High Yield Corporate Debt (aka Junk bonds)

“Junk” bonds are a special segment of the corporate debt universe. High yield bonds, also called non-investment grade, speculative grade, or junk bonds, are the bonds rated below investment grade. As the rating suggests, these bonds carry a higher risk of default, and pay a higher yield to compensate for the higher risk.

When evaluating high yield debt, the analyst must pay close attention to:

  • Debt structure
  • Corporate Structure
  • Covenants

Debt Structure

High yield borrowers may utilize the following debt types:

  • Bank loans are commonly short term and have priority over other corporate debt if liquidation occurs.
  • Broker loans are commonly intermediate in term.
  • Reset notes will have coupons that are periodically adjusted to maintain the bond’s price at par.
  • Senior debt is not senior to bank loans in liquidation; if the senior debt is zero-coupon, then analysts must ensure that the company can make the cash payment at maturity.
  • Senior subordinated debt is only prioritized over subordinated debt.
  • Subordinated debt can include payment in kind (PIK) bonds which allow the borrower to pay interest in the form of more bonds.

Corporate Structure

High yield borrowers may be structured as holding companies.  Therefore, the analyst may need to evaluate every subsidiary to comprehensively assess risk.

Covenants

Analysts should review the covenants of high yield issuers, but should also keep an eye out for loopholes.

Equity Approach to Junk Bonds

The returns from high yield debt more closely correlate with stocks than with investment grade bonds.  This implies that analysts may want to employ the same type of equity valuation cash flow forecasting when valuing high yield debt.

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.