U.S. patent application number 16/582453 was filed with the patent office on 2020-01-16 for climate data processing and impact prediction systems.
The applicant listed for this patent is BDC II, LLC dba Entelligent, SAND Investment Holdings, LLC. Invention is credited to Pooja Khosla, Mark Labovitz, David Schimel, Thomas Stoner, JR..
Application Number | 20200020036 16/582453 |
Document ID | / |
Family ID | 65435355 |
Filed Date | 2020-01-16 |
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United States Patent
Application |
20200020036 |
Kind Code |
A1 |
Stoner, JR.; Thomas ; et
al. |
January 16, 2020 |
CLIMATE DATA PROCESSING AND IMPACT PREDICTION SYSTEMS
Abstract
The present disclosure generally relates to assessing climate
change risk at a security level. A computing system receives a
selection of a climate change scenario from a user operating a
remote client device. The computing system generates one or more
environmental metrics for one or more energy sources based on the
scenario selected by the user. The computing system converts the
one or more environmental metrics for the one or more energy
sources into one or more profitability indicators. The computing
system correlates at least one energy source of the one or more
energy sources to each security of the one or more securities. The
computing system generates a projected climate change risk for each
security based on the one or more environmental metrics for the one
or more energy sources. The computing system provides a data set
representing the projected climate change risk to the remote client
device.
Inventors: |
Stoner, JR.; Thomas;
(Boulder, CO) ; Khosla; Pooja; (Longmont, CO)
; Schimel; David; (Boulder, CO) ; Labovitz;
Mark; (Superior, CO) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
BDC II, LLC dba Entelligent
SAND Investment Holdings, LLC |
Boulder
Boulder |
CO
CO |
US
US |
|
|
Family ID: |
65435355 |
Appl. No.: |
16/582453 |
Filed: |
September 25, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16107702 |
Aug 21, 2018 |
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16582453 |
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62548509 |
Aug 22, 2017 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 50/06 20130101;
G06Q 40/06 20130101 |
International
Class: |
G06Q 40/06 20060101
G06Q040/06; G06Q 50/06 20060101 G06Q050/06 |
Claims
1. A system, comprising: a processor; and a memory having
programming instructions stored thereon, which, when executed by
the processor, causes the processor to perform an operation,
comprising: receiving, via at least one network, a selection of a
climate change scenario from a user operating a remote client
device; generating one or more environmental metrics for one or
more energy sources based on the climate change scenario selected
by the user; converting the one or more environmental metrics for
the one or more energy sources into one or more profitability
indicators; retrieving, over the at least one network, one or more
sets of financial information directed to one or more securities
from one or more remote financial data sources; correlating at
least one energy source of the one or more energy sources to each
security of the one or more securities, by downward deploying the
one or more profitability indicators of the one or more energy
sources to the one or more sets of financial information of each
security; generating a projected climate change risk for each
security of the one or more securities based on the one or more
environmental metrics for the one or more energy sources; and
providing a data set representing the projected climate change risk
to the remote client device.
2. The system of claim 1, wherein the processor is configured to
generate the one or more environmental metrics for the one or more
energy sources based on the scenario selected by the user, by:
simulating a climate change impact of each energy source using one
or more metrics associated with the selected climate change
scenario.
3. The system of claim 1, wherein the one or more profitability
indicators include at least one of an absolute annualized capital
cost, an absolute variable cost, an absolute revenue, a gross
margin, a unit gross margin, a net present value (NPV), an internal
rate of return (IRR), and a return on investment (ROI).
4. The system of claim 1, wherein the processor is configured to
correlate the at least one energy source of the one or more energy
sources to each security of the one or more securities, by: mapping
the at least one energy source with at least one financial
subsector; and ranking a relevance of each energy source to the at
least one financial subsector.
5. The system of claim 4, wherein the processor is further
configured to: identify the one or more securities associated with
each financial subsector; correlate each energy source mapped to
each financial subsector with the identified one or more securities
associated with each financial subsector; and rank a relevance of
each energy source with each of the identified one or more
securities associated with each financial subsector.
6. The system of claim 1, wherein the processor is configured to
generate the projected climate change risk for each security based
on the one or more environmental metrics for one or more energy
sources, by: retrieving historical price returns for each security
from the one or more financial data sources.
