The Process of Valuing Equity

Valuation: Relative vs. Intrinsic

Relative valuation is an approach where an asset’s price attractiveness is evaluated in comparison to the valuation of like securities.

For example, an analyst may calculate the justified P/E ratios for stocks in a specific industry to determine which companies are relatively “cheap” and which are relatively “expensive” based on the industry average.

Intrinsic valuation is an approach whereby an estimation of future returns for an asset is calculated in order to derive the asset’s fair price; the derived price can then be compared to the current market price to see if the asset is over-priced or under-priced by the market.

For example, an analyst may estimate a stock’s future dividend payments and its share price appreciation to determine a fair current price.

Going Concern Valuation vs. Liquidation Valuation

In performing a going concern valuation, the analyst will assume that the corporation in question will exist and generate profits in perpetuity.

A liquidation valuation essentially looks at the value of a company in the event that its assets are quickly sold.

Other Considerations when Valuing Equity

The valuation approach an analyst takes should be congruent with the nature of the ownership perspective that would be assumed.

Control

When valuing a company from a takeover/acquisition perspective, the valuation should adjust for a control premium as controlling interest gives the owner the ability to dictate how cash flow will be used (compared to say an individual shareholder who has limited to almost no ability to influence the amount or timing of dividend payments, for example.

While the Gordon Growth Model may be appropriate for an individual investor, this valuation method would not readily reflect the ownership nature of a controlling position investment.

Marketability and Liquidity

Companies that do not trade publicly require higher returns from investors.

Stock values for a non-publicly traded company should be discounted for their lack of marketability (i.e., a marketability discount).

Additionally, publicly traded stocks that trade thinly should have a liquidity discount applied to their valuations.

Applications for Equity Valuation

  • Stock selection
  • Market expectations analysis
  • Fairness opinions
  • Shareholder communication
  • Corporate event interpretation
  • Private equity
  • Business strategy assessment

Alpha

Alpha is the attainment of excess risk adjusted returns above an appropriate benchmark.

Ex-Ante Alpha: based on expectations of the future.

Expected(Holding Period Return) = ((Price 1 - Price 0) + Expected Dividend)/P 0

Ex-Ante Alpha = E(HPR) – Expected Theoretical Return

The expected theoretical return can be based on an acceptable theoretical valuation technique, such as the Capital Asset Pricing Model.

Ex-Post Alpha: analyzes actual returns.

α = HPR – Return generated by similar securities.

Equity Valuation Steps

  1. Study the company’s business and industry.
  2. Forecast the company’s future financial performance.
  3. Determine the valuation method (intrinsic/absolute or relative) and incorporate any special considerations.
  4. Calculate the company’s value.
  5. Make a clear investment recommendation on the company.

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 includes PDFs, explanations, instructions, data files, and R code for all examples.

Get the Bundle for $29 (Regular $57)
JOIN 30,000 DATA PROFESSIONALS

Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.

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.