm to generate ISIN pricing curves for issuers with illiquid or insufficient secondaries (Low Issuers). Peers for each Low Issuer are identified using a score based on fundamental financial metrics, industry sector, credit ratings, secondary spreads, and issuances. The top High Issuer with the lowest blended score vis-à-vis a Low Issuer is classified as the peer. The secondary data from the top peer, along with the secondary data from the Low Issuer, is used to form an enhanced dataset for phase three to build curves.

The third phase creates pricing curves for all Low Issuers using Support Vector Regression models within SVM algorithm family, on the combined secondary data set of the Lower Issuer and the top peers as derived from the second phase.

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 its peers, as determined in phase two of the algorithm.

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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 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 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
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COBI-Pricing Intra Day API 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 COBIPricing output can be integrated into a data feed via API, presented as custom visualization, or viewed on the Overbond Platform as downloadable table.

Output Schema for Secondary Market Pricing

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Output Visualization for Designated Portfolio

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Back test 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 diversified universe across issuers and ISINs with a liquid day-trading pattern, different ratings/risk profile, and bonds across curve. Back test description is per below.

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Validation questions back-test should answer:

  1. Does the model perform consistently and deterministically in building credit curves?
  2. Is the accuracy of the model output validated by the fact that the difference between model individual ISIN price output and actual secondary trade is within an acceptable range (ie. 10 cents in price terms)?
  3. Can the model perform well in pricing ISINs with very limited trading activity/liquidity profile?
  4. Does the model construct accurate credit curves for issuers with few outstanding bonds or with illiquid bonds by using Nearest Neighbor AI approach?
  5. Does the Nearest Neighbor algorithm ensure the validity of relative value argument across sector and credit quality, effectively guaranteeing that best possible peers /comparables are utilized in pricing benchmarking?
  6. Does the application of Support Vector Regressor (SVR) which uses non-linear regression to build credit curves, effectively ensure that no significant pricing curve distortions exist when longer or shorter tenor pricing is determined across the curve?

In order to test the yields suggested by COBI, back-test methodology has been defined with various metrics that would compare the prices/yields generated to various reference prices from legacy proprietary systems and/or Bloomberg data feeds. Prices suggested by COBI model should be within 10 cents of traded best price and ideally within traded best price and legacy system suggested price.

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Back test visualization approach:

  • COBI-Pricing model provided spreads converted to price through the pricing library and stored them in a local database both with spread and price levels
  • The results are joined with various reference data elements including OMS reference executed trades prices, received RFQs record, Tradebook, Bloomberg CBBT to provide confidence in the way COBIPricing functions and can deliver results that can be used and trusted by the execution trader
  • The results are visualized to allow the stakeholders to interact and understand them

Metrics for error analysis:

Following metrics are used for prices and i-spreads against Tradebook actual trades and quotes, Refinitiv/Bloomberg, received RFQ records and OMS executed trades:

  1. Mean Absolute Error
  2. Mean Squared Error
  3. % within Bid/Ask or Mid/Ask
  4. Number of times trend break was predicted
  5. Error where trend jumps or breaks occur
  6. Performance in Time
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Back test Results

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

  1. Prices of the best received RFQ response
  2. Automatic legacy system price (Bloomberg CBBT + Automatic Legacy Adjustment)

The expectation from trading execution desk is that prices based on COBI yields should effectively correct the automated system price:

i.e. Modeled price – Traded price < Automatic system price – Traded 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 XYZ BID vs. BBG XYZ ASK), which is the category that indicates that COBI price is in line with traded price. 

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As we know, not all bonds fit perfectly on an issuer curve, some might be trading off the curve for a number of reasons while employing a curve-builder approach to price ISINs in the secondary bond market we acknowledge that the model will not be precise for 100% for the entire bond universe. Hence, we segregated the bonds based on various measures including liquidity and volatility to identify bonds which can be priced accurately, and which bonds cannot be due to various market behavior, trading pattern and data availability limitations. COBI-Pricing model output price for each ISIN also has these explainability features attached to it:

  1. Liquidity Score – indicates deep liquidity profile of the ISIN, based on bid-ask spread wideness, trade count and trade volume on the day and in recent history, peer ISIN comparisons, OTC flows from settlement layer data, price volatility intra-day and monitoring of the distribution of all correlated factors.
  2. Confidence Score – measures confidence of the modeled COBI-Pricing output price for each ISIN is within a pre-defined threshold, ie. clients can set the threshold to be within 10 cents on the price basis
  3. Confidence Interval – same as above but provides a range for a trader to consider the high and low point
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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 buy-side institutions with significant trading volumes and with over $2+ trillion of combined AUM. Their innovation groups actively explore new technologies that can serve as the catalyst for trading execution automation, pre-trade best execution, post-trade fixed income TCA, and custom AI analytics.

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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 inhouse 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?

Custom AI Services

Overbond works with clients to identify and recommend practical AI analytics use cases that are aligned with strategic goals of the financial institution. We help assess current-state AI capabilities and define roadmap to help clients realise value from AI applications. We manage cross-channel data flows across multiple systems and enable custom front-end visualizations.

Proven Methodology

With our targeted approach and implementation methodology, we quickly demonstrate value of AI analytics to test use cases, enabling client-side change management approach and stakeholder buy-in.

Operational Acceleration

We help clients build and deploy custom AI solutions to deliver proprietary analytics and tangible business outcomes. Our experience combines calibrated models, design patterns, engineering and data science best practices, that accelerate value and reduce implementation risk.

AI Analytics As-a-Service

Overbond helps customers design and oversee mechanisms to optimize and improve existing fixed income credit valuation, issuance and pricing prediction and pre-trade opportunity monitoring using AI. Our team of worldclass data scientists and engineers manage an iterative implementation approach from current state assessment to operational handover.

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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

vuk.magdelinic@overbond.com

Justin Hui

Sales Manager

+1 (289) 544-7975

Justin.hui@overbond.com