7. The system of claim 6, wherein the processor is further
configured to: identify the one or more environmental metrics
associated with each respective energy source; and generate a
future environmental impact for each security using a linear
regression algorithm with the identified one or more environmental
metrics and the retrieved historical prices returns of each
security.
8. A computer-implemented method of assessing climate change risk
at a security level, comprising: receiving, by a computing system
comprising a processor and a memory, the memory comprising a
climate data analytics module executable by the processor, via at
least one network, a selection of a climate change scenario from a
user operating a remote client device; generating, by the processor
of computing system, one or more environmental metrics for one or
more energy sources based on the climate change scenario selected
by the user; converting, by the processor of the computing system,
the one or more environmental metrics for the one or more energy
sources into one or more profitability indicators; retrieving, by
the processor of the computing system, over the at least one
network, one or more sets of financial information directed to one
or more securities from one or more remote financial data sources;
correlating, by the processor of the computing system, at least one
energy source of the one or more energy sources to each security of
the one or more securities, by downward deploying the one or more
profitability indicators of the one or more energy sources to the
one or more sets of financial information of each security;
generating, by the processor of the computing system, a projected
climate change risk for each security of the one or more securities
based on the one or more environmental metrics for the one or more
energy sources; and providing, by the processor of the computing
system, a data set representing the projected climate change risk
to the remote client device.
9. The computer-implemented method of claim 8, wherein the
generating of the one or more environmental metrics for the one or
more energy sources based on the scenario selected by the user
further comprises: simulating a climate change impact of each
energy source using one or more metrics associated with the
selected climate change scenario.
10. The computer-implemented method of claim 8, wherein the one or
more profitability indicators include at least one of an absolute
annualized capital cost, an absolute variable cost, an absolute
revenue, a gross margin, a unit gross margin, a net present value
(NPV), an internal rate of return (IRR), and a return on investment
(ROI).
11. The computer-implemented method of claim 8, wherein the
correlating of the at least one energy source of the one or more
energy sources to each security of the one or more securities
further comprises: mapping the at least one energy source with at
least one financial subsector; and ranking a relevance of each
energy source to the at least one financial subsector.
12. The computer-implemented method of claim 11, the method further
comprising: identifying the one or more securities associated with
each financial subsector; correlating each energy source mapped to
each financial subsector with the identified one or more securities
associated with each financial subsector; and ranking a relevance
of each energy source with each of the identified one or more
securities associated with each financial subsector.
13. The computer-implemented method of claim 8, wherein the
generating of the projected climate change risk for each security
based on the one or more environmental metrics for one or more
energy sources further comprises: retrieving historical price
returns for each security from the one or more financial data
sources.
14. The computer-implemented method of claim 13, the method further
comprising: identifying the one or more environmental metrics
associated with each respective energy source; and generating a
future environmental impact for each security using a linear
regression algorithm with the identified one or more environmental
metrics and the retrieved historical prices returns of each
security.
15. A non-transitory computer readable medium having instructions
stored thereon, which, when executed by a processor, cause the
processor to perform an operation of assessing climate change risk
at a security level, the operation comprising: receiving, via at
least one network, a selection of a climate change scenario from a
user operating a remote client device; generating one or more
environmental metrics for one or more energy sources based on the
climate change scenario selected by the user; converting the one or
more environmental metrics for the one or more energy sources into
one or more profitability indicators; retrieving, over the at least
one network, one or more sets of financial information directed to
one or more securities from one or more remote financial data
sources; correlating at least one energy source of the one or more
energy sources to each security of the one or more securities, by
downward deploying the one or more profitability indicators of the
one or more energy sources to the one or more sets of financial
information of each security; generating a projected climate change
risk for each security of the one or more securities based on the
one or more environmental metrics for the one or more energy
sources; and providing a data set representing the projected
climate change risk to the remote client device.
16. The non-transitory computer readable medium of claim 15,
wherein the generating of the one or more environmental metrics for
the one or more energy sources based on the scenario selected by
the user further comprises: simulating a climate change impact of
each energy source using one or more metrics associated with the
selected climate change scenario.
17. The non-transitory computer readable medium of claim 15,
wherein the one or more profitability indicators include at least
one of an absolute annualized capital cost, an absolute variable
cost, an absolute revenue, gross margin, a unit gross margin, a net
present value (NPV), an internal rate of return (IRR), and a return
on investment (ROI).
