Lessons

CFA Level 2: Financial Reporting Part 2 – Introduction

Welcome to the belly of the financial reporting beast.  Candidates are expected to understand accounting for: intercorporate investments, retirement (or post-employment) benefits, stock (or share) based compensation, and multi-national operations.  Compared to the other sections this is one of the longest sections.  The good news is that the math is not particularly complicated, but there are a number of decision rules and procedural steps to learn.  This series is designed to provide a framework of understanding for the aforementioned financial accounting issues.  However, to be fully prepared for the exam, candidates will need to repeatedly work through examples with this framework in order to be fully prepared for item sets on the exam.  It is reasonable to expect that each of these items will be touched upon in the exam.

Definitions

Fair Value: Commonly synonymous for an asset’s market value, however some assets do not have a readily observable market price, which increases the subjectivity in determining the asset’s fair value.

Financial Statements: Refers to a company’s: income statement, balance sheet, statement of cash flows, and/or statement of changes in owner’s equity.  There will be several references to the statement of changes in owner’s equity in this section.

Material:

This series will discuss the following material:

I.          Intercorporate Investments Accounting

II.        Retirement (Post-employment) Compensation and Benefits Accounting

III.       Stock (Share) Based Compensation Accounting

IV.       Financial Statement Consolidation of Multinational Operations

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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.