costCurves

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

load KRGExampleData

The variable TradeData appears in the MATLAB^® workspace.

TradeData contains these variables:

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

Estimate market-impact costs with the trade quantity 'Size' and strategy 'POV'. Specify the trade quantity range with increments of 0.01 by starting from 0.01 and ending at one. Specify the trade strategy range with increments of 0.05 by starting from 0.05 and ending at 0.5.

cc = costCurves(k,TradeData,'Size',(0.01:0.01:1),'POV',(0.05:0.05:0.5));

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

cc(1:3,:)

ans = 

    Symbol    Size     Shares       Dollars      POV     TradeTime    Cost_BP    Cost_DollarsPerShare    Cost_Dollars
    ______    ____    _________    __________    ____    _________    _______    ____________________    ____________

    'AAL'     0.01    114764.24    6251208.50    0.05    0.19         11.42      0.06                     7139.93    
    'AAL'     0.01    114764.24    6251208.50    0.10    0.09         17.93      0.10                    11210.76    
    'AAL'     0.01    114764.24    6251208.50    0.15    0.06         23.42      0.13                    14637.37    

The market-impact cost data contains:

Display cost curves for the first stock for these percentage of volume rates: 5%, 15%, 25%, and 35%.

figure
subplot(2,2,1) 
plot(cc.Size(1:10:1000)*100,cc.Cost_BP(1:10:1000))
grid on
axis([0 100 0 250]) 
xlabel({'Size','(%ADV)'}) 
ylabel({'Cost','(bps)'}) 
title('POV = 5%') 
a = gca; 
a.XAxis.TickLabelFormat = '%g%%';

subplot(2,2,2) 
plot(cc.Size(3:10:1000)*100,cc.Cost_BP(3:10:1000)) 
grid on
axis([0 100 0 250]) 
xlabel({'Size','(%ADV)'}) 
ylabel({'Cost','(bps)'}) 
title('POV = 15%') 
b = gca; 
b.XAxis.TickLabelFormat = '%g%%';

subplot(2,2,3) 
plot(cc.Size(5:10:1000)*100,cc.Cost_BP(5:10:1000)) 
grid on
axis([0 100 0 250]) 
xlabel({'Size','(%ADV)'}) 
ylabel({'Cost','(bps)'}) 
title('POV = 25%') 
c = gca; 
c.XAxis.TickLabelFormat = '%g%%';

subplot(2,2,4) 
plot(cc.Size(7:10:1000)*100,cc.Cost_BP(7:10:1000)) 
grid on
axis([0 100 0 250]) 
xlabel({'Size','(%ADV)'}) 
ylabel({'Cost','(bps)'}) 
title('POV = 35%') 
d = gca; 
d.XAxis.TickLabelFormat = '%g%%';

This figure demonstrates how fast to trade a specific order size within a price level.