18. The non-transitory computer readable medium of claim 15,
wherein the correlating of the at least one energy source of the
one or more energy sources to each security of the one or more
securities further comprises: mapping the at least one energy
source with at least one financial subsector; and ranking a
relevance of each energy source to the at least one financial
subsector.
19. The non-transitory computer readable medium of claim 18,
wherein the operation further comprises: identifying the one or
more securities associated with each financial subsector;
correlating each energy source mapped to each financial subsector
with the identified one or more securities associated with each
financial subsector; and ranking a relevance of each energy source
with each of the identified one or more securities associated with
each financial subsector.
20. The non-transitory computer readable medium of claim 15,
wherein the generating of the projected climate change risk for
each security based on the one or more environmental metrics for
one or more energy sources further comprises: retrieving historical
price returns for each security from the one or more financial data
sources; identifying the one or more environmental metrics
associated with each respective energy source; and generating a
future environmental impact for each security using a linear
regression algorithm with the identified one or more environmental
metrics and the retrieved historical prices returns of each
security.
Description
TECHNICAL FIELD
[0001] The present disclosure relates to systems and methods for
climate data processing and impact prediction, including, for
example, financial impact and performance prediction, and more
specifically to assessing climate change risk at a security
level.
BACKGROUND
[0002] Uncertainty and unpredictability within environmental
phenomenon, social phenomenon, and governance standards makes it
difficult to understand their emerging trends, as well as the
impact that the trends may have upon investments.
[0003] Nevertheless, there is a recent movement towards
environmental, social, and governance standards (ESG) investing;
investing that focuses on environmental, social and governance
standards used by socially responsible investors in screening
investments. An environmental aspect examines a company's
performance regarding the environment. A social aspect examines a
company's products and services and relationships with employees,
customers and the community. A governance aspect examines a
company's leadership, internal controls and shareholder rights. The
recent movement and growth towards ESG investing is evidenced by
the increase in the subsection of ESG focused exchange traded funds
(ETFs).
[0004] Despite the interest in ESG investing, much of the data used
to make decisions based on environmental, social and governance
standards of a company is unreliable, as it is self-reported, and
often non-quantifiable. For example, hundreds of firms are
dedicated to analyzing and rating company ESG performance. However
it is unclear what mechanism the firms utilize to analyze and rate
company ESG performance. Furthermore, a majority of the ESG data
used by firms is binary (yes/no) answers to questions related to
corporate policies. The ESG data is compiled from self-reported
information and so there is a lack of quantifiable metrics and the
ability to compare between different companies. Accordingly, there
exists a need for quantifiable metrics related to ESG
investing.
[0005] Moreover, the environmental aspect of ESG investing has
added uncertainty and unpredictability due to a changing climate.
The impacts of climate change are far reaching and vast. Climate
change has been associated with a rise in global sea levels,
melting ice, thermal expansion (the warming of ocean water).
Additionally, the rise in global sea levels, increase in melting
ice and thermal expansion may interact with cyclical phenomenon
such as El Nino and La Nina, thus compounding the volatility of
local environment and global climate. Climate change creates new
uncertainties for investors as rising global temperatures and sea
levels may make weather patterns more difficult to predict.
Additionally the global regulatory response to climate change adds
uncertainty to the performance of investments. Moreover climate
change may also increase the risk of modeling error to account for
extreme weather risks. Accordingly, there remains a need for a way
to better process climate data to understand climate phenomenon and
the impact of climate phenomenon on investments.
[0006] Additionally, the changing environmental conditions
contribute to stock market volatility as the changing environmental
conditions may impact, for example, the oil prices that are central
to the stock market. Accordingly there is a need for realistic
valuations of both energy and non-energy companies in view of a
changing climate and local environment and providing a quantitative
evaluation of companies most likely to benefit or suffer from
climate change. There is a need for systems and methods that are
able to relate climate data to economic data and quantifiably
measure the impact of climate change on economic investment.
