CFA Level 2 Summary Notes

If you have started preparing for the CFA Level II exam, you’ve come to the right place. We, at Finance Train, have prepared summary notes and study plan for CFA Level 2 exam to help to review the syllabus in a systematic way.

In this article, we will talk a little about the CFA Level 2 exam, and then proceed to how you can access the summary notes.

Question Types: The CFA Level 2 exam has all the questions grouped into small case-lets called item sets. Each item set question starts with a case study followed by 6 multiple choice questions. There are a total of 20 item set question, 10 in the morning session and 10 in the afternoon session, making a total of 120 questions. The number of questions is half that of CFA Level 1, but more complex.

Exam Curriculum: The CFA Level II exam shifts its focus to asset valuation, and you are tested on your application and analysis skills of the concepts learned. The entire exam is divided into 10 main topics, as listed in the following table:

Topic AreaLevel 2
Ethical and Professional Standards (total)10
Investment Tools (total)30-60
Corporate Finance5-15
Economics5-10
Financial Reporting and Analysis15-25
Quantitative Methods5-10
Asset Classes (total)35-75
Alternative Investments5-15
Derivatives5-15
Equity Investments20-30
Fixed Income5-15
Portfolio Management and Wealth Planning (total)5-15
Total100

For each topic, CFA institute provides study session documents, which contain the detailed outline, reading list, and the learning outcome statements. For example, the first topic “Ethical and Professional Standards”, there is one study session, which consists of 4 readings. We have prepared free study material for anyone who is looking to study for CFA Level II exam. The study notes are organized as per the topic. Below is the link to the summary notes:

Go to CFA Level 2 Study Plan

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Practice Questions for CFA Level 2 Exam
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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
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  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

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