Execution strategies
Why is it so hard to optimize?
Tianwu (Michael) Cai & George Sofianos
Goldman Sachs Equity Execution Strategies
Q Group Seminar
October 2007
1
Overview
„
Big change over past decade: buy-side traders now choose from a wide
spectrum of execution choices
„
„
„
„
This was not always the case
Ten years ago few execution choices and buy-side traders would delegate most of
these choices to high-touch executing brokers
Expected short-term alpha is perhaps the most important determinant of the
appropriate execution strategy1
We have a large data set of buy-side trader executions across the whole
spectrum of execution choices
„
What is the empirical evidence: do traders optimize across execution choices?
„
We find no evidence traders optimize executions based on expected ST-alpha
„
Why?
„
„
„
„
„
The choice of execution
strategy is shifting away
from center of order flow
ST-alpha is difficult to forecast?
Sub-optimal flow of information between PMs and traders
Transition period: traders are still learning
The offering is too complicated, traders cannot easily choose
What are the implications?
„
„
„
Cannot properly evaluate execution strategies & algorithms if traders do not optimize
their usage of strategies & algorithms
Should not design execution strategies & algorithms that require unrealistic information,
e.g. dynamic algorithms
Traders should be modest in their execution choices; should not fine-tune too much
1. See Abrokwah & Sofianos, “Shortfall Surprises,” Journal of Trading, Summer 2007
2
Wide spectrum of execution choices
Increasingly, buy-side traders make
most of these execution choices
Single-stock trading
1
2
The
buy-side
choices
Full
service
High-touch
commissions
> 12 bps
more choices…
Capital request
Market
Agency
Limit
VWAP
DMA
algo
trading
Aggressive
Passive
Shortfall
Participation, etc
Broker-sponsored (e.g. REDIPlus)
3
DMA
other
Low-touch
commissions
< 3 bps
ECNs (e.g. BATS)
Crossing networks (e.g. Sigma X)
Portfolio trading
4
5
6
Full
service
DMA
algo
trading
DMA
other
Agency
Capital request
Shortfall
Participation, etc.
Broker-sponsored
How should buy-side traders choose?
How do buy-side traders choose?
3
How should buy-side traders choose: the Cube framework1
ƒ Choice of execution strategy depends on order difficulty
ƒ Order difficulty depends on
ƒ Order size
ƒ Stock liquidity
ƒ Trade urgency
Hypothesis: for difficult orders buy-side
traders should choose high-touch; for
easy orders should choose low-touch
Small-cap
Large order
High urgency
Most difficult orders
Large
orders
High urgency
Small
orders
Large-cap
Small order
Low urgency
Low urgency
Large-cap
Easiest orders
1. For details see Goldman Sachs, Street Smart report, Issue 22
Small-cap
ƒ Trade urgency has 2 components:
Trade urgency
ƒ Short-term (ST) alpha
ƒ Execution risk
4
The trading cost measure: execution shortfall
ƒ Definition of execution shortfall (buy orders)
ƒ Execution price minus price when trader received the order (strike price)
ƒ Includes opportunity cost of slow execution
bps
150
125
Order to buy 100,000 shares XYZ
100
Average execution price
60 bps
75
50
Strike price
0 bps
Execution shortfall
25
0
9:30
10:00
10:30
11:00
11:30
12:00
12:30
13:00
13:30
14:00
14:30
15:00
15:30
16:00
-25
-50
execution horizon
-75
-100
Hypothetical example
5
Trade urgency: ST-alpha
1
ƒ Definition of ST-alpha (buy orders)
ƒ Price increase over execution horizon,
aside from impact of trade itself
bps
Hypothesis: higher expected ST-alpha means
higher trading urgency; traders should choose
higher execution aggressiveness
150
The two components of shortfall
125
