How to Calculate Payback Period

The payback period of a project is defined as the number of years it takes for the project to recover its original investment.

Let’s take a simple example to understand how payback period is calculated. Assume that a company invests $5,000 in a project, which generates the following cash flow in the next 5 years.

YearCash Flow
0$-5,000.00
1$2,000.00
2$2,000.00
3$2,000.00
4$1,000.00
5$1,000.00

The payback period will be equal to the time period when the firm has generated back its $5,000 investment.

In year 1, the firm generates $2000. In year 2, it generates $2,000. By the end of the year 2, the cumulative cash inflow is $4,000. In year 3, it generates $2,000. At the end of year 3, the cumulative cash flow is $6,000, which is more than our initial investment. That means the payback period is somewhere between year 2 and year 3.

By the end of year 2, we have recovered $4,000. The unrecovered amount is $1,000. In year 3, the total cash flow is $2,000.

With this information, the payback period can be calculated as follows.

Payback period = 2 years + $1,000/$2000 = 2.5 years

The payback period is a measure of the firm’s liquidity. Generally, the shorter the payback period is, the better it is for the firm. However, the method has some drawbacks. For example, it does not consider time value of money, or the cash flows after the payback period. Because of these drawbacks, the payback period method cannot be used as a measure of profitability.

This method is generally used along with another method such as NPV or IRR while making capital investment decisions.

Related Downloads

Related Quizzes

Capital Budgeting

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.