Global Convergence of Accounting Standards

In the recent past, there has been a lot of discussion and activity towards to goal of moving towards a global standard for financial reporting. This is an important topic as businesses are becoming more and more global and complex.

Both International Accounting Standards Board (IASB) and Financial Accounting Standards Board (FASB) have expressed their commitment towards this convergence of standards. In 2002, they both signed the “The Norwalk Agreement”, in which both the FASB and IASB pledged to use their best efforts to (a) make their existing financial reporting standards fully compatible as soon as is practicable and (b) to coordinate their future work programs to ensure that once achieved, compatibility is maintained.

Both the boards have been working on many projects aimed at removing a variety of individual differences between U.S. GAAP and International Financial Reporting Standards (IFRS).

Many countries have already started adopting IFRS by issuing their country equivalents of IFRS. For example, Australia has Australian equivalents to IFRS' (A-IFRS). All listed EU companies have been required to use IFRS since 2005. More than 100 countries around the world are moving towards IFRS.

To have a common financial reporting standard that is accepted worldwide is a huge undertaking and may take several years. There are also several barriers to achieve full convergence.

  • The standard-setting bodies and regulators may have different views.

  • There may be influence and pressure from industry lobbying groups and others who are subject to these accounting standards or are affected by them.

  • There could be political pressure because of different views and objectives.

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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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