Transactions Costs
and Trading
Zhiwu Chen, Yale School of Management
Note: This presentation is mostly adapted from the slides prepared by Ian
Domowitz, Managing Director of ITG, for his talk at Yale on Oct. 1, 2003. His
work is gratefully acknowldged.
Best
Execution
ITG Inc., Member NASD, SIPC
©2003 ITG Inc., All Rights Reserved, Not to be reproduced without permission
91603-82599
Successful Implementation Strategies
Portfolio Management
Risk analysis
 Optimization
 Fair value pricing

Pre-Trade Analysis
Trade Blotter
Post-Trade Analysis
Performance vs.
benchmarks
 Sorted and
organized

Trading cost
 Risk analysis
 Optimal horizon

Trade data
 Organization

Trading
Access to all
liquidity sources
 Logical participation
trading strategies

©2003 ITG Inc. All Rights Reserved, Not to be reproduced without permission
2
Who’s Got the Alpha?*


Two funds:

Large Cap Value
Gross Alpha=13.1%

Small Cap Growth Gross Alpha=17.8%
Both Incur Trading Costs
Components of Transaction Cost
2.00%
1.50%
Market Impact
Opportunity Cost
Bid/Ask Spread
Commissions
1.00%
0.50%
0.00%
-0.50%
Large Cap Value
Small Cap Growth
*John Bogle Jr.. “Transaction Cost and Growth of Assets”
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3
Will the Real Return (and Risk) Please Stand up?
Large Cap Value
Trade Cost in bp
Turnover
Annual Cost
Small Cap Growth
51
180
600%
325%
51bp*600%*2=
180bp*325%*2=
6.10%
10.80%
Gross Versus Net Alpha
20.00%
18.00%
16.00%
14.00%
12.00%
10.00%
8.00%
6.00%
4.00%
2.00%
0.00%
Net Excess Return
Transaction Cost
Large Cap Value
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Small Cap Growth
4
Trading Costs Impact Fund Rankings
Top S&P 500 Funds
3 Year Annualized Return
% Return
-9%
Top 25 funds more
pronounced:
Average 8.5 bps
between ranks
-10%
Next 75 funds:
Average 0.6 bps
between ranks
-11%
-12%
0
20
40
60
80
100
Rank
So urce: Lipper
16 bps would move the #50 Fund to #20.
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5
Three Step Program


Measurement

Regular pre-trade and post-trade measurement

Focus on implicit costs of the entire trade
Analysis



Compare trades to appropriate benchmarks
Aggregate pre-trade and post-trade trade results by meaningful categories to
see hidden costs
Control

Trading as a source of value

Logical participation

Control the attributes of residual portfolios throughout the execution process
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6
Measurement

Trading costs are more than commissions and spreads

Implicit costs, including market impact, are significant...

But do not omit delay and opportunity costs
midpoint
BID
EXECUTION
ASK
Spread
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Market Impact
COST
7
Measure Indirect Trading Costs
Paper
Returns
Returns if all
trades were
executed
instantaneously
and with zero
cost at the
decision price
Implementation
Shortfall
Direct Costs
Commissions,
Ticket
charges,Taxes
Indirect Costs
Real
Returns
Trades partially or
fully executed at
prices achievable
in the market, or
not executed at
all
Delay Cost,
Timing Gain/Loss
Market Impact
Opportunity cost
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8
Types of Costs
PM Decision Price
Release Price
Delay
B/O Midpoint
at Execution time
Timing Gain/Loss
Executed
Price
(Actual)
Market Impact
Executed
Orders
Opportunity Cost
TIME
Opportunity Cost
Unexecuted
Orders
Executed
Price
(Assumed)
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9
Arbitrageur
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Price
Anomalies
10
The organization of stock exchange makes a difference to price impact
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11
On Benchmarks

So many choices, so much confusion

What benchmarks to use?

Miscommunication between traders and portfolio managers symptomatic of
benchmark issues

Traders may perform well versus VWAP benchmark

...but portfolio managers are dissatisfied

Pursuing a VWAP benchmark encourages traders to parcel out their trades over
several days, missing the opportunity to obtain alpha
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12
Typical Example
INTL (Inter-Tel)
close
10.5
10.0
9.5
9.0
8.5
8.0
7.5
7.0
1/10/01
1/11/01
1/12/01
1/13/01
1/14/01
1/15/01
1/16/01
1/17/01
Original Order: Buy 100,000 INTL 1/10/01 10:46
Executed as follows:
1/10/01 30,000 @ $8.00
1/11/01 20,000 @ $8.75
1/12/01 30,000@ $9.50
1/16/01 20,000@$10.00
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13
Benchmarks Results
Using Multi-Day, Order-Level
VWAP Benchmark
Cost is Negligible
But Using Decision Price
Cost is 14.25%
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14
A Study in Timing: an example

