# Return.annualized

##### calculate an annualized return for comparing instruments with different length history

An average annualized return is convenient for comparing returns.

##### Usage

`Return.annualized(R, scale = NA, geometric = TRUE)`

##### Arguments

- R
an xts, vector, matrix, data frame, timeSeries or zoo object of asset returns

- scale
number of periods in a year (daily scale = 252, monthly scale = 12, quarterly scale = 4)

- geometric
utilize geometric chaining (TRUE) or simple/arithmetic chaining (FALSE) to aggregate returns, default TRUE

##### Details

Annualized returns are useful for comparing two assets. To do so, you must scale your observations to an annual scale by raising the compound return to the number of periods in a year, and taking the root to the number of total observations: $$prod(1+R_{a})^{\frac{scale}{n}}-1=\sqrt[n]{prod(1+R_{a})^{scale}}-1$$

where scale is the number of periods in a year, and n is the total number of periods for which you have observations.

For simple returns (geometric=FALSE), the formula is:

$$\overline{R_{a}} \cdot scale$$

##### References

Bacon, Carl. *Practical Portfolio Performance Measurement
and Attribution*. Wiley. 2004. p. 6

##### See Also

##### Examples

```
# NOT RUN {
data(managers)
Return.annualized(managers[,1,drop=FALSE])
Return.annualized(managers[,1:8])
Return.annualized(managers[,1:8],geometric=FALSE)
# }
```

*Documentation reproduced from package PerformanceAnalytics, version 2.0.4, License:*