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Figure 2: Stocks A and B broken by traditional and clusters view, left and right, respectively.
Stock B: VWAP, OTD, Max 5%
Stock A : VWAP, OTD, Max 5%
Traditional Market View
• FTSE 100
• Large Cap
• Index Name
Cluster 1
• The most liquid of all
• Consistent and predictable
Traditional Market View
• FTSE 100
• Large Cap
• Index Name
Cluster 3
• Price and volume spikes
• Information leakage
Source: GS Global Markets Division. For Illustrative purposes only.
Figure 3: Shifts in intraday correlation and volatility for S&P500 stocks.
70%
50%
United States - Correlation
United States - Volatility
45%
60%
40%
50%
35%
Securities Division | Confidential
40%
30%
2
30%
25%
20%
15%
20%
10%
10%
5%
0%
Jan 2014
09:30 - 10:00
Jan 2015
10:00 - 11:00
Jan 2016
11:00 - 12:00
Jan 2017
12:00 - 15:00
Jan 2018
15:00 - 16:00+
0%
Jan 2014
09:30 - 10:00
Jan 2015
Jan 2016
10:00 - 11:00
11:00 - 12:00
Jan 2017
12:00 - 15:00
Jan 2018
15:00 - 16:00+
Source: Goldman Sachs QES, Reuters, Bloomberg
the simple ‘addition’ of single stock
variances.
Over the last couple of years our
team has incorporated many of the
above insights, and a plethora of oth-
ers, to our suite of Algorithmic Port-
folio Execution products (APEX) to
Securities Division | Confidential
help our clients improve execution
and reduce slippage. When we
consider execution of anything
more than a single stock, we utilise
three modelling pillars to account
for intraday (and overnight, where
applicable) dynamics: risk, impact
and multi-period optimisation.
Risk modelling accounts for the
non-stationary nature of the cova-
riance structure of stocks intraday
and for overnight effects explicitly.
Market impact estimation is more
granular—it incorporates time of
day effects and models are trained
within a more homogenous set of
stock characteristics to improve
their explanatory power. Finally,
optimisation of the balance between
risk and cost is multi-period, dynam-
ic in its horizon specification, and
adaptive to the characteristics of the
sets of stocks being traded and the
execution objective.
Portfolio execution is a complex
area of trading that has historically
been approached with similar theo-
rems and practices utilised in quant
portfolio management, like factor
models and mean-variance opti-
misation tools, typically ingesting
daily data The ever-changing nature
of global markets, the non-linear
convolution of micro and macro
effects, the non-stationary nature
of the higher frequency covariance
structure, the global intraday con-
tagion effects and the continuously
morphing impact of exogenous
factors—like the active to passive
shift—onto trading dynamics, render
such portfolio approaches, as well
1
as purely single-stock calibrated
algorithms, relatively ineffective in
trading portfolios of stocks.
The preponderance of computing
power, coupled with innovative
modelling techniques and excru-
ciating curation and interrogation
of masses of data, have allowed
us to take a few steps closer —we
believe—to deciphering some of the
more pertinent questions of optimal
execution. The beauty (and bane) of
research in our industry is that this
quest is fairly boundless in its scope.
The above information is an analysis under certain scenarios and is not a representation of future performance. The stated values above are the theoretical as of the date calculated and are not representation that any transaction
can be effected at that values. We are not soliciting any action based upon this material. The information provided herein was supplied in good faith based on information which we believe, but do not guarantee, to be accurate or
complete; however we are not responsible for errors or omissions that may occur. The information provided is historical data. Past performance is not indicative of future results. For illustration purposes only.
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