Common Pitfalls in Capital Budgeting

Company managers commonly make errors when evaluating capital projects.  Some of these errors include:

  • The failure to account for economic reactions.  If a company introduces a highly profitable product to the market, then competitors will enter the market and future profitability will deteriorate.
  • Standard approaches for different capital projects.  A company may use a common model to analyze all of its capital projects despite differences across all the capital projects that it considers.
  • Focusing on accounting results.  A company’s management may be incentivized to initiate projects that show positive short term accounting results at the expense of long term projects with high net present values.
  • Utilizing IRR over NPV.  IRR may not lead to optimal decision making when evaluating mutually exclusive projects.  NPV is considered the superior approach.
  • Pet projects.  Influential company managers may initiate projects which advance their own interests but do not create company value (or even destroy it).
  • Cash flow errors.  Many estimates and assumptions go into forecasting cash flows and these are subject to error.
  • Inappropriate discount rate.  A company might use too low of a discount rate for a high risk project and overstate the project’s NPV.
  • Misunderstanding sunk costs and opportunity costs.  A company may incorrectly include sunk costs into its capital budgeting analysis, but exclude opportunity costs.

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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.