Derecognition of Debt

The issuer of a bond can either hold the bond till maturity or redeem it before maturity. Let’s look at the accounting treatment in both situations.

Bond Matures

If the bond matures, then the carrying value of the bond would be the same as its face value. Any premium or discount would have been fully amortized. The cash outflow to repay the bond would be shown as a financing activity on the cash flow statement.

Bond Redeemed Before Maturity

The issuer may decide to redeem the bond before maturity if the interest rates have fallen or if the firm has surplus funds.

In this case: 1) The premium or discount wouldn’t have fully amortized and the carrying value will be different from the face value. 2) The issuer may have to reacquire the bonds at a price different from face value.

For example, let’s say the carrying value of the bonds is $997,500. The issuer reacquires the bonds at 101%, i.e., at $1,010,000. The accounting treatment will be as follows:

  1. Recognize a loss of $12,500 (1,010,000 – 997,500) in the income statement in a separate line item (Gain/loss on debt extinguishment)
  2. Cash paid to redeem the bonds is reported as financing activity
  3. If cash flow statement is prepared using indirect method, any gain (loss) is subtracted (added) from the net income to calculate cash flow from operating activities.

Treatment of Debt Issuance Costs

Remember that under US GAAP, issuance costs are shown as an asset (a deferred charge) which is amortized using straight-line method over the life of the bond. Any unamortized bond issuance cost must be written off and included in gain/loss calculation. This will reduce gain/increase loss.

Under IFRS, issuance costs are included in the liability, bonds payable. So, no write off is needed.

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Data Science in Finance: 9-Book Bundle

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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
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  • Credit Risk Modelling With R
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  • Machine Learning in Finance using Python

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