(Not exactly an Excel question. But I know a lot of Excel users do
this, and I know there are a number of statistics-savvy participants
in these newsgroups.)

If I have historical daily price data (y1, y2,..., yN) for a stock or
fund, the daily return is computed by y2/y1, y3/y2,..., yN/y[N-1].
Typically, the annualized average rate of return is computed by the
geometric average, namely (yN/y1)^(252/(N-1))-1. The annualized
standard deviation of the rate of return is computed by the antilog of
the standard deviation of the log returns, e.g. exp(stdev(ln({y2/
y1,...,yN/y[N-1]})*sqrt(252))-1. (Note: 252 is used instead of 365
because there are typically 252 trading days per year.)

That method -- especially the annulaized geometric average -- provides
a "warm fuzzy" feeling because when the initial investment is
multiplied by the compounded annualized rate of return, we get the
current value of the investment. The annualized geometric mean is
comparable to the APY for other asset classes, notably cash.

I realize that we must compute the annualized statistics in that
manner when we have insufficient data, e.g. 3-12 months of daily
prices.

But I wonder: if I have sufficient data (i.e. many years of daily
prices), would it be reasonable and arguably better to simply compute
the arithmetic average and arithmetic standard deviation of the year-
over-year daily returns?