The head trader believes costs are too high for relatively liquid stocks

Goal: identify the cost drivers

The trade order management system has time stamps for:

When the order was released by the PM to the desk

When the desk released the order to the broker

When the broker executed the trade
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15
The Scenario
PM Releases
Order
Trading Desk
Places Trade
Order Received
By Broker
Exec Price
(Actual)
Decision Price
Delay
Timing Gain/Loss
Market Impact
Opportunity Cost (Slippage)
Cost 38 bp
Cost 14 bp
Total Cost 52 bp
Overall transaction costs were 52 bp from
order release to execution
Costs from order release by the PM’s to
the Trading Desk equaled 38 bp per share
Costs from when the trade was placed by the desk
to the Broker were 14 bp per share.
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16
Costs By Order Size / Market / Side
OrderSize
0 - 99
100 - 499
500 - 999
1000 - 1499
1500 - 2499
2500 - 4999
5000 - 9999
>= 10000
Trade Cost V. Benchmark
All Trades All Listed
(0.752)
(0.588)
1.693
1.957
(0.292)
0.331
(1.858)
(1.185)
(4.343)
(3.368)
(5.484)
(4.470)
(7.562)
(7.396)
(10.281)
(10.435)
(in bp)
All OTC
(1.556)
0.718
(1.795)
(3.961)
(7.994)
(8.773)
(12.317)
(6.379)
All Buys
(2.096)
1.858
(0.950)
(2.750)
(5.706)
(7.065)
(9.664)
(10.651)
All Sells
1.600
1.469
0.519
(0.914)
(2.724)
(3.483)
(5.706)
(9.988)
Blue = outperforms benchmark
White = Underperforms benchmark by less than 5bp
Tan = Underperforms benchmark by more than 5 bp
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17
Costs By Time Delay
Trade Cost V. Benchmark (in bp)
Time_Delay
1. 0 - 2
2. 2 - 4
3. 4 - 10
4. 10 - 20
5. more than 20
All Trades All Listed
2.613
(1.957)
(8.758)
(17.043)
(6.083)
2.897
(1.072)
(7.777)
(16.596)
(6.087)
All OTC
All Buys
All Sells
1.796
(5.565)
(15.111)
(19.427)
0.505
2.015
(3.391)
(10.114)
(16.135)
(7.356)
3.253
(0.412)
(7.216)
(17.980)
(3.811)
Blue = outperforms benchmark
White = Underperforms benchmark by less than 5bp
Tan = Underperforms benchmark by more than 5 bp
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18
Costs By Time Delay & Order Size
Trades V. Benchmark Open or Sell/Bid & Buy/Ask (in bp)
1000Time_Delay
0-99 Shares 100-499 500-999
1499
15002499
25004999
50009999
10000+
1. 0 - 2
2. 2 - 4
3. 4 - 10
4. 10 - 20
5. more than 20
0.888
2.171
4.297
3.076
3.902
1.177
3.613
0.005
2.767
-1.162
2.200
(2.590)
1.180
(4.720)
2.560
(7.290)
1.415
-9.851
-20.161
(1.103)
(1.484)
0.390
(3.238)
(6.931)
(0.484)
(5.095)
(11.112)
(4.139)
-8.137
-19.52
-9.502
(10.220)
(19.470)
(6.800)
(12.690)
(16.840)
(5.550)
(11.830)
(27.230)
(14.390)
Blue = outperforms benchmark
White = Underperforms benchmark by less than 5bp
Tan = Underperforms benchmark by more than 5 bp
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19
Back to the Head Trader

Not just large orders

Timing study might suggest excess costs for larger orders when
sufficient liquidity was unavailable

Instead, presentation of a coherent set of results elicits:

desk has been holding small and large orders to package together as part of
programs

the packaging has adverse consequences

opportunity costs were incurred when the markets moved against the trade
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20
Analysis

Building a narrative

Aggregate pre-trade and post-trade trade results by meaningful
categories to see hidden costs
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21
Periodic Reviews Add Value

Head of Trading performs periodic post-trade analysis to detect trends
and refine investment style


Classify by market, sector, etc.
Post-trade analysis indicates mediocre trading performance

Costs are 135 basis points overall, relative to an order-level, mid-point benchmark
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22
Outcome


Improve trading strategy and performance by synchronizing PM and
trader goals

Use implementation shortfall as the trader’s benchmark

Incorporate this benchmark in the PM’s stock selection process
Traders incented to obtain target price close to target price of the PM


Traders may be willing to pay up in some cases to get the trade done
PM’s are more aware of the liquidity characteristics of their trades and potential
costs
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23
Delay Costs: consider an example

