portfolioCostCurves

Retrieve the market impact data from the KRG FTP site. Connect to the FTP site using the ftp function with a user name and password. Navigate to the MI_Parameters folder and retrieve the market impact data in the MI_Encrypted_Parameters.csv file. miData contains the encrypted market impact date, code, and parameters.

f = ftp('ftp.kissellresearch.com','username','pwd');
mget(f,'MI_Encrypted_Parameters.csv');

miData = readtable('MI_Encrypted_Parameters.csv','delimiter', ...
    ',','ReadRowNames',false,'ReadVariableNames',true);

Create a Kissell Research Group transaction cost analysis object k.

k = krg(miData);

Load the example portfolio data from the file KRGExampleData.mat, which is included with the Datafeed Toolbox™.

load KRGExampleData

The variable PortfolioData appears in the MATLAB^® workspace.

PortfolioData contains these variables:

For a description of the example data, see Kissell Research Group Data Sets.

Estimate market-impact cost for an order execution on a portfolio of assets. Specify the trade quantity as DollarValue. Specify the trade quantity range tqRange with increments of $10,000,000. Start with a total portfolio value of $100,000,000 and end with $500,000,000. Set the percentage of volume trading strategy POV. Specify the trade strategy range tsRange with increments of 10% by starting with a percentage of volume of 10% and ending with 40%.

tqRange = (100000000:10000000:500000000); 
tsRange = (0.10:0.10:0.40);

pcc = portfolioCostCurves(k,PortfolioData,'DollarValue',tqRange,...
'POV',tsRange);

Display the first three rows of market-impact cost data.

pcc(1:3,:)

ans = 

    Size      Shares       TradeValue     AbsTradeValue    POV     TradeTime    Cost_bp    Cost_DollarsPerShare    Cost_Dollars
    ____    __________    ____________    _____________    ____    _________    _______    ____________________    ____________

    0.02    5612057.03    100000000.00    328737579.09     0.10    0.18         38.74      0.07                    387447.95   
    0.02    5612057.03    100000000.00    328737579.09     0.20    0.08         61.18      0.11                    611819.30   
    0.02    5612057.03    100000000.00    328737579.09     0.30    0.05         80.07      0.14                    800683.38   

The market-impact cost data contains:

Display portfolio cost curves for percentage of volume rates: 10%, 20%, 30%, and 40%.

figure
size10 = pcc.Size(1:4:end)*100;
size20 = pcc.Size(2:4:end)*100;
size30 = pcc.Size(3:4:end)*100;
size40 = pcc.Size(4:4:end)*100;
cost10 = pcc.Cost_bp(1:4:end);
cost20 = pcc.Cost_bp(2:4:end);
cost30 = pcc.Cost_bp(3:4:end);
cost40 = pcc.Cost_bp(4:4:end);
plot(size10,cost10,size20,cost20,size30,cost30,size40,cost40)
grid on
axis([2 11 25 200]) 
xlabel({'Size','(%ADV)'}) 
ylabel({'Cost','(bps)'}) 
legend('POV = 10%','POV = 20%','POV = 30%','POV = 40%',...
'Location','northwest') 
title('Portfolio Costs') 
a = gca; 
a.XAxis.TickLabelFormat = '%g%%';

This figure demonstrates using portfolio costs to construct the portfolio and manage portfolio contents. By analyzing portfolio costs, you can determine the optimal portfolio size.