SUMMARY
[0007] Embodiments disclosed herein generally relate to a system,
method, and non-transitory computer readable medium for assessing
climate change risk at a security level. A computing system
receives a selection of a climate change scenario from a user
operating a remote client device. The computing system generates
one or more environmental metrics for one or more energy sources
based on the climate change scenario selected by the user. The
computing system converts the one or more environmental metrics for
the one or more energy sources into one or more profitability
indicators. The computing system retrieves one or more sets of
financial information directed to one or more securities from one
or more remote financial data sources. The computing system
correlates at least one energy source of the one or more energy
sources to each security of the one or more securities, by downward
deploying the one or more profitability indicators of the one or
more energy sources to the one or more sets of financial
information of each security. The computing system generates a
projected climate change risk for each security of the one or more
securities based on the one or more environmental metrics for the
one or more energy sources. The computing system provides a data
set representing the projected climate change risk to the remote
client device.
[0008] In one embodiment, the system may generate public equity
indices and portfolio analysis tools. In one embodiment, the
generated public equity indices may be benchmarked to one or more
public indices such as the S&P 500 or the Russell 1000. In one
embodiment, the system may uniquely leverage machine learning to
neutralize climate change risk and integrate energy economics and
financial analysis to optimize portfolio performance.
[0009] In one embodiment, the system may focus on impacts of
climate change on equity portfolios using machine learning and
artificial intelligence to model climate change related risks. The
system may assist in determining changes in future energy subsector
investments all the while considering capital expenditures, cost of
infrastructure investments, and carbon emissions.
[0010] In one embodiment, the system may forecast the global usage
and prices of various energy sources including for example, oil,
coal, renewables (including, for example, solar and wind), nuclear,
natural gas, and the like. The system may then analyze the effect
of the global usage and prices of energy sources on the
profitability of all industries and individual companies within the
industry. In one embodiment, the predicted energy source behavior
and profitability of industries and individual companies may be
used to optimize portfolios for climate risk.
[0011] In one embodiment, the system may utilize a climate model, a
global energy model and a dynamic system of equations. The
exemplary system may allow multiple scenario generation and testing
capacity, flexibility to generate customizable scenarios, the
ability to align portfolios to low carbon scenarios, forecasted
energy usage by type and price, adjustments for technology
advancements and breakthroughs, aggregate level scenario
sensitivities (by subsector and region), may calculate scenario
sensitivities at individual security levels, and may provide the
ability to optimize any broad-based market index.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] Many aspects of the present embodiments may be better
understood with reference to the following drawings. The components
in the drawings are not necessarily to scale, emphasis instead
being placed upon illustrating example embodiments:
[0013] FIG. 1 is a functional block diagram illustrating a
computing environment for climate data processing and impact
predication, according to one exemplary embodiment.
[0014] FIG. 2 is a flow diagram illustrating a method of climate
data processing and impact prediction, according to one exemplary
embodiment.
[0015] FIG. 3 is a flow diagram illustrating a method of one or
more operations included in a first phase of the method discussed
in FIG. 2, according to one exemplary embodiment.
[0016] FIG. 4 is a flow diagram illustrating a method of one or
more operations included in a second phase of the method discussed
in FIG. 2, according to one exemplary embodiment.
[0017] FIG. 5 is a flow diagram illustrating a method of one or
more operations included in a third phase of the method discussed
in FIG. 2, according to one exemplary embodiment.
[0018] FIG. 6 is a flow diagram illustrating a method of one or
more operations included in a fourth phase of the method discussed
in FIG. 2, according to one exemplary embodiment.
[0019] FIG. 7 is a functional block diagram illustrating a
computing environment, according to one exemplary embodiment.
[0020] FIG. 8 is a screen shot illustrating an example graphical
user interface, according to one exemplary embodiment.
[0021] FIG. 9 is a screen shot illustrating an example graphical
user interface, according to one exemplary embodiment.
[0022] FIG. 10 is a screen shot illustrating an example graphical
user interface, according to one exemplary embodiment.
[0023] FIGS. 11A and 11B are screen shots illustrating an example
graphical user interface, according to one exemplary
embodiment.
[0024] FIG. 12 is a screen shot illustrating an example graphical
user interface, according to one exemplary embodiment.
[0025] FIG. 13 is a screen shot illustrating an example graphical
user interface, according to one exemplary embodiment.
[0026] FIG. 14 is a screen shot illustrating an example graphical
user interface, according to one exemplary embodiment.
DETAILED DESCRIPTION
[0027] As discussed above, the impacts of climate change may alter
the behavior of energy sources and impact the profitability of
industries and individual companies. Thus, in order to allocate
investments within investment portfolios that mitigate for the
short-term and long-term impacts of climate change, it is
beneficial to quantitatively understand the impact of temperature
changes, greenhouse gas emissions, and carbon emissions on
investments.
