Corporations play a significant role in our economic and financial eco-systems. Corporate debt is often a key driver of important business interactions and transactional activity. While it represents a significant fraction of US debt, there is limited public knowledge about corporate debt instruments, and the role played by financial institutions along the corporate debt supply chain. While financial researchers often have a clear theoretical understanding of how debt structure, cash flows, covenants, etc. can impact liquidity measures or can lead to contagion that can potentially increase systemic risk, there are currently few public resources available to enable rigorous data-driven systemic studies of corporate debt.
This research will address the “data science for finance” challenges of creating a public CorpDebt repository that will provide precise yet detailed insights into the corporate debt eco-system, capturing relationships among financial institutions, and specific details about a corporation and its debt structure.
This is an important first step in creating a representation of the complete debt market, which includes mortgage backed, other asset backed, municipal, federal and sovereign debt products. The intellectual merit of this research is to harness computational methods and financial big data to create rich and relevant resources and tools that can be exploited by financial researchers.
We will utilize cloud based text analytics and machine learning, ontological knowledge, crowd-sourced wisdom, and financial big data from prospecti, to construct an open, rich and complex repository, CorpDebt, centered around US corporate debt.
The CorpDebt repository will build upon the following ontological concepts defined in the Financial Industry Business Ontology (EDM Council 2016):
Relationships: e.g., a focal corporation, a parent or subsidiary, affiliate, etc. Amount and timing of new issuances, debt structure, seniority, cash flow, etc. Guarantees, covenants, corporate events, etc.
Impact on financial stability and monitoring: The CorpDebt repository will enable academic researchers and regulators to undertake systemic data-driven research, at a deep(er) level of granularity about debt products, and incorporating semantic knowledge about corporate relationships; such research would not be an option using traditional resources such as COMPUSTAT. Research hypotheses could be validated by combining data from the CorpDebt repository with additional time-series econometric and financial datasets. For example, researchers studying corporate debt liquidity or contagion can use the CorpDebt repository to create panel datasets across various industry sectors, market capitalization levels, etc. They can also include network-level metrics reflecting connected communities of financial institutions, concentration, outliers, etc. CorpDebt may also be able to discover, and provide a priori alerts about evolving and emerging (latent) relationships among systemically important financial institutions. The CorpDebt repository will thus provide regulators with high quality knowledge; it will improve transparency and it will provide an additional valuable resource to monitor systemic risk.