Relative value trading is a popular investment strategy for firms looking to achieve high returns while minimising risk. This whitepaper builds on a previous whitepaper published by Quantifi – Growth of Relative Value Credit Strategies – which explored how an increase in bond issuance (as was the case in 2021), combined with extreme volatility in credit markets, creates attractive opportunities for relative value credit strategies.
Relative value credit strategy depends on the isolation of identical or very similar credit instruments, where one is assessed to be comparatively under- or overvalued. These might be bonds issued by the same borrower but at different points of the yield curve or be bonds issued by different but similar borrowers. For instance, straightforward credit analysis of cash flow and balance sheets of two seemingly similar pharmaceutical firms might reveal that one deserves to be trading at tighter yields than the other, which in turn invites a long/short trade. In essence, the strategy relies on extracting value from dislocation in the pricing of credit risk across markets, instruments and maturities.
Although these strategies can differ significantly from each other, they have one thing in common; that is to take advantage of the opportunities requires sophisticated bond analytics. This whitepaper explores the challenges of bond analytics and how access to the right analytics can provide opportunities for more comprehensive trading strategies.
Contents
- What are the Biggest Challenges for Credit Analytics?
- Bond Analytics
- Bond Analytics for Callable bonds
- Calibrating Credit Curve to Bonds
- Advanced Analytics Required for Convertibles
- LIBOR Replacement