Trader is concerned that his firm’s DOT executions are too costly

DOT flow is routed through one major broker


From the time the trade was released to the desk to the time of execution, costs
averaged 35 basis points (buy-ask, sell-bid)

In dollar terms, this was about 9.5 cents

Given the volume of DOT orders, this represents a significant cost
Should the broker be fired?
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24
Decomposition
PM Releases
Order
Desk Places
Trade
Desk Delay
Broker Gets
Order
Time Delay
Executed
Price
Market Impact
Executed
Orders
Delay Costs = 26 bps
TIME
Total Cost = 35 bps
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25
Results of Decomposition

Approach



Obtain time-stamp from TOMS to figure out time when order was first sent from the PM
desk, client’s trading desk.
Broker has time it received order
Of the 35 bps cost

26 bps is attributable to delays/slippage


Of which, 3 bps is noise due to time stamp mismatches
9 bps is the cost

Measured from when the broker received the order

Benchmark is buy at ask, sell at bid
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26
“9 bps is still too high!”

Maybe

Further analysis finds that some trades are sent prior to the open

Cost computation uses previous quotes, which might be considered to
be misleading
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27
Illustration for Sell Order
Portfolio managers have a systematic tendency to generate
sell orders prior to open if market is likely to decline
Previous Bid (Benchmark for sell)

Incorrect Attribution
of Cost (5 bps)
Opening Price

Broker Executes at
Opening Price
9:30AM
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28
Resolution

For orders received pre-open, use opening price as benchmark;
otherwise buy at ask, sell at bid

Results: Broker cost falls to 4 bps

Outcome

No change in broker

Methodology adopted to measure other brokers

Approach to creating program trades reviewed
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29
Now, what can you do to control price impact costs?

The real name of the game

Identify means of reducing price impacts

Example: liquidity monitoring possibilities

Larger sizes in an environment characterized by more trades

Larger sizes with smaller spreads
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30
Liquidity States and Costs
Size
low
high
low
Liquidity
high
Cost
Size
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31
Price Impact and Upstairs Trades
16
14
12
10
Predicted
Actual
Cross
8
6
4
2
0
10
20
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30
40
50
60
70
80
90
100
32
Horizon Managers

Given a strategy, trading over
extended horizons depends on
characteristics

For a particular stock, logical
participation depends on
strategy
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33
Different Stocks /Different Strategies To Reduce Costs
AMGN & LNY Intra-day Volume Dispersion
Pct of Daily Volume
18%
Bin
16%
14%
12%
10%
AMGN:
Volume
Curve
LNY:
Volume
Curve
Percent
Percent
9:30
17%
11%
10:00
12%
9%
10:30
9%
8%
11:00
7%
7%
8%
11:30
6%
7%
6%
12:00
5%
6%
4%
12:30
5%
6%
2%
13:00
5%
6%
13:30
5%
6%
14:00
5%
6%
14:30
6%
7%
15:00
8%
9%
15:30
10%
13%
Bin
LNY: Volume Curve
15:30
15:00
14:30
14:00
13:30
13:00
12:30
12:00
11:30
11:00
10:30
10:00
9:30
0%
AMGN: Volume Curve
AMGN
LNY
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Spread
(bps)
Volatility
(bps)
3
160
29
226
34
Trade Distribution Example
Aggressive:
high volatility, small
percentage ADV
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Passive:
low volatility,
high percentage ADV
35
Traditional Index Strategy v. Logical Participation
1800
1600
1587
1400
1200
920
1000
789
800
600
458
400
200
0
VWAP ADR's
ACE ADR's
VWAP ADR & SPX
ACE ADR & SPX
Expected Cost/Shr (BPS)
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36
Another way to manage costs: trading over Longer Horizons


Two objectives

match the desired trading distribution/benchmark closely

obtain favorable execution prices
Objectives achieved by

Placing and correcting limit orders to maximize opportunities to earn the spread

Sending marketable orders as necessary to keep on schedule

Design for large trade sizes in portfolio trading applications

Next generation VWAP
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37
A Server for Horizon Trading
Intelligent autopilot for portfolio
trading



Continuously monitor progress and
urgency
Bands define leeway for straying from
the distribution in search of better
executions
To price orders appropriately
according to market conditions
100%
Percent Completed

50%
Fills
0%
©2003 ITG Inc. All Rights Reserved, Not to be reproduced without permission
9:30
Time Horizon
4:00
38
Pitfalls in Pegging and Discretion Strategies

Typical pegging algorithm errors



contribute to momentum by instantaneously adjusting price to match all quote
changes
pegged orders typically leave an obvious information trail
Typical discretion order type errors


excess time and effort required to make informed discretion range judgements
constancy of discretion range over life of order, although aggressiveness should
be a function of urgency, which may change
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39
Pegging and Discretion Revisited

