Cash Basis Accounting vs. Accrual Basis Accounting

  • In cash basis accounting, the revenue is recognized when the cash is collected, and expenses are recognized when the cash is paid. However, the problem with this is that the cash flow may not occur at the same time when the revenue is earned or the expenses are incurred. For example, a business may make a sale in 2012, but expects to be paid for it only in 2013.
  • Rooted in the concept of the matching principle, where revenues and expenses are recognized in the period in which the transaction occurs and not when cash actually exchanges hands, accrual basis accounting provides more timely insights for analysts and investors.
  • Both U.S. GAAP and IFRS mandate accrual basis accounting.
  • However, accrual basis accounting relies on estimates and assumptions from management, providing management with the opportunity to manipulate the appearance of its financial performance.
  • While cash basis accounting is less timely than accrual accounting, it is much more objective in its reporting of transactions.
  • Accrual based earnings can be separated into two distinct components:
  1. Cash flow component
  2. Accrual component

The accrual component of a company's earnings is commonly less persistent over time. When making comparisons of companies, generally those with a higher proportion of accrual based earnings are considered to have weaker earnings than those with a higher proportion of cash flow based earnings.

NOTE: CFAI expects candidates to possess the ability to scrutinize management estimations used in financial reporting and make analytical adjustments in order to better forecast future financial performance. This ability is a core competency of the Level 2 exam.

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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.