Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
SYSTEM AND METHOD FOR AUTOMATICALLY OPTIMIZING A PORTFOLIO
FIELD
[0001] The present disclosure relates to a computerized system and method for
automatically generating and optimizing a portfolio and more particularly to a
system
and method that automatically tracks and analyzes an index for deployment in
generating the optimized portfolio.
BACKGROUND
[0002] In passive fixed income management, there have been challenges in
effectively rebalancing fixed income (Fl) index funds. Traditionally, this has
been
done manually by different portfolio managers having access to different
spreadsheets containing disparate information about an Fl fund to be
replicated and
attempting to manually estimate how to best replicate the Fl fund.
[0003] However, some items in the Fl fund may have various characteristics
that
make them difficult to replicate directly. For example, poor liquidity (e.g.
some bonds
cannot be sold at all, or some bonds can be sold but unable to be found in the
market
again, or some bonds can be traded but the costs may be prohibitive) prevents
direct
replication of the funds. Additionally, criteria for replication of an Fl
index are more
complex than other indices because of the large number of characteristics of
the Fl
index that need to be matched for replication. For example, the criteria for
"replication" of an Fl index fund are more complex than an equity index.
Notably, in
addition to matching sector weights, there may be a need to match many more
characteristics of the index.
[0004] The above issues make the portfolio optimization problem impossible to
be
performed manually and any such manual estimation of a passive fixed income
portfolio would lead to vast inaccuracies and errors. Additionally, delays of
presenting information to a user regarding the portfolio render such manual
systems
useless.
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[0005] Furthermore, defining a portfolio manually can be both time consuming
and
error prone. It is vital to define a portfolio with correct content that
efficiently tracks
an index such as a fixed income index.
[0006] Accordingly, there exists a need to obviate or mitigate at least some
of the
above-mentioned disadvantages of existing systems and methods. Notably, there
is
a need to dynamically define a portfolio with correct content that efficiently
tracks an
index such as a fixed income index. The proposed system and method provide an
automatic optimization tool for generating and optimizing a portfolio for
indexes such
as those used in passive fixed income management.
SUMMARY
[0007] It is to be understood that both the foregoing general description and
the
following detailed description are exemplary and explanatory only and are not
restrictive of the claims.
[0008] In one aspect, there is provided an improved system and method for
automating real-time portfolio optimization suitable for passive fixed income
funds,
such as bonds, where the liquidity may be a concern and the index is not
directly
replicable.
[0009] In one aspect, there is provided a system and method which generates an
optimized portfolio for tracking a passive index, e.g. a fixed income index
and
exposes the optimized portfolio information (e.g. its constituents and
characteristics)
on a real-time integrated dashboard user interface for subsequent interaction
and
manipulation of the portfolio. Additionally, in yet another aspect, the
optimized
portfolio is generated with additional constraints such as a limited number of
constituents (e.g. securities), while maintaining a desirable
representativeness of the
Fl fund. In yet another aspect, the system and method generates an optimized
portfolio which represents the sector weights of the index fund but
additionally
considers characteristics of the fund such as overall duration, sector
duration, rating
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weights, etc. In at least some aspects, the optimized portfolio selects a
limited
subset of the list of projected constituents from the target index having
optimal
liquidity scores and the associated weighting similar to the weighting in the
target
index.
[0010] In yet another aspect there is provided an automated real-time
portfolio
optimization suitable for passive fixed income funds.
[0011] Advantageously, in at least some implementations, the disclosed system
and
method improves efficiency, and accuracy of concurrently managing and
optimizing a
number of portfolios (e.g. fixed income portfolios), in real-time, by
minimizing tracking
error to a desired index (e.g. passive fixed income index) while considering
preferences for the portfolios when optimizing the portfolios. In at least
some aspect,
there is provided a portfolio analyzer for analyzing the desired index
including
forecasting the index into a future state (proforma benchmark or target index)
and
predicting liquidity levels of the index's holdings.
[0012] Further advantageously, in at least some aspects, the disclosed systems
and
methods provide at least one user interface, which presents analytics
information
(e.g. liquidity levels, proforma benchmarks, optimized portfolios, etc.) on a
visual
display for subsequent manipulation by a user such that any such manipulation
results in all users of the user interface receiving updated real-time
analytics
information on their corresponding local displays. Thus, this allows users
(e.g.
portfolio managers) to collaborate and share the application data related to
the
portfolio analytics and output portfolio in a shared worker environment such
as to
automatically compute any changes resulting from a particular user's actions
to all
related users associated with the portfolio.
