Financial Statement Analysis Framework

The main job of financial analysts is to analyse various types of financial information with the objective of providing recommendation to their clients. The clients use these recommendations to make economic decision and manage their investments. Due to the nature of their job it is important that the financial analysts follow a financial statement analysis framework to bring consistency and depth in their analysis.

The CFA Institute curriculum adopts the financial statement analysis framework from the book – Analysis and Managing Banking Risk: A Framework for Assessing Corporate Governance and Financial Risk, Second edition by Hennie Van Greuning and Sonja Brajovic Bratanovic.

There are six steps in the financial statement analysis framework. The outcome of one step serves as the input for the next step. 

  1. State the objective and context: This step involves establishing objectives of financial analysis by defining the purpose and context of financial statements analysis
  2. Gather data: In this step the analyst will collect data necessary for financial analysis from different sources such as company’s annual reports, industry and economy data, and ask questions to customers, management, creditors, etc.
  3. Process the data: The analyst will now process the data. Processing may include a range of activities such as sorting data, adjusting data to prepare common-size financial statements, and preparing graphs of important ratios and trends.
  4. Analyse and interpret data: The analyst will now conduct analysis on processed data and interpret the results. He will decide on the conclusions and recommendations as supported by the information.
  5. Report conclusions and recommendations: Based on his analysis, the analyst will develop recommendations and communicate them to relevant audience. This is an important phase and the analyst must ensure that he is complying with the ethical standards related to investment recommendations.
  6. Updated the analysis: The analyst is also required to follow up and review his analysis and recommendations on a periodic basis by repeating all the above steps and make necessary changes to his analysis and recommendations.

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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.