Estimate instantaneous trading cost for order
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
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 = krg(miData);
Load the example data from the file
which is included with the Datafeed Toolbox™.
TradeData appears in the MATLAB® workspace.
TradeData contains these variables:
Number of shares
Average daily volume
Percentage of volume
For a description of the example data, see Kissell Research Group Data Sets.
Estimate instantaneous trading cost
itc for each stock using the
Kissell Research Group transaction cost analysis object
k. Display the
first three instantaneous trading costs.
itc = iStar(k,TradeData); itc(1:3)
ans = 33.48 317.58 62.94
Instantaneous trading costs display in basis points.
The I-Star trading cost model (I-Star) estimates the instantaneous cost of an order. If a market participant immediately releases the entire order to the market for execution, they incur this cost. This cost also refers to the market participant cost accounting for 100% of the market volume over the execution period.
The I-Star model is
Shares are the number of shares to trade. ADV is the average daily volume of the stock. is the price volatility. , , and are the model parameters.
Price sensitivity to order flow
Order size shape
The general I-Star model that includes stock-specific factors is
Price is the stock price. is the price shape model parameter. is the stock-specific factor such as market capitalization, beta, P/E ratio, and Debt/Equity ratio. This formulation can include multiple stock-specific factors. is the corresponding shape parameter for the stock-specific factor .
For details about the formula and calculations, contact the Kissell Research Group.
 Kissell, Robert. “A Practical Framework for Transaction Cost Analysis.” Journal of Trading. Vol. 3, Number 2, Summer 2008, pp. 29–37.
 Kissell, Robert. “Algorithmic Trading Strategies.” Ph.D. Thesis. Fordham University, May 2006.
 Kissell, Robert. “Creating Dynamic Pre-Trade Models: Beyond the Black Box.” Journal of Trading. Vol. 6, Number 4, Fall 2011, pp. 8–15.
 Kissell, Robert. “TCA in the Investment Process: An Overview.” Journal of Index Investing. Vol. 2, Number 1, Summer 2011, pp. 60–64.
 Kissell, Robert. The Science of Algorithmic Trading and Portfolio Management. Cambridge, MA: Elsevier/Academic Press, 2013.
 Kissell, Robert, and Morton Glantz. Optimal Trading Strategies. New York, NY: AMACOM, Inc., 2003.