Depreciation - Important Points

  • Choosing a Useful Life – In isolation, the shorter the useful life, the higher the depreciation expense.  A company’s management may attempt to show higher earnings in the near-term by increasing the useful life estimates for its long lived assets.
  • Estimating Salvage Value – A high salvage value lowers depreciation expense, raising income and equity value.  A company’s management may decide to assign a $0 salvage value to its long-lived assets in order to show higher profitability.
  • A company’s management may decide to change its depreciation method.  The reason could be legitimate or it may signal an attempt to “window dress” (present an artificially optimistic financial report).
  • An analyst is expected to be able to read the financial statement footnotes to understand the company’s choice of depreciation method.

  • The disclosure by management of a change in depreciation method should serve as a red flag to a skilled financial analyst and he or she must calculate the impacts of the depreciation method change on key financial ratios.

  • Depreciation Accounting and Inflation – when a company depreciates PPE at historical cost during a period of rising prices, the true depreciation expense is likely understated.  If the historical cost basis for a firm’s depreciation expense is lower than its replacement cost, then an analyst may believe that the company is over reporting net income.

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.

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  • Getting Started with R
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  • Quantitative Trading Strategies with R
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  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

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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.