CFA Level 2: Financial Reporting Part 3 – Introduction

This segment gets to the heart of financial statement analysis applications for investment decision-making.

Investors and analysts should understand that a company’s management retains discretion in its choice of certain accounting methodologies and accrual estimates.  This subjectivity necessitates that analysts possess a detective like skill set to hunt down accounting maneuvers and revise financial statements to create a true economic picture of the firm’s performance and financial position.

Making adjustments is imperative as future stock and bond values will be impacted by expectations of performance persistence.  A company’s management may make accounting choices which show a positive current picture, but mask future financial performance challenges.  This section will review issues in evaluating a company’s reported earnings and common techniques for scrutinizing a company’s reported financial statements.

This tutorial attempts to address differences between accounting standards; however the candidate is advised to refer to Study Session 7 of the Level II CFA Program Curriculum © to validate requirements in understanding formal discrepancies between U.S. generally accepted accounting principles (GAAP) and international standards (referred to in this tutorial as IFRS-IAS).

Understanding financial reporting quality and adjusting reported financial statements are skills developed by an analyst through extended practice.  While a candidate would do well to memorize the concepts presented in this study session, he/she will greatly improve his/her chances of passing Level II by repeatedly practicing the application of these principles until they become instinct.

The candidate will likely see three to four financial reporting item sets on the actual exam.  Financial reporting quality could cover a complete item set in isolation or be spread over parts of all of the financial reporting item sets.  Correct application of analyzing financial reporting quality could directly represent as much as 5-10% of the test.

Material:

I.          Earnings and Cash Flows

II.        Derivatives Hedging and Financial Reporting

III.       Earnings Quality and Other Financial Reporting Challenges for Analysts

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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.