Venture Capital and Leveraged Buyout Valuation

Venture Capital Method of Valuation with a Single Round of Financing

The Venture Capital Method provides potential investors with a simple NPV or IRR view of a prospective VC fund investment.

The following calculations are employed by the Venture Capital Method:

Post Money Valuation (POST) = Estimated Terminal Fund Value / (1 + discount rate) # of years to exit

Pre Money Valuation (PRE) = POST – amount of VC investment

Ownership Fraction (F) = amount of VC investment / POST

Required Shares = # of fund shares * (F / (1-F))

Share Price = amount of VC investment / Required Shares

Discount Rate and Risk: VC investors commonly apply a high discount rate when valuing a fund (30 – 40% for example); this must be done to account for the high degree of risk and uncertainty surrounding venture capital investments.

Leveraged Buyout Valuation

LBOs are commonly valued from an enterprise value perspective.

Performing a NPV or IRR analysis on an LBO investment is somewhat consistent with standard valuation approaches discussed in the equity sections.

The analyst must forecast earnings and cash flows, create a terminal value (the terminal value is commonly based on a multiple), then discount the terminal value.

Special LBO valuation considerations:

  • Cash sweep: Depending on the nature of the debt arrangements, the company may be required to retire debt with excess cash flows.  Debt may be required to be paid off before any capital can be returned to equity investors.  Cash sweep requirements must be accounted for in analyst forecasts.
  • Exit Strategy: Much of the investor value will be realized at a single exit point, when the company is sold.  This can be in the form of an initial public offering, a private sale, or another leveraged buyout.

Two methods for valuing an LBO from an investor standpoint are the: Target IRR method and the Equity Cost Flow method.

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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.