This is a guest post co-written with Mutisya Ndunda from Trumid.
Like many industries, the corporate bond market doesn’t lend itself to a one-size-fits-all approach. It’s vast, liquidity is fragmented, and institutional clients demand solutions tailored to their specific needs. Advances in AI and machine learning (ML) can be employed to improve the customer experience, increase the efficiency and accuracy of operational workflows, and enhance performance by supporting multiple aspects of the trading process.
Trumid is a financial technology company building tomorrow’s credit trading network—a marketplace for efficient trading, information dissemination, and execution between corporate bond market participants. Trumid is optimizing the credit trading experience by combining leading-edge product design and technology principles with deep market expertise. The result is an integrated trading solution delivering a full ecosystem of protocols and execution tools within one intuitive platform.
The bond trading market has traditionally involved offline buyer/seller matching processes aided by rules-based technology. Trumid has embarked on an initiative to transform this experience. Through its electronic trading platform, traders can access thousands of bonds to buy or sell, a community of engaged users to interact with, and a variety of trading protocols and execution solutions. With an expanding network of users, Trumid’s AI and Data Strategy team partnered with the AWS Machine Learning Solutions Lab. The objective was to develop ML systems that could deliver a more personalized trading experience by modeling the interest and preferences of users for bonds available on Trumid.
These ML models can be used to speed up time to insight and action by personalizing how information is displayed to each user to ensure that the most relevant and actionable information a trader may care about is prioritized and accessible.
To solve this challenge, Trumid and the ML Solutions Lab developed an end-to-end data preparation, model training, and inference process based on a deep neural network model built using the Deep Graph Library for Knowledge Embedding (DGL-KE). An end-to-end solution with Amazon SageMaker was also deployed.
Benefits of graph machine learning
Real-world data is complex and interconnected, and often contains network structures. Examples include molecules in nature, social networks, the internet, roadways, and financial trading platforms.
Graphs provide a natural way to model this complexity by extracting important and rich information that is embedded in the relations between entities.
Traditional ML algorithms require data to be organized as tables or sequences. This generally works well, but some domains are more naturally and effectively represented by graphs (such as a network of objects related to each other, as illustrated later in this post). Instead of coercing these graph datasets into tables or sequences, you can use graph ML algorithms to both represent and learn from the data as presented in its graph form, including information about constituent nodes, edges, and other features.
Considering that bond trading is inherently represented as a network of interactions between buyers and sellers involving various types of bond instruments, an effective solution needs to harness the network effects of the communities of traders that participate in the market. Let’s look at how we leveraged the trading network effects and implemented this vision here.
Solution
Bond trading is characterized by several factors, including trade size, term, issuer, rate, coupon values, bid/ask offer, and type of trading protocol involved. In addition to orders and trades, Trumid also captures “indications of interest” (IOIs). The historical interaction data embodies the trading behavior and the market conditions evolving over time. We used this data to build a graph of timestamped interactions between traders, bonds, and issuers, and used graph ML to predict future interactions.
The recommendation solution comprised four main steps:
- Preparing the trading data as a graph dataset
- Training a knowledge graph embedding model
- Predicting new trades
- Packaging the solution as a scalable workflow
Read the full case study on aws.amazon.com