[0013] In at least some implementations, there is provided a computing device
for
automatic generation of a portfolio, the computing device comprising a
processor, a
storage device and a communication device where each of the storage device and
the communication device is coupled to the processor, the storage device
storing
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instructions which when executed by the processor, configure the computing
device
to: receive an index comprising index holdings and characteristics;
automatically
project the index from a current time period to a future time period as a
target index
providing a proforma benchmark characterizing a future state of the index
holdings,
the target index projected based on expected future events impacting the
index, the
target index comprising: a list of projected constituents, characteristics
defining each
of the constituents and a weighting of each of the constituents within the
target index;
automatically predict, using a machine learning model, a liquidity score
associated
with each of the constituents, the liquidity score for a particular
constituent based on
prior liquidity scores for other constituents in a past time period with one
or more
similar characteristics to the characteristics of the particular constituent;
and, in
response to the target index and the liquidity score, generate, in real-time
an
optimized portfolio providing an optimized set of constituents with associated
weighting tracking the target index.
[0014] In at least some implementations, there is provided a computer-
implemented
method for automatic generation of a portfolio, the method performed on a
computer
device, the method comprising: receiving an index comprising index holdings
and
characteristics; automatically projecting the index from a current time period
to a
future time period as a target index providing a proforma benchmark
characterizing a
future state of the index holdings, the target index projected based on
expected
future events impacting the index, the target index comprising: a list of
projected
constituents, characteristics defining each of the constituents and a
weighting of each
of the constituents within the target index; automatically predicting, using a
machine
learning model, a liquidity score associated with each of the constituents,
the liquidity
.. score for a particular constituent based on prior liquidity scores for
other constituents
in a past time period with one or more similar characteristics to the
characteristics of
the particular constituent; and in response to the target index and the
liquidity score
considered along with a set of pre-defined constraints for desired portfolio
characteristics, generating, in real-time on a user interface (UI) of the
device, an
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optimized portfolio providing an optimized set of constituents with associated
weighting tracking the target index.
[0015] It is to be understood that both the foregoing description and the
following
detailed description are exemplary and explanatory only and are not
restrictive of the
disclosure. Further, the accompanying drawings, which are incorporated in and
constitute a part of this specification, illustrate aspects of the present
disclosure and
together with the description, serve to explain principles of the disclosed
embodiments as set forth in the accompanying claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] These and other features of the disclosure will become more apparent
from
the following description in which reference is made to the appended drawings
wherein:
[0017] Fig. 1 is a block diagram illustrating an example computing device
communicating in a communication network and configured to output for display
a
recommended optimized portfolio (e.g. a bond portfolio) generated from an
index
being tracked (e.g. a passive fixed income index fund), in accordance with one
or
more aspects of the present disclosure.
[0018] Fig. 2 is a block diagram illustrating the computing device of Fig. 1,
in
accordance with one or more aspects of the present disclosure.
[0019] Fig. 3 is a block diagram illustrating more detail of Fig. 2, including
an
embodiment of a liquidity module, in accordance with one or more aspects of
the
present disclosure.
[0020] Fig. 4 is a flowchart illustrating example operations of a computing
device (e.g.
the computing device of Fig. 1), in accordance with one or more aspects of the
present disclosure.
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[0021] Figs. 5-6 are diagrams illustrating example graphical user interfaces
or
portions thereof, in accordance with one or more aspects of the present
disclosure.
DETAILED DESCRIPTION
[0022] While various embodiments of the disclosure are described below, the
disclosure is not limited to these embodiments, and variations of these
embodiments
may well fall within the scope of the disclosure. Reference will now be made
in detail
to embodiments of the present disclosure, examples of which are illustrated in
the
accompanying drawings. Wherever possible, the same reference numbers will be
used throughout the drawings to refer to the same or like parts.
[0023] Generally, in at least some embodiments, there is provided a computer-
implemented automated real-time portfolio optimization system and method
suitable
for passive fixed income (Fl) funds. Thus, in at least some embodiments, an
accurate and efficient portfolio having constituents and weighting tracking an
index
(e.g. an Fl index fund) is generated on a user interface by utilizing: (a) a
proforma
.. benchmark index of a future state of the index to track and optimize
against and (b)
automatically predicted liquidity levels for each holding in the benchmark
index
forecasted based on machine learning prediction models to characterize an
index
constituent item (e.g. a holding) to a historical one having similar
characteristics.
[0024] Thus, in at least some aspects, the portfolio generated by the
disclosed
.. systems and methods aims to minimize tracking error to the proforma
benchmark
index (e.g. minimize constraints on characteristics of the index) while
applying a
penalty related to the predicted liquidity levels. In at least one aspect, a
predictive
machine learning model (e.g. random forest or neural network) is used to
assign
liquidity levels by classifying individual items such as bonds within the
tracked
portfolio as similar to others based on tracking characteristics. Generally,
there is
also provided a user interface which allows defining portfolio characteristics
of the
optimized portfolio to update, in real-time, the optimized portfolio. In at
least some
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aspects, a user interface additionally allows defining the desired
characteristics of the
portfolio (e.g. limiting the number of holdings, or defining sector
preferences).
