According to the OECD, in December 2019, the global outstanding amount of non-financial corporate bonds reached USD 13.5 trillion.
In real terms, this is more than twice the amount outstanding than in December 2008. At the same time, the appetite of banks to warehouse risk has fallen from a peak of around USD 250 billion to less than 50 (source: Algebris). Put simply, this means that while issuance has doubled, risk appetite has shrunk by 80%. The net effect is that it is getting harder and harder to find liquidity for corporate bonds. Not only this, but the majority of growth in issuance has been in so-called “high yield” bonds (i.e. the risky stuff).
This is why many participants are looking to technology to sweep the 150+ electronic trading venues that now exist and why we are seeing rising popularity in a hybrid trading model whereby banks take on bonds from their favoured clients only to offload the risk somewhere else moments later.
For technology to make a real impact, you need two things – machine learning that can sort clients and bonds by characteristic to evolve a liquidity model for each instrument and good quality data. This last point is absolutely crucial as, while the datasets are very significant, they are nothing like as large as we see in the worlds of B2C or P2V (politicians to voters).
Resolving these issues are just two of the areas that the “Vorsprung durch Technik” (Progress through Technology) chaps back at valantic HQ are looking at. Should be an exciting 2021.
valantic identifies the seven most important technology trends for 2021
valantic identifies the seven most important technology trends for 2021