Order to buy 100,000 shares XYZ
ƒ Liquidity impact
ƒ ST-alpha loss
100
Very different trading implications
60 bps
75
50
Liquidity
impact
0 bps
40 bps
25
ST-alpha
ST-alpha
loss
0
9:30
10:00
10:30
11:00
11:30
12:00
12:30
13:00
13:30
14:00
14:30
15:00
15:30
16:00
-25
Trader objective:
-50
execution horizon
-75
Minimize shortfall = minimize
liquidity impact + ST-alpha loss
Minimize impact: execute slow
Minimize ST-alpha loss: execute fast
-100
Hypothetical example
6
Trade urgency: volatility and execution risk
ƒ Pure execution risk
ƒ Price equally likely to go up or down
ƒ Risk increases with time to completion
bps
Hypothesis: assuming risk aversion, higher execution
risk means higher trading urgency; traders should
choose higher execution aggressiveness
ƒ Risk depends on stock volatility
150
125
2
Order to buy 100,000 shares XYZ
100
One STD execution risk (+ve)
75
50
25
0
9:30
10:00
10:30
11:00
11:30
12:00
12:30
13:00
13:30
14:00
14:30
15:00
15:30
16:00
Intra-day execution horizon
-25
-50
-75
One STD execution risk (-ve)
Execution start time 9:30
-100
Hypothetical example
7
The fundamental execution optimization
ƒ Choose execution aggressiveness
to minimize impact plus execution
risk plus expected ST-alpha
Implication
s
bps
150
ƒ Higher volatility, higher execution risk,
higher execution aggressiveness
ƒ Higher expected ST-alpha, higher
execution aggressiveness
Objective function for trader to minimize
(I + λ ve + Ф ae)
Order to buy 100,000 shares XYZ
125
Trader risk aversion
Execution risk (+ve)
100
75
Expected ST-alpha (ae)
60 bps
50
25
Expected impact (Ie)
Optimum end time 10:45
0
9:30
10:00
10:30
11:00
11:30
12:00
12:30
13:00
13:30
14:00
14:30
15:00
15:30
16:00
Intra-day execution horizon
-25
-50
-75
Execution start time 9:30
Execution risk (-ve)
-100
Hypothetical example
8
How do buy-side traders choose? Overview
Some intriguing evidence on suboptimal execution optimization
„
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„
„
„
Effect of recent market turmoil on execution choices
„
No shift from low-touch to high touch
„
No attempt to reduce execution risk: are traders risk neutral?
Choice between high-touch and low-touch
„
Some evidence of differentiation
„
Comparisons of short-term price dynamics
Choice between passive and aggressive algorithms
„
“VWAP or Shortfall Algorithms” paper1
„
Just as likely to get it wrong as right
Within an algorithm choice of aggressiveness level
„
Shortfall (4Cast) algorithm risk preference settings
„
Just as likely to get it wrong as right
Choice of aggressiveness level across all execution strategies
„
Regressions of participation rate on possible determinants
„
Again, just as likely to get it wrong as right
Our main focus
1. Rakhlin & Sofianos, “The Choice of Execution Algorithm: VWAP or Shortfall,” Journal of Trading, Winter 2006
9
Effect of recent market turmoil on execution choices1
July 26
Mean
%
4
Benchmark period:
Jul 02 – Jul 25
1.0 %
Market turmoil period:
Jul 26 – Aug 17
2.3 %
3
S&P 500 Daily Volatility
3.5
3.3
2
2.3
2.0
1.9
1.0
0.5
0.6
0.8
0.7 0.6
2.1
1.9
1.4
1.1
1.0
0.4
2.2
2.2
2.0
1.6
1.6
0.9
2.7
2.6
1.4
1.0
0.9
0.6
0.5
Benchmark period
8/17
8/16
8/15
8/14
8/13
8/10
8/9
8/8
8/7
8/6
8/3
8/2
8/1
7/31
7/30
7/27
7/26
7/25
7/24
7/19
7/18
7/17
7/16
7/13
7/12
7/11
7/10
7/9
7/6
7/5
7/3
7/2
0
7/23
Sharp increase in execution
risk & order difficulty
7/20
1
3.0
2.8 2.8
2.2
2
Moved from low volatility to high
volatility on Thursday, July 26
Market turmoil period
Hightouch
37.5%
Hightouch
38.3%
Hypothesis: market turmoil increased
order difficulty so we should see a
shift to high-touch
We see no such shift in our data!
