MSN Investment Portfolio offer this feature. I understand that returns for
short term investments are projected over period of one year. Formula takes
into account daily price variation of share.
--
JJB

Do you have a question you want answered?

FYI, daily price variations have no impact on your investment returns. Only the
cash flow (your purchases and withdrawals) determine the return.

--
Regards,
Fred


"jjb" wrote in message
...
MSN Investment Portfolio offer this feature. I understand that returns for
short term investments are projected over period of one year. Formula takes
into account daily price variation of share.
--
JJB

jjb wrote:
MSN Investment Portfolio offer this feature. I understand that returns for
short term investments are projected over period of one year. Formula takes
into account daily price variation of share.

I don't see an answer to your question, and arguably, for good reason:
your question is unclear to me. You might be asking: how does MSNIP
project price performance over one year based on recent short-term
performance?

I don't know because I am not familiar with MSNIP per se. There are as
many ideas for doing this as there are pundits who hawk them; and
hasten to note that all of the ideas are equally inaccurate. Some
basic ideas are....

1. Monte Carlo simulation using the mean and std dev of historical
price changes. This is not a simple formula, unless you believe that
calling a VBA function that performs tens of thousands of computations
is a "simple formula". Well, okay: technically, the cell formula is
simple ;-).

2. Extrapolating the short-term growth rate. For example, if one
month-to-month prices are in P1:P22, the one-month growth rate is
P22/P1 - 1, and the annualized growth rate can be computed with one of
the following two equivalent formulas:

=(P22/P1)^12 - 1
=FV(P22/P1 - 1, 12, 0, -1) - 1

3. Linear regression. For example, if one month-to-month prices are in
P1:P22 and I1:I22 contain consecutive index numbers (e.g. 1-22), you
can compute the point one-year out on the best-fit line using
FORECAST(I1+252,P1:P22,I1:I22). This assumes an average of 21 trade
days per month and 252 trade days per year.

For example, if your month-to-month data is the S&P 500 index for
2-May-05 through 1-Jun-05, the annualized growth rate (#2 above) would
be 50.18%, predicting an index value of 1745.33 on 2-May-06; and the
linear regression (#3 above) would predict an index value of 1620.21.
The actual S&P 500 index on 2-May-06 was 1313.21.

Hopefully, the example demonstrates the fallacy of using short-term
data to predict long-term results. You can gain better insight into
the fallacy by "charting" the S&P 500 index (symbol ^GSPC in Yahoo) for
the period from 11-Mar-03 to the current date. (Or simply use
bigcharts.com to graph the symbol SP500.)

HTH.