[0025] The proposed system and method is further advantageous as in at least
one
aspect, it reduces errors in tracking the index via generating the proforma
benchmark. Additionally, in at least one aspect, the proposed system and
method
reduces wasting computer resources as it provides a plurality of computer
components (e.g. index analytics module, liquidity predictor module, proforma
benchmark generator, portfolio optimizer) which co-operate in real-time to
generate
an optimized portfolio while minimizing constraints on the index
characteristics and
applying a penalty to the predicted liquidity levels. This further improves
the
efficiency and accuracy of the portfolio (e.g. bond portfolio tracking the
fixed income
index fund) generated by the disclosed methods and system.
[0026] Fig. 1 is a diagram illustrating an example computer network 100 in
which a
recommendation computing device 102 is configured to communicate, using a
communication network 104, with one or more other computing devices, including
an
index data server 106, one or more client device(s) 108 and an order
management
server 110. In Fig. 1, client device(s) 108 may be different types of
computing
devices such as a handheld mobile computing device 108A and a mobile computer
108B (collectively shown as 108). Recommendation computing device 102 may be
configured to receive, from an index data server 106 (also may be referred to
as a
data hub), index data 103 characterizing the index to be tracked. The index
data 103
may comprise index holdings and characteristics. For example, the index data
103
may include but not be limited to: prices (e.g. price for each current holding
and/or
expected trade), positions (e.g. current positions), index holdings (e.g.
target
benchmark), trades executed and/or to be settled, client purchases and
redemptions
(CPR) and other characteristics defining the index (e.g. passive fixed income
fund).
[0027] Thus, information about a specific index being tracked is retrieved via
the
index data 103. Based on this, the recommendation computing device 102 is
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configured to generate a recommended portfolio 107 which is dynamically
optimized.
As will be described further with reference to Figs. 2 and 3, such
recommendations
are based on the recommendation computing device 102 analyzing the index data
103 and generating (1) predicted liquidity levels 111 for each of the index
holdings
and (2) target index 113. The target index 113 (also referred to as a proforma
benchmark herein) is a forecasted future state of the index holdings from the
index
data 103 based on known/predicted future events affecting the index.
[0028] Referring to the target index 113 (also referred to as the proforma
benchmark),
the recommendation computing device 102 automatically projects or forecasts
the
index (as retrieved from index data 103) from a current time to a future time
based on
predictable events resulting in index changes. As will be described,
advantageously,
the recommendation computing device 102 generates a recommended portfolio 107
which closely tracks and optimizes against the target index 113 by having a
tracking
error as close to zero as possible given other constraints (e.g. user
preferences or
limitations on number of held in the portfolio).
[0029] Referring to Figs. 1 and 3, the recommendation computing device 102
further
automatically generates predicted liquidity levels 111 from the index data 103
using a
machine learning predictive model (e.g. random forest or neural network).
Notably,
the recommendation computing device 102 (via the liquidity module 214 shown in
Fig. 2) assigns a particular liquidity level 111 for each of the target index
constituents
in the target index 113 based on characteristics of the particular constituent
(e.g.
sector, coupon, time to maturity) and/or historical data.
[0030] As will be described in further detail below, the recommendation
computing
device 102 is configured to utilize the predicted liquidity levels 111 and the
target
index 113 to generate a set of recommended portfolio(s) 107 (e.g. via an
optimizer
module 218 depicted in Fig. 2) having a set of constituents and respective
characteristics (e.g. weighting tracking that of the index data 103).
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[0031] Referring again to Fig. 1, once the recommendation computing device 102
generates the recommended portfolio 107, this may be provided to one or more
client
device 108. The recommended portfolio 107 may for example be displayed on a
browser associated with the client device 108 by accessing a website/software
program associated with the recommendation computing device 102.
Alternatively,
the recommended portfolio 107 may be provided to the client device 108 for
local
display thereon. Upon receiving the recommended portfolio 107 (including an
optimized set of constituents and corresponding weighting), the client device
108
may receive input on a user interface thereon modifying the recommended
portfolio
107 and provide said feedback to the recommendation computing device 102 for
subsequent analysis.
[0032] Furthermore, the client device 108 may receive user input selecting
orders
based on the recommended portfolio 107. Selected orders 109 (e.g. trades
requested for purchasing/selling) are then submitted from the client device
108 to the
order management server 110 which then executes the selected orders 109 (e.g.
on
a central repository containing all of the trades for all client devices 108).
For
example, the order management server 110 is configured to allow an authorized
user
(e.g. of a client device 108) to place and execute orders for trades of
various types of
securities (e.g. stocks, bonds, futures, options, derivatives etc.) in real
time.
