Estimate price movement due to order or trade
returns the market impact cost for stocks using the
Kissell Research Group (KRG) transaction cost analysis object
mi = marketImpact(
k and trade
Estimates Market-Impact Costs
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.
Estimates market-impact cost
mi for each stock using the Kissell
Research Group transaction cost analysis object
k. Display the first three
mi = marketImpact(k,TradeData); mi(1:3)
ans = 0.51 96.86 10.72
Market-impact costs display in basis points.
mi — Market-impact cost
Market-impact cost, returned as a vector. The vector values
correspond to the market-impact costs in basis points for each stock
Market impact (MI) estimates the price movement in a stock caused by a particular trade or order.
Market-impact cost always causes adverse price movement. Buy orders push the stock price higher and sell orders push the stock price lower. Market-impact cost occurs for two reasons: liquidity demands of the traders or investor and the information content of the order. The liquidity demand of a buy order requires the buyer to provide the market a premium to attract additional sells into the market. The liquidity demand of a sell order causes the seller to offer the stock at a discount to attract additional buys into the market. The information content of the trade typically signals to the market that the stock is under- or overvalued. Buy orders tend to signal to the market that the stock is undervalued thus causing an increase in price to correct for the mispricing. Sell orders tend to signal to the market that the stock is overvalued thus causing a decrease in price to correct for the mispricing. Market-impact cost depends on order size, volatility, company characteristics, and prevailing market conditions over the trading horizon such as liquidity and intraday trading patterns.
MI for an order that executes instantaneously is equal to the
I-Star trading cost model (I-Star). For details about I-Star, see
iStar. When MI equals I-Star, the trading
costs are high and prices move adversely. Therefore, investors trade
passively to reduce their cost. Thus, they slice the order and trade
over time such as minutes, hours, or possibly days.
the trade strategy of the investors into the cost calculation.
The MI model is
is I-Star. POV is the percentage of market volume, or participation fraction, of the order. and are the model parameters.
Percentage of volume rate shape
Percentage of temporary market impact. Temporary impact is dependent upon the trading strategy. Temporary impact occurs because of the liquidity demands of the investor.
Percentage of permanent market impact. Permanent impact is the unavoidable impact cost. The order does not control the permanent impact. Permanent impact occurs because of the information content of the trade.
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.
Introduced in R2016a