[0028] One or more techniques disclosed herein generally relate to
a system and method of climate data processing and impact
prediction, including, for example, financial impact and
performance prediction. More specifically, the one or more
techniques disclosed herein use climate data processing and impact
prediction to assess climate change risk at a security level.
Conventional approaches to climate data processing and impact
predicting rely on a "bottom-up" approach. In other words,
conventional approaches begin at a company (or security) level, and
rely on self-reported information to perform data processing. These
conventional techniques typically result in a lack of quantifiable
metrics and an inability to compare climate data processing and
impact prediction across two or more companies.
[0029] The systems and techniques of the present disclosure
eliminates the conventional "bottom-up" approach. Instead,
systems/techniques of the present disclosure implement a "top-down"
approach. The "top-down" approach includes the downward correlation
between one or more energy sources and one or more companies. In
particular, the one or more techniques discussed herein begin at an
energy source level, by treating each energy source as its own
entity. Climate data may then be generated for each energy source
based on one or more scenarios selected by an end user. The claimed
system then converts the generated climate data for each energy
source into one or more profitability indicators. The climate data
to financial data conversion allows the system to downward
correlate each energy source to a respective industry subsector, by
using historical price returns of each industry subsector. Within
each industry subsector, the present system can identify one or
more companies to which each energy source maps. From this
information, the present system is able to predict future returns
for each company and use the predicted future returns to assess
climate change risk for the company. In other words, the downward
correlation or downward deployment may be thought of as a
translating of profitability and risk signals associated with each
energy source (a first level) to one or more securities and
portfolios (a second level).
[0030] In some embodiments, climate data may be generated for one
or more policies and/or scenarios specified by a user. For example,
climate data may be generated for each specific policy,
socio-economic scenario, and/or macro-economic scenario specified
by the user. For each policy/scenario, an energy mix may be
determined. From the energy mix, the system may generate one or
more metrics, such as, but not limited to, energy supply, energy
demand, energy prices, energy costs, and the like. The one or more
metrics may be used for further interpolation of the financial
data/profitability indicators at an energy source level.
[0031] Accordingly, the one or more techniques discussed herein
eliminates any dependency on companies to self-report or provide
ESG information.
[0032] Additionally, such evaluation of climate risk assessment
based on global climate inputs performed using the one or more
techniques disclosed herein improves upon conventional systems, by
providing a climate forecast based on profitability indicators
under one or more scenarios selectable by an end user. Such
assessment may provide investors with valuable information, such as
the climate risk associated with such investment, under various
scenarios. Such an evaluation of climate risk assessment, contrary
to conventional systems and techniques, is predictive (i.e.,
forward-looking), is able to provide end users (e.g., investors)
risk information that a security (or an investment) may be
susceptible to with respect to climate change over time, and
according to a determination that is based on exposure to one or
more energy sources (as opposed to at a company level-via the
bottom up approach).
[0033] FIG. 1 is a functional block diagram illustrating a
computing environment 100, according to one exemplary embodiment.
As illustrated, computing environment 100 includes at least one
client device 102, an organization computing system 104, one or
more financial data sources 106.sub.1, 106.sub.2, and 106.sub.n
(generally "data sources 106"), and one or more environmental data
sources 108.sub.1, 108.sub.2, and 108.sub.n communicating via
network 105.
[0034] Network 105 may be of any suitable type, including
individual connections via the Internet such as cellular or Wi-Fi
networks. In some embodiments, network 105 may connect terminals,
services, and mobile devices using direct connections, such as
radio frequency identification (RFID), near-field communication
(NFC), Bluetooth.TM., low-energy Bluetooth.TM. (BLE), Wi-Fi.TM.,
ZigBee.TM., ambient backscatter communication (ABC) protocols,
universal serial bus (USB), wide area network (WAN), or local area
network (LAN). Because the information transmitted may be personal
or confidential, security concerns may dictate one or more of these
types of connections be encrypted or otherwise secured. In some
embodiments, however, the information being transmitted may be less
personal, and therefore, the network connections may be selected
for convenience over security.
[0035] Network 105 may include any type of computer networking
arrangement used to exchange data. For example, network 105 may
include the Internet, a private data network, virtual private
network using a public network and/or other suitable connection(s)
that enables components in computing environment 100 to send and
receiving information between the components of environment 100.