[0033] Once the orders execute, the order management server 110 provides the
executed orders 105 to the index data server 103. The index data server 103
contains a data hub repository for storing and managing the state of each
account for
users of client devices 108 such as, for example, the prices, positions,
balances,
rules for each account, index holdings, trade status, etc.
[0034] In the example of Fig. 1, the recommendation computing device 102 is a
distributed computing server communicating on the network 104. Other examples
of
the recommendation computing device 102 is a single computer, cloud computing
services, a laptop computer, a tabletop computer, or another type of computing
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device. In the example of Fig. 1, client device 108 may be a cell phone, a
laptop, an
intermediary third party, etc., each may have different storage capacity,
processing
power, user interfaces (e.g. native application, browser, etc.), communication
capabilities and communication connections with different qualities when
communicating queries (e.g. for requesting an optimized portfolio or
modifications to
the portfolio) and receiving responses from recommendation computing device
102.
[0035] In the example of Fig. 1, the order management server 110 and the index
data
server 106 are distributed servers but they may be any computing devices that
have
at least one processing device (e.g. a processor) and memory (e.g. a storage
device), storing instructions which when executed by the processing device
configure
the computing devices for performing the operation(s) described herein,
including for
example: receiving requests for trade orders; providing responses related to
the
orders; modifying any one of liquidity levels 111, target index 113 and
portfolio 107.
[0036] Recommendation computing device 102 is coupled for communication to
network 104 which may be a wide area network (WAN) such as the Internet.
Network
104 is coupled for communication with index data server 106, client device
108, and
order management server 110. It is understood that network 104 is simplified
for
illustrative purposes. Additional networks may also be coupled to the WAN of
network
104 such as a wireless network and/or a local area network (LAN) between the
WAN
and recommendation computing device 102 or between the WAN and any of devices
shown in Fig. 1.
[0037] Fig. 2 is a diagram illustrating in block schematic form, an example
computing
device (e.g. the recommendation computing device 102 shown in Fig.1), in
accordance with one or more aspects of the present disclosure, for example to
provide a system and method to dynamically generate an optimized trade
portfolio.
Preferably, the optimized trade portfolio generated minimizes a constraint
defined by
the characteristics of the target index tracked and a penalty applied for the
predicted
liquidity levels of the target index.
Date Recue/Date Received 2021-02-25
[0038] Recommendation computing device 102 comprises one or more processors
202, one or more input devices 204, one or more communication units 206 and
one
or more output devices 208. Recommendation computing device 102 also includes
one or more storage devices 210 storing one or more modules such as analytics
module 212 further comprising a liquidity module 214 and a proforma benchmark
module 216, an optimizer module 218, a notification module 220 and a Ul module
222. Communication channels 144 may couple each of the components including
processor(s) 202, input device(s) 204, communication unit(s) 206, output
device(s)
208, storage device(s) 210, analytics module 212, liquidity module 214,
proforma
benchmark module 216, optimizer module 218, notification module 220, Ul module
222 for inter-component communications, whether communicatively, physically
and/or operatively. In some examples, communication channels 144 may include a
system bus, a network connection, an inter-process communication data
structure, or
any other method for communicating data.
[0039] One or more processors 202 may implement functionality and/or execute
instructions within recommendation computing device 102. For example,
processors
202 may be configured to receive instructions and/or data from storage devices
210
to execute the functionality of the modules shown in Fig. 2, among others
(e.g.
operating system, applications, etc.). Recommendation computing device 102 may
store data/information to storage devices 210. Some of the functionality is
described
further herein below.
[0040] One or more communication units 206 may communicate with external
devices (e.g. index data server 106, client devices 108, and order management
server 110) via one or more networks (e.g. communication network 104) by
.. transmitting and/or receiving network signals on the one or more networks.
The
communication units 206 may include various antennae and/or network interface
cards, etc. for wireless and/or wired communications.
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[0041] Input devices 204 and output devices 208 may include any of one or more
buttons, switches, pointing devices, cameras, a keyboard, a microphone, one or
more sensors (e.g. biometric, etc.) a speaker, a bell, one or more lights,
etc. One or
more of same may be coupled via a universal serial bus (USB) or other
communication channel (e.g. 220).
[0042] The one or more storage devices 210 may store instructions and/or data
for
processing during operation of recommendation computing device 102. The one or
more storage devices 210 may take different forms and/or configurations, for
example, as short-term memory or long-term memory. Storage devices 210 may be
configured for short-term storage of information as volatile memory, which
does not
retain stored contents when power is removed. Volatile memory examples include
random access memory (RAM), dynamic random access memory (DRAM), static
random access memory (SRAM), etc. Storage devices 210, in some examples, also
include one or more computer-readable storage media, for example, to store
larger
amounts of information than volatile memory and/or to store such information
for long
term, retaining information when power is removed. Non-volatile memory
examples
include magnetic hard discs, optical discs, floppy discs, flash memories, or
forms of
electrically programmable memory (EPROM) or electrically erasable and
programmable (EEPROM) memory.
