Stand Alone Risk and Capital Projects

When only a single project is evaluated, then only its stand-alone risk is measured.

The single project in question likely has a range of possible cash flow outcomes, so the company really needs to evaluate risk around these multiple outcomes.

Three methods for measuring Stand Alone Risk:

1. Sensitivity Analysis

This approach focuses on a project’s NPV sensitivity to a single variable.  The approach starts with a base case and calculates the change in NPV in the event of a change to an input variable in the NPV calculation.

For example, a firm may want to determine a project’s NPV sensitivity to a change in forecasted sales.  The change can be favorable or unfavorable.  The analyst may create three NPVs for the project: one with a base sales case, one with a favorable sales case, and one with an unfavorable sales case.  The different NPVs will show the project’s sensitivity to a change in sales.  The sensitivity can be calculated for any input variable: operating expenses, discount rate/cost of capital, tax rates, depreciation method, or expected salvage value.

For candidates with calculus familiarity, the sensitivity analysis can be conceptually viewed as backing into the derivative of a single variable in a multi-variable NPV equation.

2. Scenario Analysis

This is a multi-variable adjustment version of sensitivity analysis.  The base, favorable and unfavorable scenarios will reflect NPVs where multiple inputs have been adjusted, rather than just one.

3. Monte Carlo Simulation

This capital project risk analysis will use a computer program to simulate likely events and generate an internal rate of return (IRR) or NPV for the project in question.

The computer will pick a random variable and calculate an IRR and NPV; the process will be repeated many times, generating new IRRs and NPVs.

Each of the IRRs and NPVs will be analyzed and a statistical report will be generated for the number of iterations ran, showing data on the mean, standard deviation, median, etc.

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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.