CAGR Calculator
Understanding the Compound Annual Growth Rate (CAGR)
Compound Annual Growth Rate (CAGR) is one of the most accurate ways to calculate and determine returns for anything that can rise or fall in value over time. Unlike simple returns, which ignore the effects of compounding, CAGR provides a "smoothed" annual rate of return, representing the geometric progression ratio that provides a constant rate of return over the time period.
The CAGR Formula
The mathematical representation used by our calculator is:
Where:
- Beginning Value: The initial investment or value at the start of the period.
- Ending Value: The final value at the end of the period.
- n: The number of years or periods elapsed.
Why Use CAGR Instead of Average Return?
Average annual returns can be misleading due to volatility. For example, if an investment grows 100% in year one and falls 50% in year two, your average return is 25% [(100 – 50) / 2]. However, you actually have the same amount of money you started with (a 0% actual return). CAGR accounts for this by showing you the steady rate at which the investment would have grown if it grew at the same rate every year.
Practical Example
Imagine you invested $5,000 in a stock portfolio. After 5 years, the portfolio is worth $8,500. To find the CAGR:
- Divide the ending value by the beginning value: 8,500 / 5,000 = 1.7
- Raise that result to the power of 1 divided by the number of years (1/5 = 0.2): 1.70.2 ≈ 1.1119
- Subtract 1: 1.1119 – 1 = 0.1119
- Multiply by 100 to get the percentage: 11.19%
This means your investment grew at a compound rate of 11.19% per year.
Limitations of CAGR
While CAGR is an excellent tool for comparing different investments, it has limitations:
- Ignores Volatility: It assumes growth was steady, hiding the "bumps" or risks taken during the period.
- Ignores Inflows/Outflows: The standard CAGR formula does not account for additional deposits or withdrawals made during the period.
- Time Sensitivity: CAGR is highly sensitive to the start and end dates chosen, which can lead to "cherry-picking" data to show better results.