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.
Component Depreciation – Under IFRS, each component of an asset is required to be depreciated separately. For example, in a building, the roof, walls, electrical fittings, plumbing, etc. are different components and for each component the useful life is estimated separately and depreciation expense computed separately. US GAAP also allows component depreciation but is hardly used.

Data Science in Finance: 9-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 $39 (Regular $57)