NetNews Usenet Archive 1992 #27/NN_1992_27.iso/spool/misc/invest/13908

Internet Message Format | 1992-11-18 | 2.2 KB

Path: sparky!uunet!hela.iti.org!usc!usc!not-for-mail
From: [e-mail] (Ajay Shah)
Newsgroups: misc.invest
Subject: Re: Yields and Betas...huh?
Date: 18 Nov 1992 11:24:09 -0800
Organization: University of Southern California, Los Angeles, CA
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[e-mail] (Ravi Shukla) writes:

>So, truely and technically speaking, if the market return is 2% above its
>mean, the stock return would be 3% above its mean, if the stock beta is 1.5.
>How useful is it to know that? I don't know.

One shot at interpretation:

On a day the (S&P-type) market index goes up by 1%, a stock with beta
of 1.5 will go up by 1.5% + epsilon.  (Thus it won't go up by exactly
1.5%, but by something different).

The good thing is that the epsilon of different stocks is guaranteed
to be uncorrelated with each other.  Hence in a diversified portfolio,
you can expect all the epsilons (of different stocks) to cancel out.
Thus if you hold a diversified portfolio, the beta of a stock
characterises that stock's response to fluctuations in the market portfolio.

So in a diversified portfolio, the beta of stock X is a good summary
of it's risk properties w.r.t. the "systematic risk", which is
fluctuations in the market index.  A stock with high beta responds
strongly to variations in the market, and a stock with low beta is
relatively insensitive to variations in the market.

E.g. if you had a portfolio of beta 1.2, and decided to add a stock
with beta 1.5, then you know that you are slightly increasing the
riskiness (and average return) of your portfolio.  This conclusion is
reached by merely comparing two numbers (1.2 and 1.5).  That parsimony
of computation is the major contribution of the notion of "beta".
Conversely if you got cold feet about the variability of your beta=1.2
portfolio, you could augment it with a few companies with beta<1.

If you had wished to figure such conclusions without the notion of
beta, you would have had to deal with large covariance matrices and
nontrivial computations.

        -ans.
--
Ajay Shah, [phone], [e-mail]