Enhanced pegging




peg an order loosely to the inside market
react conditionally, determining whether each quote change merits an order price
correction
randomize and blend in with the crowd
Dynamic discretion



automatically choose appropriate discretion range for each order independently
continuously adjust range over life of order, recalculating the trigger price that
demands liquidity
Adjust based on market conditions
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40
Beyond Simple Pegs


Supply liquidity to obtain favorable fills

Use carefully-timed aggressive orders to stay on schedule
Multiple electronic agents working in concert
Quoted Spread

Blended passive/aggressive strategy for price performance with on-time
completion.
One agent provides liquidity, pegging a piece
of the order loosely to the inside market.
Objective: to maintain exposure to the
inside market without driving prices
or leaking information
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Second agent trades opportunistically using
carefully-timed orders at marketable prices.
Discretion range adjusts dynamically
based on current urgency level.
Objective: to complete trade on schedule
41
Automating the Short Horizon

Watch every name individually

Update information continuously

Forecast quote movements:


Width of Spread

Direction of Market

Bid/Ask Volatility
If the model predicts favorable market movement


trade to capture a portion of the spread
If the market looks to move against the order

trade aggressively, based on the horizon
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42
The ITG View of Logical Participation
Inbound
Orders
Client
ITG
ITG Order Filter
SPI
activePeg™ Horizon
Small Orders
5-30 min Time Horizon
All Order Sizes
Large Orders
10-240 min Time Horizon 30-390 min Time Horizon
ITG Desk
Expert Manual Attention
ITG SmartServer Family
DOT
TriAct™
POSIT®
ITG OTC Router
NYSE
SuperMontage
AMEX
ECNs / ADF
Regionals
Market Makers
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43
Incorporate Risk
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
Use a pre-trade model that
incorporates a daily risk model
to quantify opportunity cost

Find optimal strategy to
minimize impact costs while
balancing delay costs
44
The Typical Tradeoff Picture
Market Impact vs. Opportunity Trade-Off
1.20
1.00
Cost
0.80
Minimum Cost Point
0.60
Market Impact
Opportunity
0.40
Total Cost
0.20
1
2
3
4
5
6
7
8
9
10
11
12
Time
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45
The Right Risk Model: Horizon Does Make a Difference
Short-term versus long-term risk of S&P 500
1
0.9
Annualized Risk
0.8
0.7
0.6
0.5
Short-term volatility can
differ significantly from
longer-term volatility
0.4
0.3
0.2
0.1
19
85
19 01
85
19 10
86
19 07
87
19 04
88
19 01
88
19 10
89
19 07
90
19 04
91
19 01
91
19 10
92
19 07
93
19 04
94
19 01
94
19 10
95
19 07
96
19 04
97
19 01
97
19 10
98
19 07
99
20 04
00
20 01
00
20 10
01
07
0
S&P is a registered trademark of McGraw Hill Inc.
©2003 ITG Inc. All Rights Reserved, Not to be reproduced without permission
Short-term
Long-term
46
Market & Specific Risk Matters More at Daily Levels
Risk Decomposition of S&P 500 index
16.00%
14.00%
Annualized Risk
12.00%
10.00%
8.00%
6.00%
4.00%
2.00%
0.00%
Total
Market
Size
Value
Daily Risk
Sector
Industry
Specific
Monthly Risk
S&P is a registered trademark of McGraw-Hill, Inc.
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47
A Complementary View



Two opposing forces

reduce market impact

reduce risk

a portfolio that behaves like the target portfolio as soon as possible
With appropriate cost and risk models

construct waves to implement the transition

analyze tradeoff between predicted cost and risk

same basic tools as the classical Markowitz portfolio problem
Example conclusion

“no wave that completes 15% of the transition, while costing 35 bps, will result in a
tracking error lower than 7.8%”
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48
Cost and Risk Tradeoffs
E fficien t F ro n tier
T ra n s a c tio n C o s t (b p s )
R is k (% )
5
7
9
11
-2 0
-2 5
-3 0
-3 5
-4 0
-4 5
-5 0
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49
Where Risk Control Meets Cost Control

Benchmarking

Strategy

Max $ traded

Min dollars at risk

Min trading costs

Urgency

Control characteristics
that add to cost of trade




$ risk
Tracking error
Sector balance
Liquidity exposure
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50
Example

This approach was recently used in a $1.2 billion two-sided transition
portfolio with 403 names


Original portfolio has aggregate tracking error of 3.5%
Transition instructions permitted the list to be traded in “waves” subject to
constraints

Analysis shows can trade a 25% “wave” of $307MM that cuts risk to 2.7%

Trade 81 of the 403 names


This wave improves liquidity of residual positions; order size drops from 18.7% to
14.7% of average daily volume
Successive application yields the optimal transition strategy
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51