Nominal Returns and Real Returns in Investments

While calculating the returns on an investment, what we directly observe is the nominal returns. These are the returns which have not been adjusted for the inflation. The nominal returns can also be looked at as pre-tax nominal returns and post-tax nominal returns. Pre-tax nominal return, as the name suggests, is the return without adjusting for the tax one has to pay on the returns earned.

In financial investments, the investors are usually concerned about the after-tax returns that they will get as the tax liability can vary substantially. The returns that an investor gets can be classified into two types: capital gains arising from the price appreciation, and the income arising from the interest or dividend received. Both capital gains and income get different treatment from each other and also the tax rates vary in different countries. The income in the form of interest or dividends is usually treated as ordinary income and clubbed with other income for the purpose of calculating tax burden. The capital gains can be long-term capital gains or short-term capital gains. In many countries long-term capital gains get preferential tax treatment.

An investment manager may reduce the overall tax liability by choosing suitable securities for their portfolios.

The real returns refer to what the investor has actually earned after adjusting for the inflation. The relation between the real rate and nominal rate can be expressed as follows:

Rnominal = (1 + rreal) * (1 + inflation rate)

Real returns are useful while comparing returns over different time periods because of the differences in inflation rates. Also, they are useful for comparing investments in different countries because the investments will be in different local currencies and will have different inflation rates.

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Data Science in Finance: 9-Book Bundle

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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

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