Algo
61.7%
Algo
62.5%
1. Abrokwah & Sofianos, Street Smart, Issue 27, Aug 27, 2007.
2. Daily high minus daily low as % of average high plus low.
Also, within high-touch we see little
shift towards higher aggressiveness
to offset the increased execution risk
10
More on the recent market turmoil1: choice of algorithms
Benchmark period
Market turmoil period
Other
PortX
3%
TW AP 3%
6%
Other
PortX
TWAP 5% 4%
2%
Sonar
12%
VWAP
37%
Sonar
19%
VW AP
42%
Hypothesis: market turmoil
increased execution risk so we
should see a shift to more
aggressive algorithms
We see no such shift in our data!
Little change in average order
characteristics across the various
GSAT algorithms
4Cast
16%
4Cast
14%
Participate
18%
The participation rate actually
decreased slightly!
Participate
19%
$ value executed
Order size
#
Orders
%
ADV
#
Shares
Arrival
strike
time
Part.
rate
Expected
shortfall
(bps)
Actual
shortfall
(bps)
Alpha-toclose
(bps)
Exec.
half life
Algorithmic trading (US GSAT)
Benchmark
119,105
4.8%
9,913
11:13
10%
12
14
15
105 min
Market turmoil
171,025
3.6%
10,875
11:30
9%
14
25
23
90 min
1. Cai & Sofianos, Street Smart, Issue 28, Sep 20, 2007.
11
Choice between high-touch and low-touch
Evidence of some order flow segmentation between
high-touch and low-touch
ST-alpha higher for high-touch
„
„
Our ST-alpha measure:
return to T+5
bps
50
High-touch capital request
High-touch agency
Low-touch
40
30
2,147 orders
(small sample)
26
78,805 orders
21
20
221,338 orders
15
10
28 bps ST-alpha
21 bps ST-alpha
10 bps ST-alpha
0
0
Exec Price
T close
T+1 close
T+2 close
T+3 close
T+4 close
T+5 close
In constructing the short-term price dynamic we exclude orders with clustering (same client, symbol, side) T to T+5
12
Choice between passive and aggressive algorithms1
„
„
VWAP is a passive algorithm, Shortfall (4Cast) is a more aggressive algorithm
What is the evidence on how buy-side traders choose?
Sub-optimal choice between VWAP and 4Casta
Expected Shortfallb
a.
b.
c.
d.
e.
f.
#
Orders
Two
Hours
Actual
Horizon
Part.
Ratec
Actuald
Shortfall
STalphae
Execution
Half-lifef
VWAP algorithm
28,022
12 bps
12 bps
10%
12 bps
(1.0)
14 bps
(1.7)
201 min
4Cast algorithm
5,033
11 bps
14 bps
22%
15 bps
(1.2)
12 bps
(3.6)
59 min
ST-alpha actually
lower for 4Cast!
Standard errors in brackets below the estimates.
From Goldman Sachs expected shortfall model assuming the execution starts at order arrival and ends two hours later or to the close,
whichever comes first. $ value-weighted.
$ value filled as % of total $ value executed in the market over the execution horizon. If numerator exceeds denominator we add
numerator to denominator.
For buys: volume-weighted execution price minus strike price as percent of strike price. For sells: Strike price minus volumeweighted execution price as percent of strike price.
Alpha-to-same-day-close. For buys: same-day official close minus strike price as % of strike price. For sells: strike price minus
same-day official close as % of strike price.
$ value-weighted (in case of multiple executions) time to execution in minutes.
Hypothesis: Orders
with higher STalpha should go to the more
aggressive 4Cast algorithm
Similar pre-trade
order characteristics
Higher impact of
more aggressive
executions: but why
incur the extra cost?
Our data shows this is not the case!
