Bad Debt Expense and Warranty Expense Recognition

A bad debt is a consequence of making a sale on credit. A warranty is a promise by a manufacturer or seller to ensure the quality or performance of the product for a specific period of time. Both bad debt and warranty are uncertain future costs arising as a consequence of making sales. The firms are required to estimate the bad debt expense and warranty expense and record these estimated expenses when the products are sold (matching principle). An equivalent liability is created. 

Warranty Expense                              xx

                        Estimated Warranty Liability               xx

When the costs are incurred, (usually in subsequent periods), the expense is charged to the warranty liability:

Estimated Warranty Liability   xx

                                    Cash, etc.                                xx

Implications for Financial AnalysisAn analyst should pay close attention to the expense recognition practices followed by a company. Since many expenses are recognized based on estimates a company can delay recognizing an expense or recognize it early which will impact the net income. For example, a company’s LIFO gross margin could be materially different that its FIFO gross margin, if the cost of the first goods purchased for inventory is materially different from the cost of the most recent goods purchased for inventory. The choice of LIFO or FIFO can overstate or understate the real economic value of short-term assets on the balance sheet. Similarly, the choice of depreciation method will impact the financial statements. If straight-line method is used, earnings will be higher, as depreciation expense is lower in early years. If accelerated method is used, earnings will be lower, as depreciation expense is higher in early years.

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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
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

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