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Estimate uncertainty of market-impact cost


tr = timingRisk(k,trade)



tr = timingRisk(k,trade) returns the uncertainty of the market-impact cost estimate, or timing risk. timingRisk uses the Kissell Research Group (KRG) transaction-cost analysis object k and trade data trade.


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Retrieve the market-impact data from the Kissell Research Group 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('','username','pwd');

miData = readtable('MI_Encrypted_Parameters.csv','delimiter', ...

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 Trading Toolbox™.

load KRGExampleData

The variable TradeData appears in the MATLAB® workspace.

TradeData contains these variables:

  • Stock symbol

  • Side

  • Number of shares

  • Size

  • Stock price

  • Average daily volume

  • Volatility

  • 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 k. Display the first three timing risk values.

tr = timingRisk(k,TradeData);

ans =


Timing risk trading costs display in basis points.

Input Arguments

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Transaction cost analysis, specified as a KRG object created using krg.

Trade data that describes the stocks in the transaction, specified as a table or structure. trade must contain these variable or field names.

Variable or Field NameDescription


Stock symbol


Buy or sell side


Number of shares in the transaction


Shares in the transaction, which is a percentage of average daily trading volume


Stock price


Average daily volume




Percentage of volume

The trading cost varies with the trade strategy. timingRisk determines the trade strategy using these variables in this order:

  1. Percentage of volume

  2. Trade time

  3. Trade schedule

To change the trade strategy from percentage of volume to trade time, remove the variable POV in the table and add the variable TradeTime with trade time data. To use the trade schedule strategy, remove the variable TradeTime and add the TradeSchedule and VolumeProfile variables.

If you specify size in the trade data, timingRisk uses the Size variable. Otherwise, timingRisk uses the variables ADV and Shares to determine the size.

For example, to create trade data as a table, enter:

trade = table({'XYZ'},{'Buy'},9300,0.06,29.68,860000,0.27,0.17,...
    'VariableNames',{'Symbol' 'Side' 'Shares' 'Size' 'Price' ...
    'ADV' 'Volatility' 'POV'})

To create trade data as a structure, enter:

trade.Symbol = {'XYZ'};
trade.Side = {'Buy'};
trade.Shares = 9300;
trade.Size = 0.06;
trade.Price = 29.68;
trade.ADV = 860000;
trade.Volatility = 0.27;
trade.POV = 0.17;

These examples do not represent real market data.

Data Types: struct | table

Output Arguments

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Timing risk, returned as a vector. The vector values correspond to the timing risk in basis points for each stock in trade.

More About

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Timing Risk

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


σ 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.


[1] Kissell, Robert. “A Practical Framework for Transaction Cost Analysis.” Journal of Trading. Vol. 3, Number 2, Summer 2008, pp. 29–37.

[2] Kissell, Robert. “Algorithmic Trading Strategies.” Ph.D. Thesis. Fordham University, May 2006.

[3] Kissell, Robert. “TCA in the Investment Process: An Overview.” Journal of Index Investing. Vol. 2, Number 1, Summer 2011, pp. 60–64.

[4] Kissell, Robert. The Science of Algorithmic Trading and Portfolio Management. Cambridge, MA: Elsevier/Academic Press, 2013.

[5] Glantz, Morton, and Robert Kissell. Multi-Asset Risk Modeling. Cambridge, MA: Elsevier/Academic Press, 2013.

[6] Kissell, Robert, and Morton Glantz. Optimal Trading Strategies. New York, NY: AMACOM, Inc., 2003.

Introduced in R2016a

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