Postprocessing Results to Set Up Tradable Portfolios

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.

Setting Up Tradable Portfolios

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  

This result indicates that you would invest primarily in bonds at the minimum-risk/minimum-return end of the efficient frontier (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            0

If you have prices for each asset (in this example, they can be ETFs), add them to your blotter and then use the tools of the dataset object to obtain shares and shares to be traded. For an example, see Asset Allocation Case Study.