1. Rakhlin & Sofianos, “The Choice of Execution Algorithm: VWAP or Shortfall,” Journal of Trading, Winter 2006
13
A simple empirical model of optimal execution aggressiveness
„
„
„
„
„
„
„
The model
Examples of execution aggressiveness
e
e
e
„ π* = F( X , v , a )
„
Choose short execution horizon
Where
„
Choose high participation rate
*
„ π = optimally chosen execution aggressiveness
„
Request capital
e
„
Choose must-fill market orders
„ v = expected volatility
e
„ a = expected ST-alpha
e
„ X = other factors determining execution aggressiveness
Using a linear approximation for illustration purposes:
*
e
e
e
„ π = b1 X + b2 v + b3 a + u
Our main hypothesis:
All else equal, optimizing traders should execute more
aggressively for higher expected volatility and ST-alpha
„ b2 > 0, b3 > 0
To empirically test the model, we measure:
*
a
„ π by the actual intra-day participation rate P
e
„ v by the previous 21 days median volatility
e
„ a by the actual T+5 price return (perfect foresight)
e
„ X other factors include: stock capitalization, quoted spreads, listing venue, limit order
Unobservable factors that could explain chosen aggressiveness
„ Forced liquidations
„ Holding constraints, etc.
„ By definition, we cannot control for these unobservable factors
In our analysis we focus on intra-day executions
14
The actual intra-day participation rate
„
„
„
„
„
Definition of actual intra-day participation (part) rate
a
a
a
„ P = Q / EV
Where
a
„ Q = actual quantity executed
a
„ EV = actual consolidated market volume in stock over execution horizon
The part rate is a natural measure of order aggressiveness
But: must deal with several measurement issues
„ Correlation between part rate and order size as % of day’s volume (DV)
„ Ceilings on part rate
„ Ideally we need the intended part rate but we only have the actual part rate
„ Bi-modal distribution of part rate: small-order fast executions
„ In certain pathological cases, part rate can exceed 100% (we exclude them)
We examine each of these issues in turn
Order arrives at
Goldman Sachs
Execution
completes
EV
Market
opens
11:00
(execution-horizon
volume)
13:30
9:30
Market
closes
16:00
DV
(day’s volume)
By definition: EV <= DV
15
Correlation between part rate & order size as % of day’s volume
„
„
Order size to execution-horizon volume (Qa / EVa)
„
The intra-day part rate can never be less than order size as % of day’s volume
a
a
a
a
a
„ P = Q / EV >= Q / DV
Because
a
a
„ EV <= DV
Also ceilings on part rate
1
„ Part rate cannot exceed 100%
„ Trading rules: e.g. do not exceed 25% of day’s volume
100%
Pa
EP
Rules: e.g. do not
exceed 25% of
day’s volume
Because EVa <= DVa it must
be that Qa/EVa >= Qa/ DVa
450
Order size to day’s volume (Qa / DVa)
Solution 1: A two-step approach
„
Step one
„ Regress part rate on order size as % of day’s
volume: Qa / EVa on Qa / DVa
„ Non-linear to take account of ceilings
„ Call the residual the excess part rate (EP)
„
Step two
„ Regress EP on the factors that may explain the
choice of high aggressiveness
Solution 2
„
One-step, but add order size as % of day’s volume as
an additional explanatory variable
Solution 3
„
One-step, but use the difference between part rate
and order size as % of day’s volume as the dependent
variable
It does not make much difference which solution we use!
We report results for Solution 2.
16
The evidence in our data on the part rate
Order size to execution-horizon volume (Qa / EVa)
100%
Relatively
few orders
above 50%
50%
450
Order size to day’s volume (Qa / DVa)
17
Intended and actual part rates
„
Traders optimize their intended part rate (Pe), based on information
available pre-trade
„
Pe = G( Xe, ve, ae)
„
But what we observe is the actual part rate Pa
„
Reasons why actual part rate may differ from intended part rate:
„
„
„
Actual execution-horizon volume (EVa) different than expected
(EVe)
Actual executed order size (Qa) different than intended (Qe)
„
Cancelled market orders
„
Limit orders that do not fully execute
We control for these factors by including in our regressions:
„
Volume surprises (EVa/EVe)
„
„
Fill rates (Qa/Qe)
„
„
We measure expected volume as the prior 21 trading days
median
Hypothesis: positive volume
surprise (EVa > EVe) lowers
actual part rate
Where we measure intended order size by the order size
submitted
Hypothesis: low fill rate
lowers actual part rate
Limit order dummy
18
ST-alpha again
„
Back to our model….
