By: Jishnu S, Senior Consultant, BFSI

Artificial Intelligence (AI) for bond issuance prediction is a relatively new concept, especially given the information asymmetry between investors and issuers, which makes it extremely difficult to predict bond prices and investors. To overcome this, AI can provide predictions on potential investors based on past investments made in different sectors by the various investors. This approach involves leveraging large datasets and algorithms to predict bond prices based on a variety of factors such as asset pricing, credit spreads, industry growth, economic indicators, etc.

Bond issuance process

• Checks and verification:
  First, the company or the institution reaches out to the bank and explains the finance requirements to expand the business. The bank will further check the company’s financial situation; this includes verification of information in financial statements, tax documents, asset and liability management procedures, collateral (if applicable) and shareholder structure, and background checks on management. These checks help the bank to determine whether to go ahead with the bond issuance or not.
• Rating and documentation:
  After the rating process is complete and the rating agency has decided on a rating for the company, it will prepare a report about its financial strength, management, and business prospects. This will help potential bond investors to determine if the company is a worthy investment.
• Roadshow:
  During this time the issuer will present its business model, growth prospects, and the key commercial factors that make its business a good investment opportunity. At the same time, potential investors will have the opportunity to question management about how the business works, where it is going, and how it will compete with other similar businesses in the future.
• Bond issuance on the market:
  The company will issue a news release to announce the bond offering, specifying the amount being offered, maturity date, coupon rate, and any information pertinent to the bonds.
• Allocation:
  After the book has been refined and closed, a decision is made about how much to give each investor. This decision is based on both the quality of investors as well as the objectives of the issuing business. Allocations are based on preferential treatment for long-term holders of securities, and on who can provide liquidity to the market. This can include brokers with electronic trading capabilities as well as investors who can properly value securities with limited information.

Current challenges in finding investors

The bond market is a $100 trillion market, and it is the largest in the capital market space. There are more than 1 billion bonds outstanding in the various exchanges in the world. In this huge market, challenges to identify the potential investors for bond instruments include:

• Lack of detailed data on an investor’s investment preferences.
• Difficulty in determining whether a bond is risky or not.
• Finding investors and sales is an expensive endeavor for institutions and businesses.

AI-powered issuance prediction

Several financial institutions and investment banking firms have tried to predict the probability of bond issuance given a set of inputs and identifying key risk parameters from a set of bonds. However, very little research has been done on incorporating Artificial Intelligence (AI) to predict bond issuance in developing countries such as India.

In order to build this model, past data needs to be collected on bond issues in collaboration with the issuer’s staff and vendor partners. This data needs to be analyzed and specific accounts can select for further analysis using natural language processing techniques and descriptive statistics to create a baseline for the AI platform.

Once this baseline is established, a predictive model can be created using various Machine Learning (ML) techniques including decision trees, regression equations, and support vector machines – all of which provided different types of predictions to increase the accuracy of the final model.

This will help the issuers to search the predictions based on the bond characteristics that they are planning to issue in the bond near future. This may range across characteristics such as:

• Sector
• Rating
• Country/ Region
• Amount
• Currency
• Maturity

After the search, the issuer will gain insight into the investors who invested in these bonds in the past based on historical data which we collected to build the model. This indicates a pattern as these investors would be likely to invest in similar bonds in the future as well.

Conclusion

Clearly, bond issuers face a host of challenges in managing the process of issuing debt are complex. This is where AI comes into play. An AI-powered bond issuance platform could serve as a tool that addresses these problems by providing bond issuers with a centralized location for conducting their workflow. This solution will be appealing to both small and large companies looking for ways to manage these complexities in the future.

References

• Overbond – Bond Buyer Matching Artificial Intelligence (AI)
• Step-by-step guide to issuing a bond (bbva.com)