Equity Analysis Part 2 - Introduction

Moving into part two of equity analysis, the material introduces more calculations than part one.   Candidates will see a number of formulas that are commonly applied when valuing stocks.  It is important not only to know the formulas, but also to able to interpret them and understand their respective strengths and weaknesses.  Building on the foundation of part one, part two illustrates methods for intrinsic and relative valuation of stocks.

Beyond the increase in formulas, part two of equity also continues to examine the qualitative aspects of stock analysis.  Concepts concerning industry analysis (including Porter’s Five Competitive Forces), the business cycle, country analysis, and additional considerations for emerging market equities.

This tutorial aligns with Study Session 11 material in the Level II CFA Program Curriculum ©.

NOTES:

rce = common notation for required return on common equity (or required return on equity for short).

rrf = common notation for return on a risk free asset; the standard risk free asset is government debt, such as U.S. Treasuries

b = common notation for a company’s earnings retention ratio

k = common notation for a company’s dividend payout ratio

β = beta; used in applications involving the Capital Asset Pricing Model

Subscript 0 vs. Subscript 1: a “0” in the subscript represents the current period, while a “1” in the subscript indicates the value is the next future period

Material:

I.          Porter’s Five Competitive Forces

II.        Industry Analysis

III.       Emerging Markets Valuation

V.        Discounted Dividend Valuation

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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.