After obtaining efficient portfolios or estimates for expected portfolio risks and returns,
use your results to set up trades to move toward an efficient portfolio. For information
on the workflow when using Portfolio objects, see Portfolio Object Workflow.
Suppose that you set up a portfolio optimization problem and obtained portfolios on the
efficient frontier. Use the dataset object from Statistics and Machine Learning Toolbox™ to form a blotter that lists your portfolios with the names for each
asset. For example, suppose that you want to obtain five portfolios along the
efficient frontier. You can set up a blotter with weights multiplied by 100 to view
the allocations for each
portfolio:
m = [ 0.05; 0.1; 0.12; 0.18 ];
C = [ 0.0064 0.00408 0.00192 0;
0.00408 0.0289 0.0204 0.0119;
0.00192 0.0204 0.0576 0.0336;
0 0.0119 0.0336 0.1225 ];
pwgt0 = [ 0.3; 0.3; 0.2; 0.1 ];
p = Portfolio('InitPort', pwgt0);
p = setAssetList(p, 'Bonds','Large-Cap Equities','Small-Cap Equities','Emerging Equities');
p = setAssetMoments(p, m, C);
p = setDefaultConstraints(p);
pwgt = estimateFrontier(p, 5);
pnames = cell(1,5);
for i = 1:5
pnames{i} = sprintf('Port%d',i);
end
Blotter = dataset([{100*pwgt},pnames],'obsnames',p.AssetList);
display(Blotter)Blotter =
Port1 Port2 Port3 Port4 Port5
Bonds 88.906 51.216 13.525 0 0
Large-Cap Equities 3.6875 24.387 45.086 27.479 0
Small-Cap Equities 4.0425 7.7088 11.375 13.759 0
Emerging Equities 3.364 16.689 30.014 58.762 100 Port1), and that you would invest completely in emerging equity
at the maximum-risk/maximum-return end of the efficient frontier
(Port5). You can also select a particular efficient
portfolio, for example, suppose that you want a portfolio with 15% risk and you add
purchase and sale weights outputs obtained from the “estimateFrontier”
functions to set up a trade
blotter:m = [ 0.05; 0.1; 0.12; 0.18 ];
C = [ 0.0064 0.00408 0.00192 0;
0.00408 0.0289 0.0204 0.0119;
0.00192 0.0204 0.0576 0.0336;
0 0.0119 0.0336 0.1225 ];
pwgt0 = [ 0.3; 0.3; 0.2; 0.1 ];
p = Portfolio('InitPort', pwgt0);
p = setAssetList(p, 'Bonds','Large-Cap Equities','Small-Cap Equities','Emerging Equities');
p = setAssetMoments(p, m, C);
p = setDefaultConstraints(p);
[pwgt, pbuy, psell] = estimateFrontierByRisk(p, 0.15);
Blotter = dataset([{100*[pwgt0, pwgt, pbuy, psell]}, ...
{'Initial','Weight', 'Purchases','Sales'}],'obsnames',p.AssetList);
display(Blotter)Blotter =
Initial Weight Purchases Sales
Bonds 30 20.299 0 9.7007
Large-Cap Equities 30 41.366 11.366 0
Small-Cap Equities 20 10.716 0 9.2838
Emerging Equities 10 27.619 17.619 0dataset object to obtain shares and shares to be traded. For an
example, see Asset Allocation Case Study.checkFeasibility | estimateAssetMoments | Portfolio