„
Pe = G( Xe, ve, ae)
„
We cannot observe ae
„
We use actual ST-alpha (aa) through T+5 close (see diagram below)
„
„
Implications of using actual (perfect foresight) ST-alpha
„
„
T+5 to allow for reversal to play through
Cannot distinguish between inability to optimize or inability to forecast ST-alpha
Complication: multi-day executions - impact may stretch over several days
„
We drop orders that “cluster” T through T+5
„
Same-trader, same-symbol, same-side within the 5-day window
Execution
completes
Order arrives at
Goldman Sachs
Buy order
11:00
13:30
aa
T+5 close
19
Small orders with fast executions
„
Another issue with the part rate:
„
„
„
Small orders that execute fast may have a very high part rate but it is not aggressiveness
„
The liquidity is there just take it
„
100 shares of MSFT executing instantaneously may have a 100% part rate
Our model applies to relatively large orders where there is a credible time-impact trade-off
„
Part. rate
It has a bi-modal distribution as a function of order size
What length execution time provides a credible time-impact trade-off?
100%
Solution: Drop orders with fast executions
„
Sensitivity analysis using different cut-offs
Drop
Bi-modal distribution
450
Order size
20
Summary of main estimated aggressiveness model
„
Dependent variable
a
a
„ Log( P / (1-P ))
a
a
a
„ Where P is the actual part. rate (Q / EV )
The 9 explanatory variables
Relation to part. rate
Detail
Notation
Functional form
1
Size as % of DV
POSITIVE (+)
Executed order size as % of same
day’s volume
Qa / DVa
Log
2
Volume surprise
NEGATIVE (-)
Expected day’s volume divided by
actual day’s volume
DVe / DVa
Log
3
Fill rate
NEGATIVE (-)
Actual quantity executed divided by
intended quantity
Qa / Qe
Log
4
Market cap
Stock market capitalization
5
Spread
21-day median daily average bid-ask
spread (in bps)
6
Listing venue
NYSE=1, NASDAQ=0
7
Limit order
NEGATIVE (-)
Limit=1, Market=0
8
Volatility
POSITIVE (+)
21-day median % day’s high price
minus low price
ve
9
ST-alpha
POSITIVE (+)
Percent price return from order
arrival to T+5 close (in bps)
aa
„
Log
Main results are not sensitive to the regression functional form
21
Sample description
„
„
US equity trading, Goldman Sachs client orders
June 06 to June 07 (13 months)
„
„
„
„
We aggregate intra-day same orders
„
„
„
„
„
„
„
„
„
„
Only stocks with US country of origin
Initial
Only common stocks
After aggregation
Regular settlement
After filtering
Intra-day executions (09:30 – 15:55)
Excl. multi-day
Execution half-life > 1 min
Drop odd lots
Drop if exec. quantity > exec. horizon volume
Drop outliers, e.g. 5-day return > 20%, shortfall > 1000 bps
Drop orders with missing information
~Number of orders in sample
High-touch
single-stock
Low-touch
algo (GSAT)
Total
1,400,000
1,600,000
3,000,000
300,000
700,000
1,000,000
170,000
530,000
700,000
70,000
230,000
~300,000
We also exclude multi-day T to T+5 orders
„
„
Same-trader, same-symbol, same-side1
We do extensive filtering, e.g.
„
„
High-touch single-stock (US Shares) orders
Low-touch GSAT Direct orders (GSAT=Goldman Sachs Algorithmic Trading)
Low-touch GSAT REDIPlus orders (Feb to Jun 07)
Same-trader, same-symbol, same-side1
Final sample: ~300,000 orders
1. The order after aggregation assumes strike time at first order's arrival and end time at last order's last execution
22
Part rate regressions: results and sensitivity analysis
Summary statistics: the t-values
Hypothesis
Intercept
Dominates
Never positive
& significant!
