Major Types of Return Measures

For the purpose of portfolio construction, the financial assets are primarily looked at from the perspective of risk and returns. Based on the analysis of risk and returns we analyse thousands of securities and portfolio combinations before making the right selection for our own portfolio. Therefore, it is important that we have a deeper understanding of the risk and return and how these are calculated.

When we talk about the returns from a financial asset, we can broadly classify them into two types. First, financial assets may provide some income in the form of dividends or interest payments. Second, some financial assets may also offer capital appreciation as the prices of these securities change themself. For example, stocks provide both these types of returns, that is dividend income and capital appreciation. There are some stocks which don’t pay dividends, ad only provide price appreciation, such as Google, and Apple. Other assets such as bonds and pension plans only provide income such as monthly income payments in case of pension funds.

While making investment decisions, we should be aware of what kind of returns we are actually talking about. Some of the information you directly observe from the stock market may be misleading. For example, the stock indices will do a good job of reporting the price appreciation but may not truly reflect the adjustment for dividend payment. The return from the dividend payment and capital gain will put together will be the total return but may not be directly observable. Similarly the period for which you are observing the returns also becomes important; whether you are using annualized returns or the returns for your selected holding needs to be considered.

Apart from the use of correct data, how we process the return information is also important. Are we going to calculate arithmetic mean returns or geometric mean returns needs to be understood. In the next few articles we will look at the different type of return measures and how they can be applied in different situations.

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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.