Estimate uncertainty of market impact cost
tr = timingRisk(k,trade)
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
k = krg(miData);
Load the example data from the file
which is included with the Trading
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 timing risk
tr for each
stock using the Kissell Research Group transaction
cost analysis object
the first three timing risk values.
tr = timingRisk(k,TradeData); tr(1:3)
ans = 159.05 242.37 62.88
Timing risk trading costs display in basis points.
Timing risk (TR) estimates the uncertainty surrounding the estimated transaction cost.
Price volatility and liquidity risk creates uncertainty. Price volatility causes the price to be either higher or lower than expected due to factors independent of the order. Liquidity risk causes the market impact cost to be either higher or lower than estimated due to market volumes. TR is dependent upon volumes, intraday trading patterns, and market impact resulting from other market participants. The TR model is
is price volatility. 250 is the number of trading days in the year. Shares are the number of shares to trade. ADV is the average daily volume of the stock. POV is the percentage of market volume, or participation fraction, of the order.
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. “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.
 Glantz, Morton, and Robert Kissell. Multi-Asset Risk Modeling. Cambridge, MA: Elsevier/Academic Press, 2013.
 Kissell, Robert, and Morton Glantz. Optimal Trading Strategies. New York, NY: AMACOM, Inc., 2003.