In Part 1 we covered the basics of credit spreads. We determined that the credit spread is an indication of how much additional yield an investor demands above a "riskless" investment in order to buy the risky security.
Also, we determined that an investor will compare both the expected probability of default and the expected recovery rate of a debt security in order to determine the amount of additional yield they require to "own credit risk."
So, if credit spreads are fundamentally driven by default probabilities and recovery rates, we'll need to understand what drives those metrics.
Understanding Defaults
In the context of a corporate borrower, default refers to the failure of the company to meet its contractual debt obligations as outlined in a loan agreement or bond indenture. This typically happens when the borrower cannot make a required payment (most commonly interest or principal) on time.
Default isn’t limited to just non-payment, though. It can also occur if the borrower violates other terms of the debt agreement, known as technical defaults. This might include breaching financial covenants, like maintaining a certain debt-to-equity ratio, even if payments are still being made.
When a default happens, it often triggers consequences like accelerated repayment demands or legal action from creditors, and it can push the company toward bankruptcy if unresolved.
Leverage & Liquidity
The two primary drivers of default are a company's leverage (liabilities/earnings) and liquidity (near term sources of cash).
Spend an hour on a high-yield trading desk and you'll undoubtedly hear the question "what is the company's leverage" being yelled by traders and sales people. The leverage metric that they are mostly likely referring to is debt/ebitda.
Leverage = (total debt) / (net income + taxes + interest + depreciation + amortization)
Liquidity is often gauged by metrics like the current ratio (current assets divided by current liabilities) or the quick ratio (which excludes less liquid assets like inventory).
For example, a company with ample cash reserves, marketable securities, or an untapped credit line has high liquidity—it can pay bondholders or suppliers on time. Conversely, a firm strapped for cash, with assets tied up in illiquid forms like real estate or equipment, might struggle to meet a sudden debt payment, signaling low liquidity.
All things being equal, a borrower with high leverage and low liquidity has a higher probability of defaulting than a company with lower leverage and higher liquidity.
Understanding Recovery Rates
Recovery rates are impacted by a number of scenarios that may occur in the event of default. These factors are influenced by the qualities of the security, the assets of the business, the industry in which the business operates and macro financial market conditions at the time of default.
At the security level, certain covenants, security pledges and the concept of absolute priority will impact how much enterprise value can be claimed by the various providers of capital to a business.
At the company level, the marketability of the assets which make up the enterprise will determine how much cash can be generated through a liquidation process. Assets like land and real estate are likely to sell more quickly and closer to book value than assets like office equipment, software or other intangibles.
At the industry level, the cyclicality of the industry and number of companies operating in the industry will impact the number of potential buyers of the defaulting companies assets.
Finally, financial market conditions at the time of default will impact the valuation multiples that certain assets will sell for. Also, the availability and cost of financing will impact the ability of potential buyers to transact.
Why It Matters
When underwriting credit risk, there are many variables to incorporate into a comprehensive model. Unfortunately, no quantitative model can accurately forecast the credit risk of a business. Individuals like NYU Stern finance professor Edward Altman have pioneered advances in quantitative modelling (see this article on the Altman Z-Score) however, most investors will agree that credit analysis is both a science and an art.