Although one network 105 is illustrated in FIG. 1, it is understood
that network 105 may include one or more interconnected
networks.
[0036] Client device 102 may be operated by a user (or customer).
For example, client device 102 may be representative of a mobile
device, a tablet, a desktop computer, workstation or any computing
device or computing system having the capabilities described
herein. Client device 102 may include at least application 110.
Application 110 may be representative, for example, of a web
browser that allows access to a website or a stand-alone
application. Client device 102 may access application 110 to access
one or more functionalities of organization computing system
104.
[0037] Client device 102 may communicate over network 105 to
request a webpage, for example, from web client application server
112 of organization computing system 104. For example, client
device 102 may be configured to execute application 110 to access
content managed by web client application server 112. The content
that is displayed to client device 102 may be transmitted from web
client application server 112 to client device 102, and
subsequently processed by application 110 for display through a
graphical user interface (GUI) generated (or rendered) by client
device 102.
[0038] Organization computing system 104 may include at least web
client application server 112 and climate data analytics module
114. Climate data analytics module 114 may be configured to predict
the performance of one or more companies based on a predicted
environmental state. Climate data analytics module 114 may include
climate data integrator 116, a financial data integrator 118,
optimization module 120, output module 122,
environmental-to-financial (EF) converter 124, and climate module
130.
[0039] Each of climate data integrator 116, a financial data
integrator 118, an optimization module 120, an optimization module
122, an environmental-to-financial (EF) converter 124, and climate
module 130 may be comprised of one or more software modules. The
one or more software modules may be collections of code or
instructions stored on a media (e.g., memory of organization
computing system 104) that represent a series of machine
instructions (e.g., program code) that implements one or more
algorithmic steps. Such machine instructions may be the actual
computer code a processor of organization computing system 104
interprets to implement the instructions or, alternatively, may be
a higher level of coding of the instructions that is interpreted to
obtain the actual computer code. The one or more software modules
may also include one or more hardware components. One or more
aspects of an example algorithm may be performed by the hardware
components (e.g., circuitry) itself, rather as a result of an
instructions.
[0040] Climate data integrator 116 may be configured to receive
climate related data from one or more environmental data sources
108. As illustrated, organization computing system 104 may be in
communication with one or more environmental data sources 108.
[0041] One or more environmental data sources 108 may be
representative of one or more computer systems hosting
environmental data. Such environmental data sources 108 may
include, but are not limited to governmental and international
organizations and their databases such as the United States Energy
Information Administration (US EIA), the International Monetary
Fund's World Economic Outlook (IMF WEO), the US Department of
Energy (DOE), the United Nations (UN), the International Energy
Agency (IEA), and the National Air and Space Administration's
Goddard Institute for Space Studies (NASA GISS).
[0042] In some embodiments, one or more environmental data sources
108 may further include academic and research institutes such as
the Massachusetts Institute of Technology (MIT). Academic and
research institutes may provide climate or financial related
databases and models such as MIT's Emissions Prediction and Policy
Analysis model (MIT EPPA). Climate data integrator 116 may retrieve
from one or more environmental data sources 108 climate or
environmental data such as atmospheric carbon dioxide levels,
global surface temperatures, gross domestic product (GDP),
population, energy intensity of GDP, carbon intensity of energy,
carbon dioxide emissions from energy, measures of the effect of
technological progress on energy, measures of the effect of energy
price on energy intensity, cost by electrical source, electrical
generation by energy source, and fraction of revenue put into
process research and development by energy source.
[0043] In some embodiments, one or more environmental data sources
108 may include sources of corporate social responsibility (CSR)
ratings or other environmental, governance, political, geographic,
and social metrics. Other data may include financial and other
security level data from FACTSET.RTM.. In some embodiments, other
data may include regulatory reports filed to the Securities and
Exchange Commission (SEC), company disclosures through press
releases, corporate sustainability reports, industry specific
information such as Integrated Resource Plans filed by electric
utilities, analyst reports, self-reported climate disclosure
reports and other commercial and non-profit sources of
information.
[0044] In some embodiments, one or more environmental data sources
108 may further includes the carbon dioxide information analysis
center (CDIAC) of the National Climatic Data Center (NCDC), the
National Ocean and Atmospheric Administration (NOAA) and the
International Panel on Climate Change (IPCC). In one embodiment,
data related to selected features may be extracted from the climate
related data at climate data integrator 116. Selected features may
include one or more of the projected gross domestic product (GDP)
level, carbon tax, and the subsidy on renewables.