[0043] Analytics module 212 may be configured to obtain input from index data
server
106 providing index data 103 which may include: index holdings and
characteristics
of a particular index of interest (e.g. duration, rating, prices, weighting,
sectors, etc.),
and user account historical trading information such as trades, prices,
positions, etc.
[0044] The input data may instruct the performance by the proforma benchmark
module 216 of automatically projecting the index into a future state (e.g. a
future time
period) to define a target index (also referred to as a proforma benchmark)
based on
expected events affecting the index. Such events affecting the index may
include for
example, coupon payments, re-openings and new issues, index extensions and
roll-
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outs with various amortization periods which may affect the index holdings
and/or
characteristics. Since the index to be replicated (or closely tracked) as
provided by
the index data 103 is dynamically and constantly changing based on events
affecting
its component individual indices, the liquidity module 214 aims to pre-
position the
portfolio to the forecasted changes, in order to reduce the tracking error.
Thus, the
generated target index 113 (providing a proforma benchmark) is computed
automatically to account for the various changes expected.
[0045] For example, in order to generate the target index 113, the proforma
benchmark module 216 may retrieve and access a storage of expected events
(e.g.
stored on storage device 210) that are relevant to each of the index holdings
in the
index data 103. The proforma benchmark module 216 may then apply the expected
events (e.g. planned to occur between a current time and a pre-defined future
time
for when the portfolio recommendations 107 are being generated) to each of the
relevant indices in the index data 103 to project the impact on the index data
103.
Notably, the proforma benchmark module 216 projects predictable index changes
onto the index data 103 and generates the target index 113 as proforma
benchmarks. The target index 113 may include for example a passive fixed
income
index. The target index 113 comprises: a projected list of index constituents
(e.g.
bonds), their characteristics (e.g. sector, coupon, time to maturity,
duration, rating,
and price) and the relevant weighting within the index. In at least some
examples,
the pre-defined future time may be pre-defined based on historical data and/or
user-
defined via the computing systems described herein.
[0046] In one example, the proforma benchmark module 216 displays the target
index 113 on a user interface 312 of the recommendation computing device 102
(or
alternatively accessible via devices 108) using the Ul module 222 to
illustrate how the
index will appear at a given future time and/or allow modifications to the
target index
113.
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[0047] In at least one aspect, as shown in Fig. 5, Ul module 222 may present a
graphical view 500 of the target index 113 including the projected
constituents of the
target index 502 and the corresponding characteristics of the target index
504. The
graphical view 500 and other such graphical views depicting the target index
113,
index data 103 or the recommended portfolio 107 may be presented via a user
interface of the recommendation computing device 102 using the Ul module 222
depicted in Fig. 2. The characteristics of the target index 504 may include
but not
limited to: a security descriptor, a unique identifier, a transaction type, an
effective
date, a par amount such as face value of bond, % of outstanding, an
amortization
.. method, and amortization period (days). Additionally, as shown in the view
500, a
plurality of graphical user interface (GUI) elements 506 may also be presented
on the
user interface view 500 associated with the recommendation computing device
102
for manipulating the target index including, the projected constituents of the
target
index 502 and/or characteristics of the target index 504. Any modifications or
input
received via the GUI elements 506 to update the target index items or their
characteristics may be used to update user preferences (e.g. see user
preferences
224 in Fig. 2) so that subsequent iterations of forecasting the target index
(e.g. via a
proforma benchmark module 216) take into account the user preferences 224
(e.g.
previously defined or defined via the Ul module 222). Additionally, in at
least one
aspect, in response to receiving an override request to the projection of the
index
from a present state to the future state as the target index (e.g. via the GUI
elements
506), the Ul module 222 may be configured to communicate with the notification
module 220 to generate an electronic report (e.g. email notification) to at
least one
user associated with receiving the optimized portfolio 107.
[0048] Referring again to Fig. 2, upon receipt of the index data 103, the
proforma
benchmark module 216 is configured to automatically predict, using a machine
learning model, a liquidity level 111 (also referred to as a liquidity score)
for each of
the constituents of the target index 113. In at least one aspect, the
liquidity level 111
for a particular constituent is based on historical liquidity scores for other
constituents
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in a past time period having one or more similar characteristics to the
characteristics
of the particular constituent.
[0049] Referring to Fig. 3, shown is an embodiment of the liquidity module 214
of Fig.
