Expense Recognition

Expenses are decreases in economic resources, either by way of outflows or reductions of assets or incurrences of liabilities, resulting from an entity’s ordinary revenue generating or service delivery activities. Under accrual accounting, the expenses are recognized based on the matching principle.

According to the matching principle, the expenses incurred in the business must be matched to the revenue generated. For example, the salary paid to the employees should be reported in the month in which the employees worked, not in the month in which they were paid. Similarly, the company should report any sales commissions paid in the same period in which the sale was made, not in the period in which the commissions were paid. In such a case, the sales commission will be reported as sales commission due as a liability till the commission is actually paid in the following period. Similarly, if a company purchased some inventory in December and sold it in January next year, then both the sales and the expenses are recognized in January, not December.

There are certain cases where the matching principle does not apply. For example, in case of advertising, it is difficult to measure the future economic benefits. Therefore, advertising expense is recognized in the period in which it is incurred. Similarly administrative costs are recognized as expenses in which they are incurred. Such costs are called period costs.

We will now apply the principles of expense recognition in different situations such as inventory expense recognition, depreciation expense recognition, and amortization expense recognition.

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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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