[0045] Climate data integrator 116 may be configured to aggregate
and process the environmental and/or climate information received
from one or more environmental data sources 108.
[0046] Financial data integrator 118 may configured to retrieve
financial data from one or more financial data sources 106. As
illustrated, organization computing system 104 may be in
communication with one or more financial data sources 106.
[0047] One or more financial data sources 106 may be representative
of one or more computer systems hosting environmental data. Such
environmental data sources 106 may include, but are not limited to,
stock exchanges, academic journals and the like. Data retrieved may
also include economic data such as gross domestic product (GDP),
interest rates, data on taxes and subsidies, energy prices, and
carbon prices.
[0048] In some embodiments, such financial data sources 106 may
further include data on the return and performance of securities
such as FACTSET.RTM.. In some embodiments, financial data
integrator 118 may collect data on companies listed on major stock
exchanges such as the New York Stock Exchange (NYSE), NASDAQ, the
Toronto Stock Exchange (TSX), the Toronto Stock Exchange-Venture
(TSX-V), the London Stock Exchange (LSE), the Stock Exchange of
Hong Kong (SEHK), and the Australian Securities Exchange (ASX).
Financial data integrator 118 may be configured to determine
returns for each of the constituents along with the mean returns
for all constituents.
[0049] In one embodiment, financial data sources 106 may collect
data regarding the constituents of an index. In one embodiment, the
impact of climate change on investments may be determined and
recalculated on a quarterly basis, although any suitable time
period may be used. The constituents of the index may differ for
each recalculation. The financial data may include data spanning a
number of months or years. In one embodiment, the financial data
may include historical returns for securities for at least eight
historical years. In one embodiment, securities with limited data
(e.g., securities with less than six months of data) may be
excluded. In one embodiment, securities demonstrating un-realistic
returns may be excluded.
[0050] Climate module 130 may be configured to receive climate data
from climate data integrator 116. In some embodiments, climate
module 130 may simulate the future price, supply, and demand for
energy sources using data received from climate data integrator
116. Such energy sources may include oil, gas, coal, biofuels,
renewable fuel, renewable electric, hydro, new technology and
nuclear. In some embodiments, climate module 130 may take as input
from, for example, client device 102, a scenario upon which to base
the analysis. Accordingly, climate module 130 is configured to
project climate data for each energy source based on the selected
scenario.
[0051] EF converter 124 may be configured to receive output from
climate module 130, and convert the output received from climate
module 130 into one or more profitability indicators. In other
words, EF converter 124 may be configured to convert climate
related metrics into financial related metrics. By converting
climate related metrics for each energy source into financial
related metrics, climate data analytics module 114 may generate one
or more indicators for each energy source as if each energy source
represents a company or business subsector.
[0052] EF converter 124 may use the simulations of the future price
and demand for energy sources produced by the climate module 130 to
generate a profitability estimate. In some embodiments, the
profitability estimation is based on one or more algorithms 126
(e.g., a system of difference equations). The profitability
simulations executed by EF converter 124 may generate financial
time series values such as net present value (NPV), return on
investment (ROI), debt coverage ratio (DCR), and internal rate of
return (IRR) for each of the one or more energy sources. The
financial time series values may be based on an estimate of the
total revenue of energy sources and the total cost of investment in
energy sources.
[0053] In some embodiments, the total revenue of energy sources may
be based upon energy demand and price that accounts for
macro-economic indicators such as GDP, population, average overall
cost of supply, energy supply capital lifetime and discount rate
percentage, climate change indicators such as temperature, sea
level estimates, amount of greenhouse gas emissions (GHG), and
water consumption, as well as policy indicators such as carbon
price, subsidies and GDP, population, interest rates, capital
efficiency.
[0054] Optimization module 120 may generate company level
predictions by downward deployment of the profitability indicators
generated for each energy source to a security level data. In other
words, optimization module 120 may correlate each energy source
with one or more business subsectors based on the profitability
indicators generated by EF converter 124. In some embodiments,
optimization module 120 may identify the most correlated energy
sources under one or more scenarios (e.g., a business as usual
scenario, a carbon emission scenario, etc.) for each security.