2. In one aspect of the liquidity module 214, a liquidity level 111 for an
index
constituent (e.g. a particular security holding in the target index) may be
calculated by
classifying the index constituent as similar to other index constituent(s)
based on its
characteristics (e.g. a bond having a duration, a rating, and/or a price as a
bond with
a historical liquidity value attributed thereto) and thereby apply a same
liquidity level
111. The liquidity module 214 may determine similarity based on analyzing that
there
is a degree of overlap between one or more characteristics between the index
constituents.
[0050] Predicting liquidity levels is one of the biggest challenges in fixed
income
portfolios and is typically manually estimated which can lead to significant
errors, as it
is impossible to characterize a large number of similar characteristics. In at
least
some aspects, the present system and method streamlines the process to
accurately
determine the liquidity levels 111 in real-time by applying machine learning
models as
described herein.
[0051] Liquidity module 214 performs two operations: training via training
module 302
and execution via liquidity execution module 310.
[0052] Training module 302 generates a trained process 308 for use by the
liquidity
execution module 310 to predict liquidity levels 111. Training module 302
comprises
training data 304 and machine learning algorithm 306. Training data 304 is a
database of historical index data 301 (index holdings, index constituent
characteristics including overall duration and sector duration, rating, price,
sector
weights, sector, coupon, time to maturity, etc.) and respective liquidity
levels 111 for
individual index items (e.g. bonds) in the historical index data 301. Machine
learning
algorithm 306 may be a regression or a classification method and attempts to
find an
optimal trained process 308. The trained process 308 automatically maps each
Date Recue/Date Received 2021-02-25
index item in the historical index data 301 to a set of processes that would
generate
an optimal measure of liquidity. This training may include executing, by the
training
module 302, a machine learning algorithm 306 to determine a set of model
parameters based on the training set, including historical index data 301.
[0053] In one aspect, the machine learning algorithm 306 is a regression
method
using one or more of linear regression, logistic regression, and polynomial
regression. In another aspect, the machine learning algorithm 306 is a
supervised
classification using one or more of neural network, support vector machine,
linear or
quadratic discriminate analysis, and boosting. In another aspect, the machine
learning algorithm 306 is an unsupervised classification using one or more of
k-
means (clustering), Gaussian mixture models, and principal or independent
component analysis.
[0054] Additionally or alternatively, the machine learning algorithm 306 may
include a
decision tree algorithm, which may include a tree ensemble algorithm (e.g.,
generated using bagging and/or boosting), a random forest algorithm, or a
boosted
trees algorithm.
[0055] Liquidity execution module 310 uses the trained process 308 to find an
optimal
relationship between the target index 113 constituents (e.g. bonds) and
associated
predicted liquidity levels 111. In one aspect, the machine learning model thus
predicts the liquidity level 111 for a particular index item in the target
index 113 based
on determining that there is a degree of overlap of similar characteristics
(e.g. sector,
coupon, time to maturity, etc.) between the particular index item and one or
more
other index items defined in the historical index data 301.
[0056] The liquidity module 214 may use one or more hyperparameters 303 to
tune
the machine learning model generated in the trained process 308. A
hyperparameter
may include a structural parameter that controls execution of the machine
learning
algorithm 306, such as a constraint applied to the machine learning algorithm
306.
Unlike a model parameter, a hyperparameter 303 is not learned from data input
into
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the model. Example hyperparameters 303 for a decision tree algorithm include a
tree
ensemble technique to be applied (e.g., bagging, boosting, a random forest
algorithm, and/or a boosted trees algorithm), a number of features to
evaluate, a
number of observations to use, a maximum depth of each decision tree (e.g., a
number of branches permitted for the decision tree), or a number of decision
trees to
include in a random forest algorithm. Preferably, the hyperparameters 303
define
that the decision tree algorithm applied by the machine learning algorithm 306
is a
random forest algorithm. In one aspect, the hyperparameters 303 may be defined
via
user interface 312 or previously defined.
[0057] In one embodiment, the liquidity levels 111 generated may also be
presented
on user interface 312 of recommendation computing device 102, to allow user
input
to override liquidity score for a particular constituent of the target index
113.
Additionally or alternatively, the user interface 312 may receive input to
override
index constituent classifications performed by the machine learning algorithm
306
(e.g. override whether two index constituents are similar) thereby causing an
automatic retraining of the machine learning algorithm 306 based on the manual
override input.
[0058] In some implementations, machine learning algorithm's fit metrics are
calculated such as cross validation score and other such error metrics.
