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Robert Engle and Robert FerstenbergMicrostructure in Paris
December 8, 2014
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Is varying over time and over assets Is a powerful input to many financial
decisions such as portfolio construction andtrading algorithms
Is never directly observed but is believed tobe correlated with volatility and volume andbid ask spread
Can be interpreted as the ease with which aninvestor can purchase or sell large quantities.
Forecasting Transaction Costs
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Create several measures of transaction costsbased on market data with daily marketvolume orders (DMVO)
Produce forecasts of these measures
Compare these forecasts with transactionrecords from Abel Noser Solutions (ANS)
Do this daily for many US equity names over a
15 year period.
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IS is the difference between the market priceat the time the order is entered and the priceat which a trade is executed, measured as areturn.
IS =log(execution price/arrival price)*side◦ Where side is 1 for a buy order and -1 for a sell
order
◦ IS should be positive on average for all trades.
◦ IS has low ratio of signal to noise.
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Perold, Kyle, Grinold and Kahn, EngleFerstenberg Russell, Kyle and Obisheva,Pastore and Stambaugh, Hasbrouck, Roll,Amihud, Easley Lopez de Prado and O’Hara,
Russell, Jones and Lipson, Engle and Lange,Keim and Madhavan….
Many industry studies, ITG, ANS, AQR…
Apologies for the authors omitted
Forecast Transaction Costs
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Use transaction data from ANS. These are“parent” orders which were typically filled withsequences of small “child” orders.
We have execution price and fill quantity. We
also have a buy sell indicator. We approximate the arrival price by the opening
price.
We do not have the order size for orders that arenot completed. This is probably an important
bias. Thus we can compute IS for more than 300
million executed orders since 1998.
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We consider the entire daily market volume on aname as an order .
The arrival price is then the open and theexecution price is VWAP. We approximate with
the close. The direction of the trade must be inferred from
the price movement. If the price goes up, thetrade is classified as a buy and conversely for a
sell. An order which is a small fraction of daily volume
will incur only a fraction of the daily impact.
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Trade direction has always been inferred fromprices with market data. After all, everyexecution has a willing buyer and a seller.
The Lee and Ready algorithm offers two solutionsto signing a single trade – either the price change
is used or the price relative to the recent mid-quote. Lee and Ready was motivated by the idea that a
market order was active and executed by aspecialist. Today there are no specialists and
informed traders do not generally use marketorders. Limit orders can have price impact so thedirection of trade can best be inferred by theprice movement.
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Not all trades have equal price impact. Moreinformative trades have more impact. Hence,it makes sense to measure impact onaverage. Information on the informativeness
of trades can be used if available.
Easley, Lopez de Prado, O’Hara in a series ofpapers have introduced Bulk VolumeClassification or BVC which uses inter dailydata to classify trades by the price change.
We take this to the daily limit. DMVO
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Volatility Volume
Average Daily Volume
Bid Ask Spread
The correlation of volume and volatility iscentral to price impact.
Predictions of transaction cost are based onorder size relative to predicted volume.
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Amihud, Yakov(2002), Illiquidity and stockreturns: Cross-section and time series effects,
Journal of Financial Markets 5, 31– 56.
Grinold,Richard and Ronald Kahn(1999) Active
Portfolio Management: A Quantitative Approachfor Producing Superior Returns and ControllingRisk, McGraw Hill
Engle, Robert, Robert Ferstenberg and Jeffrey
Russell (2012), ―Measuring and ModelingExecution Cost and Risk,‖ The Journal of PortfolioManagement.
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Historical forecasts◦ Lagged 21 day average of absolute return divided
by daily dollar volume
◦ There are no unknown parameters in this system
◦ Forecasting can also be done econometrically byusing MEM models as in VLAB
Transaction costs are approximated by
multiplying ILLIQ by the order in dollars
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This specification ensures that expected IS isnon-negative. It is a generalization of GK.
Estimate with panel of one year data on alltrades in the year.
Forecast for the next years trades.
