Why Use Lognormal Returns in Finance (Stock Prices)?

The logarithm of a number is the exponent by which another fixed value, the base, has to be raised to produce that number. For example, the logarithm of 100 to base 10 is 2, because 100 is 10 to the power 2: 1000 = 10 × 10  = 103. More generally, if x = by, then y is the logarithm of x to base b, and is written y = logb(x), so log10(100) = 2.

The inverse of a logarithmic function is an exponential functions.

Logarithms play a significant role in quantitative finance. If we use a base of natural e, we can compute continuously compounded returns.

The first video below provides an intuitive explanation of the concept of logarithms.

The video below discusses why we use log returns in finance.

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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.