Notably, the
liquidity module 214 may perform cross-validation when training machine
learning
algorithm 306. Cross validation can be used to obtain a reliable estimate of
machine
learning model performance by testing a machine learning algorithm 306 ability
to
predict new data that was not used in estimating it. The liquidity module 214
may
combine the cross-validation scores for each training procedure to generate an
overall cross-validation score for the machine learning model used by the
machine
learning algorithm 306. The overall cross-validation score may include, for
example,
an average cross-validation score (e.g. across all training procedures), a
standard
deviation across cross-validation scores, or a standard error across cross-
validation
scores. In some aspects, the liquidity module 214 may compare the performance
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scores for each machine learning model implemented by the machine learning
algorithm (e.g. where a plurality of different machine learning models have
been
used), and may select the machine learning model with the best (e.g., highest
accuracy, lowest error, or closest to a desired threshold) performance score
as the
trained process 308.
[0059] Referring again to Figs. 2 and 3, optimizer module 218 receives
liquidity levels
111 (as generated via liquidity module 214) and target index 113 (e.g.
proforma
benchmarks generated via proforma benchmark module 216) and performs
optimization to generate optimized portfolio 107. The optimizer module 218
determines, in real-time, an optimized portfolio 107 comprising a set of
optimized
index constituents, characteristics for each constituent and weighting. In at
least
some implementations, this process advantageously minimizes tracking error to
the
target index 113 by having a constraint defined by the characteristics and a
penalty
applied related to the liquidity levels 111.
.. [0060] In at least some implementations, the optimized portfolio 107
generated may
be further customized via user preferences 224 input on user interface 312.
User
preferences 224 may include desired portfolio characteristics (e.g. a set of
pre-
defined constraints for generating the portfolio 107). Upon receipt of the pre-
defined
constraints input on user interface 312 associated with a particular account
or user
profile, the optimizer module 218 further updates the optimized portfolio 107
to
account for the pre-defined constraints.
[0061] In at least some implementations, the optimizer module 218 runs using a
distributed computing cluster such as to enable real-time generation of
multiple
optimized portfolios for a plurality of different users/accounts.
[0062] Referring to Figs. 2 and 6, the optimized portfolio 107 may be
displayed on the
user interface 312 and/or other user interfaces associated with client devices
108 via
the Ul module 222. The user interface 312 allows users (e.g. portfolio
managers) to
review the trades in real-time and make adjustments where needed. In one
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implementation, the Ul module 222 may receive input to modify the set of
constituents provided as recommendations in the form of the optimized
portfolio 107.
Advantageously, this allows users (e.g. the portfolio managers) to
interactively tweak
the optimized portfolio 107 in real-time. Suggested changes may be propagated
to
other user devices related to the portfolio (e.g. other client devices 108)
for additional
approval (e.g. a PM manager). Once approved, in response to the input to
modify
the recommended optimized portfolio 107 (e.g. to remove a constituent, add a
constituent, or modify characteristics of a constituent suggested) the
optimizer
module 218 may log user preferences 224 (e.g. via Ul module 222). These
modifications may subsequently be used modify the operations of the liquidity
module
214, proforma benchmark module 216 and/or optimizer module 218 to update
subsequent recommendations generated based on corresponding characteristics
for
the modified optimized set of constituents (e.g. the modified optimized
portfolio).
[0063] Fig. 6 shows a view 600 of a graphical user interface (GUI) provided by
the Ul
module 222 displaying the optimized portfolio 107 (e.g. as generated via the
optimizer module 218) including a plurality of interactive GUI elements 602
allowing
modification of the projected index constituents and/or characteristics. In at
least
some implementations, the GUI may receive input via GUI elements 602 for
modifying underlying values used by the liquidity module 214, proforma
benchmark
module 216 and/or optimizer module 218. For example, the request to modify a
portfolio profile of the optimized portfolio 107 displayed (e.g. request for
modifying the
constraint function used by the optimizer module 218 by modifying the
portfolio
characteristics or the penalty function for the portfolio) to trigger
generating an
updated optimized portfolio based on the request for modification.
[0064] Fig. 4 is a flowchart illustrating example operations 400 which may be
performed by a computing device, such as the recommendation computing device
102 illustrated in Fig. 1, in accordance with at least one aspect of the
present
disclosure.
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[0065] Operations 400 receive index information (e.g. for a passive fixed
income
index) to be replicated for generating an optimized portfolio which tracks the
index
information as described with reference to Fig. 1. Additionally, the
operations project
predictable index changes to generate proforma benchmarks, predict liquidity
levels
of index items including securities and new issues and thereby generate the
optimized portfolio. Additionally, operations 400 train a machine learning
model using
historical liquidity levels for known index holdings to determine current
liquidity levels
for index items forecasted to a future time as needed by the optimized
portfolio.
[0066] At 402, operations of the computing device receive an index (e.g. a
passive
fixed income index) comprising index holdings (e.g. bonds) and index
characteristics
(e.g. sector weights, overall duration, sector duration, rating weights,
etc.).
Additionally, in at least some aspects, operations of the recommendation
computing
device 102 may retrieve from an associated storage, pre-defined desired
portfolio
characteristics, which define a profile for the portfolio (e.g. preferred
types of index
items, number of securities, preferred sector weights, etc.).
