I'm using the XIRR function in a model in which the dates are dynamic and
can be changed. The initial dates are irregular which is why I'm using the
XIRR function, however subsequent dates happen at a regular (monthly)
interval.

The following set of dates return an IRR that is 151683%
Aug-08
Dec-08
Mar-09
Apr-09
May-09
Jun-09
Jul-09
Aug-09
Sep-09
Oct-09
Nov-09
Dec-09


If I change the initial dates to be further apart by a few years, I get a
more reasonable 30%

August 1, 2008
September 1, 2013
December 1, 2008
January 1, 2009
February 1, 2009
March 1, 2009
April 1, 2009
May 1, 2009
June 1, 2009
July 1, 2009
August 1, 2009
September 1, 2009
October 1, 2009
November 1, 2009

I need to fix the large IRR as the set of dates with dates that are close
together is the more realistic scenario.

How do you propose to "fix the large IRR"? XIRR is giving you the correct
answer for the data that you've given it. If you don't like the answer, the
only solution is to change the data.

Regards,
Fred.

"KevinGH" wrote in message
...

I'm using the XIRR function in a model in which the dates are dynamic and
can be changed. The initial dates are irregular which is why I'm using
the
XIRR function, however subsequent dates happen at a regular (monthly)
interval.

The following set of dates return an IRR that is 151683%
Aug-08
Dec-08
Mar-09
Apr-09
May-09
Jun-09
Jul-09
Aug-09
Sep-09
Oct-09
Nov-09
Dec-09


If I change the initial dates to be further apart by a few years, I get a
more reasonable 30%

August 1, 2008
September 1, 2013
December 1, 2008
January 1, 2009
February 1, 2009
March 1, 2009
April 1, 2009
May 1, 2009
June 1, 2009
July 1, 2009
August 1, 2009
September 1, 2009
October 1, 2009
November 1, 2009

I need to fix the large IRR as the set of dates with dates that are close
together is the more realistic scenario.