Execution half-life:
Half-life >15 & order size (%DV):
>1 min
>5 min
>15 min
>30 min
>0.1%
>0.5%
>1.0%
>2.0%
1
2
3
4
5
6
7
8
-21.5
18.3
36.6
9.9
22.9
4.8
2.0
4.8
Size as % of DV
(+)
426.1
649.8
817.9
996.6
547.4
327.8
241.6
170.3
Volume surprise
(-)
1.9
-7.6
-10.0
-14.2
-12.6
-6.1
-2.1
-0.4
Fill rate
(-)
-35.3
-43.2
-40.9
-36.7
-34.8
-23.4
-15.9
-9.3
Market cap
39.8
23.3
11.3
8.9
21.8
28.9
23.5
12.8
Spread
23.8
9.1
6.5
5.2
8.7
5.6
2.5
0.0
Listing venue
16.5
9.1
0.3
-6.4
-4.9
-4.4
-2.6
-1.7
Limit order
(-)
-4.7
-11.2
-8.1
-1.1
-7.1
-5.9
-4.6
-0.2
Volatility
(+)
10.5
9.6
0.0
-2.0
5.8
12.4
11.2
6.7
ST-alpha
(+)
-0.8
-1.8
-0.8
0.5
-0.4
-0.7
-1.2
-1.5
Sample size
R-square
302,290 245,410
41%
67%
200,766 163,972
80%
88%
156,369 107,259 81,942
68%
54%
48%
55,407
44%
Preferred
„
„
No evidence of ST-alpha optimization
„ In all specifications!
„ ST-alpha is never positive & significant
„ A robust result
„
Volume surprise, fill rates, limit orders:
significant and with predicted sign
„
„
Most of the time!
Volatility: mixed evidence
Tried many other specifications (market orders only, algo only, high-touch only, etc.), similar results!
23
The two-step approach
„
Step one
„
The t-values
Hypothesis
„
Half-life
>15 min
„
Step two
„
Step one: part rate on size as % DV
Intercept
Size as % of DV
Regress part rate on order size as % of day’s volume
(Qa / EVa on Qa / DVa)
Call the residual the excess part rate (EP)
Regress EP on the factors that may explain choice of
high aggressiveness
140.5
(+)
R-square
900.5
80%
Most explanatory power in first step
Step two: excess part rate
Intercept
-10.3
Volume surprise
(-)
-9.2
Fill rate
(-)
-40.5
Market cap
9.9
Spread
7.1
Listing venue
0.8
Limit order
(-)
-6.8
Volatility
(+)
-0.4
ST-alpha
(+)
-0.6
R-square
1%
„
Consistent with our one-step regression results:
„
„
Most explanatory power is in the first step regressing
the part rate on order size as % of day’s volume
In the second step, little explanatory power in
explaining the excess part rate
Little explanatory power in explaining the
excess part rate
24
Liquidity impact and reversal
The t-values
„
„
„
Am interesting decomposition of ST-alpha
a
„ Liquidity impact (I )
a
„ Reversal (R )
Where
a
a
a
„ a = I + R
We also ran regressions on the impact and
reversal decomposition
a
e
e
a
a
„ P = b1 X + b2 v + b4 I + b5 R
„ Hypothesis: b4 > 0, b5 < 0
Execution
completes
Hypothesis
Intercept
Half-life >15 min
A
B
36.6
36.5
Size as % of DV
(+)
817.9
813.4
Volume surprise
(-)
-10.0
-9.8
Fill rate
(-)
-40.9
-40.9
Market cap
11.3
11.1
Spread
6.5
6.5
Listing venue
0.3
0.3
Limit order
(-)
-8.1
-7.6
Volatility
(+)
0.0
-0.2
ST-alpha
(+)
-0.8
Liquidity impact
(+)
5.6
Reversal
(-)
-2.2
R-square
80%
High part rate
drives up price but
price then reverses
80%
Ra (reversal)
Order arrives at
Goldman Sachs
Ia (impact)
aa
Buy order
10:00
12:30
T+5 close
25
Evidence on algo choice: choosing aggressiveness settings
„
A different dependent variable in our
regressions
„
„
No longer part rate
Our results are not sensitive to the use of
part rate as our aggressiveness indicator
„
„
„
„
All 4Cast orders
„
Intercept
-18.9
„
Size as % of DV
14.2
„
Volume surprise
4.8
Fill rate
-3.9
Market cap
25.6
Spread
3.2
Listing venue
11.1
Limit order
59.7
Volatility
-0.6
ST-alpha
-2.5
„
25,977
„
„
„
Sample size
„
1 (least aggressive) to 10 (most aggressive)
Divided them into two groups:
1 – 4 Aggressive dummy variable=0
5 – 10 Aggressive dummy variable=1
We estimated a multifactor probit model to explain the
[0,1] aggressiveness choice
Main findings:
„
Again no
evidence traders
optimize
Do traders optimize in the choice of aggressiveness
settings?