0 31 2
1 1t t t t IS e Volatility Volume ADV b bb b
- -=
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0,00E+00
5,00E-09
1,00E-08
1,50E-08
2,00E-08
2,50E-08
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Mean by Year of Daily ADN Weighted Mean of Mean of 21 Day Lagged IILIQ
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12/22/2014NYU Stern Volatility Institute 16
0
0,00005
0,0001
0,00015
0,0002
0,00025
0,0003
0,00035
0,0004
0,00045
0,0005
Mean by Year of Daily ADN
Weighted Mean of Out of Sample
ILLIQ Forecast of 1% ADV
0,00E+00
5,00E-07
1,00E-06
1,50E-06
2,00E-06
2,50E-06
3,00E-06
3,50E-06
4,00E-06
4,50E-06
5,00E-06
1 3 5 7 9 11 13 15 17 19 21
Mean by Year of Daily ADN
Weighted Mean of Out of Sample
ILLIQ Forecast of $1M
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12/22/2014NYU Stern Volatility Institute 18
0
0,005
0,01
0,015
0,02
0,025
0,03
0,035
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
forecast
realization
prediction
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12/22/2014NYU Stern Volatility Institute 19
0
0,0005
0,001
0,0015
0,002
0,0025
0,003
0,0035
0,004
1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
Mean by Year of Daily ADN
Weighted Mean of Out of Sample
CRSP GK Forecast of 1% ADV
0
0,0005
0,001
0,0015
0,002
0,0025
1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
Mean by Year of Daily ADN
Weighted Mean of Out of Sample
CRSP GK Forecast of $1M
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0
0,005
0,01
0,015
0,02
0,025
0,03
0,035
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
forecast
realization
prediction
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12/22/2014NYU Stern Volatility Institute 22
0
0,001
0,002
0,003
0,004
0,005
0,006
0,007
0,008
1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
Mean by Year of Daily ADN
Weighted Mean of Out of Sample
CRSP EFR Forecast of 1% ADV
0
0,0002
0,0004
0,0006
0,0008
0,001
0,0012
0,0014
1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
Mean by Year of Daily ADN
Weighted Mean of Out of Sample
CRSP EFR Forecast of $1M
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ABEL NOSER SOLUTIONSANCERNO TRANSACTIONDATA SET
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More than 300 million orders executed byANS clients from 1998-2013.
Arrival price is the open
Execution price is the average price of theexecuted shares.
Fill is the number of shares executed.
Data are cleaned, extremes are removed and
bucketed in many ways. Data are matched with CRSP by
date/cusip/symbol
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12/22/2014NYU Stern Volatility Institute 26
-0,001
-0,0005
0
0,0005
0,001
0,0015
0,002
0,0025
0,003
0,0035
0,004
0,0045
Mean IS
EW-IS
VW-IS
AW-IS
NW-IS
-0,0005
0
0,0005
0,001
0,0015
0,002
0,0025
0,003
Mean of IS 50-51%tile
EW-IS
VW-IS
AW-IS
NW-IS
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ANS Summary Statistics By Year
12/22/2014 NYU Stern Volatility Institute 29
• Comparison between:• VWAP: Un-weighted
average execution cost ifANS fills are priced at
VWAP approximated asaverage of open and closeprice
• EW-IS: Un-weightedaverage reported by ANS
•
Assumption is thatimpact of ANS executionsare priced in the marketdata 0
0,0005
0,001
0,0015
0,002
0,0025
0,003
0,0035
1 9 9 8
1 9 9 9
2 0 0 0
2 0 0 1
2 0 0 2
2 0 0 3
2 0 0 4
2 0 0 5
2 0 0 6
2 0 0 7
2 0 0 8
2 0 0 9
2 0 1 0
2 0 1 1
2 0 1 2
2 0 1 3
Mean IS Fills Priced at VWAP
EW-IS
VWAP
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The ANS estimate of Implementation Shortfallshows some surprising features.
The peak IS for medium to large tradesoccurs in 2009 and then it falls slowly.
For smaller trades it is in 2011 and seemssurprisingly large.
2001 and 2002 are very low cost years and
this seems surprising too.
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For each trade in ANS data set, predict ISfrom model parameters in preceding yearusing ANS fill and market variables.
Predict ANS IS as an intercept plus slope
coefficient on forecast for all trades in a year.This is sometimes called Mincer-Zarnowitzregression.
Consider the predictions of this model as thetransaction cost model.
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12/22/2014NYU Stern Volatility Institute 32
0
0,0005
0,001
0,0015
0,002
0,0025
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
forecast
realization
prediction
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12/22/2014NYU Stern Volatility Institute 33
0
0,0005
0,001
0,0015
0,002
0,0025
0,003
0,0035
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
forecast
realization
prediction
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Better spread data Explore impact of estimation universe
◦ CRSP is ~5000 names
◦ ANS is ~2000 names
Explore models of excess returns
Seek alternative samples of executions.
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A PROMISING APPROACHTO LIQUIDITY FORECASTING!