logopt_investment.m

斯坦福大学Grant和Boyd教授等开发的凸优化matlab工具箱

% Exercise 4.60: Log-optimal investment strategy
% Boyd & Vandenberghe "Convex Optimization" 
% Joëlle Skaf - 04/24/08 
% (a figure is generated) 
% 
% The investment strategy x that maximizes the long term growth rate 
%           R = sum_{j=1}^m pi_j*log(p_j^Tx) 
% is called the log-optimal investment strategy, and can be found by
% solving the optimization problem 
%           maximize    sum_{j=1}^m pi_j log(p_j^Tx) 
%           subject to  x >= 0, sum(x) = 1,
% where p_ij is the return of asset i over one period in scenario j and 
% pi_j is the probability of scenario j. There are n assets and m scenarios.
% We consider the case of equiprobable scenarios. 
%
% The log-optimal long term growth rate is found and compared to the one
% obtained with a uniform allocation strategy, i.e., x_i=(1/n). 
% Sample trajectories ofthe accumulated wealth for the optimal strategy and 
% the uniform one are plotted. 


% Input data 
P = [3.5000    1.1100    1.1100    1.0400    1.0100;
     0.5000    0.9700    0.9800    1.0500    1.0100;
     0.5000    0.9900    0.9900    0.9900    1.0100;
     0.5000    1.0500    1.0600    0.9900    1.0100;
     0.5000    1.1600    0.9900    1.0700    1.0100;
     0.5000    0.9900    0.9900    1.0600    1.0100;
     0.5000    0.9200    1.0800    0.9900    1.0100;
     0.5000    1.1300    1.1000    0.9900    1.0100;
     0.5000    0.9300    0.9500    1.0400    1.0100;
     3.5000    0.9900    0.9700    0.9800    1.0100];

[m,n] = size(P);
Pi = ones(m,1)/m;
x_unif = ones(n,1)/n; % uniform resource allocation

% Find the log-optimal investment policy 
cvx_begin 
    variable x_opt(n)
    maximize sum(Pi.*log(P*x_opt))
    sum(x_opt) == 1
    x_opt >= 0
cvx_end

% Long-term growth rates
R_opt = sum(Pi.*log(P*x_opt));
R_unif = sum(Pi.*log(P*x_unif));
display('The long term growth rate of the log-optimal strategy is: '); 
disp(R_opt); 
display('The long term growth rate of the uniform strategy is: ');
disp(R_unif); 

% Generate random event sequences
rand('state',10);
N = 10;  % number of random trajectories 
T = 200; % time horizon
w_opt = []; w_unif = [];
for i = 1:N
    events = ceil(rand(1,T)*m);
    P_event = P(events,:);
    w_opt = [w_opt [1; cumprod(P_event*x_opt)]];
    w_unif = [w_unif [1; cumprod(P_event*x_unif)]];
end

% Plot wealth versus time
figure
semilogy(w_opt,'g')
hold on
semilogy(w_unif,'r--')
grid
axis tight
xlabel('time')
ylabel('wealth')