Focus on the Goldman Sachs 4Cast algorithm
Ten aggressiveness settings
„
„
No evidence traders optimize
How about within an algorithm?
„
„
The 4Cast probit z-values
We already discussed the evidence on the choice
between passive and aggressive algorithms
Volatility does not explain the choice
ST-alpha does not explain the choice
No evidence traders optimize the 4Cast algorithm’s
aggressiveness settings
In terms of volatility
In terms of ST-alpha
26
Why is it so hard to optimize? Focus on evidence on ST-alpha
„
Shortfall Surprises paper1: ST-alpha main reason why actual shortfall differs from
expected shortfall
„
„
„
„
„
Expected ST-alpha by far the most important factor in predicting shortfall
Expected ST-alpha by far the most important factor in choosing the right execution strategy and
managing shortfall
But our data suggests no correlation between actual ST-alpha and choice of execution
strategy
Why?
Possibility one: traders do not optimize
„
„
A transition period, they are still learning
The offering is too complicated, traders cannot easily choose
The sell-side should
simplify its offering, e.g.
streamline algorithms
„
Possibility two: traders cannot easily predict ST-alpha
„
We prefer the 2nd possibility
„
The question then becomes: why is it so hard to predict ST-alpha?
„
At least four reasons
„
„
„
„
9
Organizational failure: suboptimal communication between portfolio managers and traders
ST-alpha is intrinsically hard to predict: exactly how alpha signal gets incorporated in price
ST-alpha is intrinsically hard to predict: short-term order flow considerations
If can predict ST-alpha should be trading on that!
1. See Abrokwah & Sofianos, “Shortfall Surprises,” Journal of Trading, Summer 2007
27
The investment & execution process
Organizational failure
The investment process
ƒ
Idea generation
ƒ
Stock selection
ƒ
Portfolio construction
ƒ
Maximize PM alpha
Strategic
The execution process
ƒ
Minimize alpha loss over the
execution horizon
Tactical
PM alpha:
months to years
ST alpha:
hours to days
Idea generation
Stock selection
Portfolio construction
Execution process enhances investment process
Trade execution
execution horizon
Investment horizon
28
The ideal organizational structure
ƒ Complete integration of idea generation, stock selection, portfolio
construction & execution
ƒ Ideas are balanced with expected trading costs
ƒ Dialog exists among idea generation, portfolio construction & execution
9
Expected
shortfall
Idea generation
Stock selection
Portfolio construction
Trade execution
Organizational failure
In reality, sub-optimal information flow across three stages in the process
Portfolio
managers &
quant
research
Estimating ST alpha
Buy-side
traders
Sell-side
traders &
algorithms
ƒ PM input: investment strategy
ƒ Trader input: “feel for the market”
ƒ Data analysis: the “science” part
29
Challenges in estimating ST-alpha
„
3 % excess return
one month out
Extracting ST-alpha from PM-alpha
„
Not trivial, even with perfect communication!
Why a straight line?!
How fast does the PM-alpha signal get
incorporated in the price?
Also depends on execution strategy!
execution horizon
Investment horizon
„
Distinguishing between momentum and reversal (permanent and transitory price moves)
execution horizon
During execution, price accelerates away from trader
How should trader react?
Execute faster? May be causing it!
Or slow down and wait for reversal?
Order flow concentration
„
What determines ST-alpha?
„
„
Traders ”feel for the market” is important
„
„
„
Short-term order-flow dynamics more than long-term
PM-alpha signals
But for traders to have a good “feel for the market”
must have central view of order flow
NYSE specialists & floor brokers used to, but nobody
has any more!
Traders that are good in predicting ST-alpha will
quickly move to other opportunities!
NYSE specialists & floor brokers
Sell-side traders
Buy-side traders
Choice of execution
strategy is shifting
away from center of
order flow
30
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