MVAportfol_IBM_Ford (MatLab R2007b)

MVAportfol_IBM_Ford computes the optimal portfolio weights with monthly returns of IBM and FORD from Jan 2000 to Dec 2009. The optimal portfolio is compared with an equally weighted one. Corresponds to example 18.2 in MVA.

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Zografia Anastasiadou, Awdesch Melzer

Applied Multivariate Statistical Analysis

Wed, April 11 2012 by Dedy Dwi Prastyo

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ford.csv, ibm.csv

- None

- Computes the optimal portfolio weights with monthly returns of IBM and FORD from Jan 2000 to Dec 2009. The optimal portfolio is compared with an equally weighted one.


Description: -


clear all;
close all;
clc;
IBM = importdata('ibm.csv');
    % Create new variables in the base workspace from those fields.
    vars = fieldnames(IBM);
    for i = 1:length(vars)
    assignin('base', vars{i}, IBM.(vars{i}));
    end
    ibm = IBM.data;
FORD = importdata('ford.csv');
    % Create new variables in the base workspace from those fields.
    vars = fieldnames(FORD);
    for i = 1:length(vars)
    assignin('base', vars{i}, FORD.(vars{i}));
    end
    ford = FORD.data;
y1 = ibm(:,4);	
a = 0;
i = 1;
while i<=120
	i = i+1;
	a(i) = (y1(i)-y1(i-1))./y1(i);
end
x1 = a(2:121)';
y4 = ford(:,4);
f=0;
i=1;
while i<=120
	i=i+1;
	f(i) = (y4(i)-y4(i-1))./y4(i);
end
x4 = f(2:121)';
ix = [1:2]';
x = [x1,x4];
s1 = inv(cov(x));
one = ones(length(ix),1);
c2 = (s1*one)./repmat(one'*s1*one,length(s1*one),1); % c1,c2 weights
c1 = one./repmat(sum(one),length(one),1); 
q1 = x*c1;                                             % Optimal MVAportfol returns 
q2 = x*c2;                                             % Nonoptimal MVAportfol returns 
t = [1:120]';
d1 = [t,q1];
d2 = [t,q2];
figure(1)
subplot(2,2,1)
subplot('Position',[0.15 0.61 0.4 0.3])
hold on
for i=1:length(t)-1
    line([d1(i,1) d1(i+1,1)],[d1(i,2) d1(i+1,2)]);
end
title('Equally Weighted Portfolio')
xlabel('X')
ylabel('Y')
ylim([-0.4 0.4])
xlim([0,121])
box on
subplot(2,2,3)
subplot('Position',[0.15 0.1 0.4 0.3])
hold on
for i=1:length(t)-1
    line([d2(i,1) d2(i+1,1)],[d2(i,2) d2(i+1,2)])
end
title('Optimal Weighted Portfolio')
xlabel('X')
ylabel('Y')
xlim([0 121])
ylim([-0.4 0.4])
box on
subplot(2,2,2)
axis off
hold on
text(0.5,1.06,'Weights')
w1=num2str(c1(1),'%10.3f');
w4=num2str(c1(2),'%10.3f');
text(0.5,0.85,w1)
text(0.5,0.75,w4)
text(0.9,0.85,'IBM')
text(0.9,0.75,'Ford')
subplot(2,2,4)
axis off
hold on
text(0.5,0.96,'Weights')
w1=num2str(c2(1),'%10.3f');
w4=num2str(c2(2),'%10.3f');
text(0.5,0.75,w1)
text(0.5,0.65,w4)
text(0.9,0.75,'IBM')
text(0.9,0.65,'Ford')
hold off