Signal Processing - May 2017 - 21

Minimum-Variance Portfolio
and Capon Beamformer

MMV Portfolios and Intertemporal CAPM
Other than expected returns, do people have other
tastes? That is, they may like aspects of a firm other than
the expected return. For example, a potential explanation
for the value factor is that many people like to hold
growth stocks (e.g., unicorn firms) for the glory and therefore lower their return expectation.
With multiple tastes/preferences, the MMV portfolio
optimization problem can be formulated as follows:
" J (x ip) = v R2p = x Tp Rx p ,,
x ie = argmin
x ip

s.t.

Consider the problem of finding the minimum-variance
portfolio, i.e., point B in Figure 3, formulated as
R i (t ) = a i E [R i] + f i (t ), i = 1, f, n.
Find weights x mv such that
x mv = argmin
x Tp R R x p,
xp
subject to the weight normalization:

n

/ x ip b is = b es, s = 1, f, S, (i.e., x Tp b p = b e),

i =1

x Tp a = 1.

n

E [R p] = / x ip E [R i] = x Tp E [R] = re,
i =1

/ x ip = 1,
i

where re is the desired level of expected return. Note
that s represents the tastes other than expected returns
and b es is the desired level of the sth taste.
The market equilibrium for the MMV criterion is a multifactor intertemporal CAPM [28]:
S

E [R i] - rf = b ie (E [R M] - rf ) + / b is (E [R s] - rf ), 6i.
s =1

That is, there are multiple MMV-efficient market portfolios (factors).

model [24], [23] (see "FF Three-Factor Model and CrossSectional Returns"), which suggests that the size and firm
value factors can generate excess returns.
Based on the FF three-factor model, prior empirical
studies suggest that additional factors persistently produce
excess returns. The most notable include the momentum
factor, as in the four-factor model by Carhart [25], and the
two additional factors, profitability and investment patterns,
that Fama and French [26] added in their more recent fivefactor model. Theoretically, multiple-factor models can be
explained by multifactor minimum-variance (MMV) portfolios [27]. See "MMV Portfolios and Intertemporal CAPM."
New research evaluating investment alpha needs to incorporate all known factors in the present multiple-factor models.
Historical empirical analysis has shown that, when the fourfactor model is considered, no skilled or informed mutual
fund portfolio managers can generate persistent investment
alpha [25].
The CAPM-related study is a mature area with established
statistical methodology. Current studies in this area are
mainly empirical research identifying new cross-sectional
factors, e.g., [26]. Beta evaluation in traditional study is
based on a windowed regression with typically three- to five-

In array SP, this is exactly the minimum-variance distortionless response beamformer, also known as the Capon
beamformer, to recover the signal sources E [R i] with
minimum noise variance [29]:
x mv =

RR a .
a T R R-1 a

Remarks
1) Many portfolio construction/analysis problems have
similar forms to array SP problems.
2) The covariance matrix R R can be a very large matrix
in portfolio analysis because n is large. It is often
difficult to estimate such a large matrix reliably. In
addition, because of high correlations among assets,
this matrix often has a very high conditional number
(close to singular), and therefore it is difficult to compute its inverse.

year windows, assuming stationarity within the window.
The opportunity for SP might lie in the time-varying nonstationary models. The difficulty of any time-varying model
is the evaluation criteria given that there are no ground-truth
beta parameters.

Portfolio analysis and array SP
Consider the return series of an asset as a noisy signal. A portfolio can be considered a way to use diversity to improve SNR.
This is quite similar to many SP formulations. See "MinimumVariance Portfolio and Capon Beamformer" for the relationships between them in array SP.
Even if we cannot generate investment alpha in portfolio
construction, there are other aspects of a portfolio with which
we are concerned. For example, different people often have
different preferences for systematic market risks represented
by various risk factors. These systematic market risks cannot
be eliminated by diversification, but investors can choose/analyze the amount of portfolio exposure to those market risks

IEEE Signal Processing Magazine

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May 2017

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21



Table of Contents for the Digital Edition of Signal Processing - May 2017

Signal Processing - May 2017 - Cover1
Signal Processing - May 2017 - Cover2
Signal Processing - May 2017 - 1
Signal Processing - May 2017 - 2
Signal Processing - May 2017 - 3
Signal Processing - May 2017 - 4
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Signal Processing - May 2017 - 111
Signal Processing - May 2017 - 112
Signal Processing - May 2017 - Cover3
Signal Processing - May 2017 - Cover4
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