[0067] At 404, operations automatically project the index from a current time
period to
a future time period as a target index (also referred to as a proforma
benchmark).
The projected target index characterizes a future state of the index holdings
(and
corresponding characteristics) based on expected events affecting the index
until the
future time period. The expected events can include for example, index
extensions
and roll-outs, coupon payments affecting indices, re-opening and new issues
affecting indices, and/or manual input of additional events or override of
expected
events provided via a user interface of the computing device. Based on the
projected
impact of the expected events, operations 404 define the target index
comprising: a
list of projected constituents (e.g. bonds), characteristics defining each of
the
constituents (e.g. characteristics taking into account the expected upcoming
events)
and a weighting of each of the constituents within the target index. The
weighting
may be defined for example based on the original index sector weighting for
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Date Recue/Date Received 2021-02-25
individual index item and/or expected impact of future events on the weighting
and/or
pre-defined portfolio characteristics defined for a particular portfolio.
[0068] At 406, operations automatically predict, using a machine learning
model, a
liquidity score associated with each of the projected constituents of the
target index.
The liquidity score for a particular constituent (e.g. projected bond) based
on prior
historical liquidity levels for other constituents in a past time period with
one or more
similar characteristics to the characteristics of the particular constituent.
Thus, for
example, the operations may allow ranking a particular forecasted index item
(e.g. a
bond) liquidity based on its characteristics, such as sector, coupon, time to
maturity,
.. etc. Also, in some implementations, the machine learning model's fit
metrics are
further computed such as the cross validation score and other error metrics
for
selecting and validating the machine learning model. In some aspects of
operation
406, a user interface of the recommendation computing device 102 may generate
a
diagnostic report which displays the liquidity score as well as predictions
made by the
.. recommendation computing device 102 for generating the liquidity score such
as to
allow overrides for incorrect predictions and retraining the machine learning
model
therefrom.
[0069] At 408, operations of the recommendation computing device 102,
generate, in
real-time, an optimized portfolio based on the target index and the liquidity
score for
the target index. The optimized portfolio provides an optimized set of
constituents
(e.g. based on constituents from the target index, further refined by
limitations on the
number of constituents and liquidity levels predicted such as to optimize
liquidity
values overall). Additionally, the associated weighting in the optimized set
of
constituents closely tracks the representation in the target index.
.. [0070] In one or more examples, the functions described may be implemented
in
hardware, software, firmware, or any combination thereof. If implemented in
software,
the functions may be stored on or transmitted over, as one or more
instructions or
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code, a computer-readable medium and executed by a hardware-based processing
unit.
[0071] Computer-readable media may include computer-readable storage media,
which corresponds to a tangible medium such as data storage media, or
communication media including any medium that facilitates transfer of a
computer
program from one place to another, e.g., according to a communication
protocol. In
this manner, computer-readable media generally may correspond to (1) tangible
computer-readable storage media, which is non- transitory or (2) a
communication
medium such as a signal or carrier wave. Data storage media may be any
available
media that can be accessed by one or more computers or one or more processors
to
retrieve instructions, code and/or data structures for implementation of the
techniques
described in this disclosure. A computer program product may include a
computer-
readable medium. By way of example, and not limitation, such computer-readable
storage media can comprise RAM, ROM, EEPROM, optical disk storage, magnetic
disk storage, or other magnetic storage devices, flash memory, or any other
medium
that can be used to store desired program code in the form of instructions or
data
structures and that can be accessed by a computer. Also, any connection is
properly
termed a computer-readable medium. For example, if instructions are
transmitted
from a website, server, or other remote source using wired or wireless
technologies,
such are included in the definition of medium. It should be understood,
however, that
computer-readable storage media and data storage media do not include
connections, carrier waves, signals, or other transient media, but are instead
directed
to non-transient, tangible storage media.
[0072] Instructions may be executed by one or more processors, such as one or
more general purpose microprocessors, application specific integrated circuits
(ASICs), field programmable logic arrays (FPGAs), digital signal processors
(DSPs),
or other similar integrated or discrete logic circuitry. The term "processor,"
as used
herein may refer to any of the foregoing examples or any other suitable
structure to
implement the described techniques. In addition, in some aspects, the
functionality
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described may be provided within dedicated software modules and/or hardware.
Also, the techniques could be fully implemented in one or more circuits or
logic
elements. The techniques of this disclosure may be implemented in a wide
variety of
devices or apparatuses, an integrated circuit (IC) or a set of ICs (e.g., a
chip set).
[0073] One or more currently preferred embodiments have been described by way
of
example. It will be apparent to persons skilled in the art that a number of
variations
and modifications can be made without departing from the scope of the
disclosure as
defined in the claims.
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