Factors Affecting the Sensitivity of a Company to Business Cycle

We learnt earlier that one approach to classifying a company is cyclical and non-cyclical industries. Cyclical industries are affected by business cycles with high demand during the peaks and contraction in demand during recession. Examples include the auto industry, housing and technology.

Non-cyclical industries tend to have a steady demand irrespective of business cycles. Examples include utilities, health care and personal care products.

Consumer usage patterns are one factor that determines the cyclical or non-cyclical nature of an industry. Food, beverage and utilities are needed on a day-to-day basis. A growing economy may seek more cars and housing. If the economy's growth is low the demand for such products diminishes. Demand environments therefore determine if an industry is cyclical or non-cyclical in nature. Seasonal conditions also determine if an industry is cyclical or not.

While describing an industry as cyclical or non-cyclical the terms defensive and growth are used. Defensive industries are those that are unaffected by economic downturns, either in profits or revenue. Growth industries are those with such high demand that business cycles do not seem to affect them, except perhaps a slower rate of growth during recession. Cyclical companies give the impression of companies with large fluctuations in profit and revenue. These terms are very limiting often. Cyclical companies may in fact have above average rates of growth during the ascent and peaks of business cycles. In the event of a deep economic recession, all industries tend to see a dip in their profits and revenues, due to overall reduced demand. Similarly companies that are defensive in nature may also be growth industries. An example is the growth of organised supermarkets in a developing country. Here the industry is both defensive and growth. However as the markets mature they slide into price wars that cut deeply into margins, therefore not making it a growth industry anymore.

Related Downloads

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 includes PDFs, explanations, instructions, data files, and R code for all examples.

Get the Bundle for $39 (Regular $57)
JOIN 30,000 DATA PROFESSIONALS

Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.

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.