[0055] In some embodiments, after optimization module 120
identifies the industry subsectors correlated to each energy
source, optimization module 120 may downward deploy the information
another level, by correlating companies within each subsector to a
particular energy source. Such company level correlations may be
made based on, for example, comparative positioning of companies
within the industry subsector. Companies may be comparatively
positioned within an industry subsector based on, for example,
their size, growth, and/or volatility within the subsector. In one
embodiment, optimization module 120 may utilize statistical models
such as, but not limited to Monte-Carlo simulations, to test the
identified industry subsector's most correlated energy categories
(discussed further below).
[0056] In some embodiments, once optimization module 120 correlates
each energy source to a business subsector, optimization module 120
may be configured to project environmental performance of the
business subsector, based on the related climate data of the
correlated energy source generated by climate module 130.
[0057] Output module 122 may be configured to provide a scoring
based, for example, on a company's exposure to the energy industry
and climate change using environmental, social and governance (ESG)
metrics and the results from the optimization module 120. The
scoring may provide a quantitative and objective means for
comparing the performance of different companies in accordance with
ESG standards.
[0058] In some embodiments, output module 122 may produce a
website, accessible by one or more users via application 110
executing on client device 102. The website may provide a dashboard
that allows users to view results generated by climate module 130,
EF converter 124, and/or optimization module 120. In some
embodiments, output module 122 may generate one or more data files
for electronic transfer to client device 102. EF converter 124 may
provide the output produced by output module 102 to one or more
client devices 102. In some embodiments, the results from the
optimization module 120 may be used to project portfolio
performance relative to multiple emission scenarios, optimize a
portfolio based on selected scenarios, and climate and energy risk
parameters associated with the selected scenarios.
[0059] Some portions of above description describe the embodiments
in terms of algorithms and symbolic representations of operations
on information. These algorithmic descriptions and representations
are commonly used by those skilled in the data processing arts to
convey the substance of their work effectively to others skilled in
the art. These operations, while described functionally,
computationally, or logically, are understood to be implemented by
computer programs or equivalent electrical circuits, microcode, or
the like. Furthermore, it has also proven convenient at times, to
refer to these arrangements of operations as modules, without loss
of generality. The described operations and their associated
modules may be embodied in specialized software, firmware,
specially-configured hardware or any combinations thereof.
[0060] Those skilled in the art will appreciate that organization
computing system 104 may be configured with more or less modules to
conduct the methods described herein with reference to FIGS. 2-6.
As illustrated in FIGS. 2-6, the methods shown may be performed by
processing logic that may comprise hardware (e.g., circuitry,
dedicated logic, programmable logic, microcode, etc.), software
(such as instructions run on a processing device), or a combination
thereof. In one embodiment, the methods shown in FIGS. 2-6 may be
performed by one or more specialized processing components
associated with components 112-130 of organization computing system
104 of FIG. 1.
[0061] FIG. 2 is a flow diagram illustrating a method 200 of
climate data processing and impact prediction, according to one
exemplary embodiment. As illustrated, method 200 may have four
phases.
[0062] At phase 202, climate data analytics module 114 may generate
one or more environmental metrics for one or more energy sources
based on a selected scenario. The one or more operations
implemented at phase 202 are discussed in further detail below, in
conjunction with FIG. 3.
[0063] At phase 204, climate data analytics module 114 may generate
one or more profitability indicators for each of the one or more
energy sources. For example, climate data analytics module 114 may
generate the one or more profitability indicators based on the one
or more environmental metrics generated during phase 202. The one
or more operations implemented at phase 204 are discussed in
further detail below, in conjunction with FIG. 4.
[0064] At phase 206, climate data analytics module 114 may
correlate each energy source with a financial subsector based on
the one or more profitability indicators. For example, climate data
analytics module 114 may downward deploy the profitability
indicators generated in phase 204 to one or more financial
subsectors down to one or more companies within each financial
subsector. The one or more operations implemented during phase 206
are discussed in further detail below, in conjunction with FIG.
5.
[0065] At phase 208, climate data analytics module 114 may generate
an output data set for a user. For example, climate data analytics
module 114 may provide a scoring based on a company's exposure to
the energy industry and climate change using environmental and
social and governance (ESG) metrics. The one or more operations
implemented during phase 208 are discussed in further detail below,
in conjunction with FIG. 6.
[0066] FIG. 3 is a flow diagram illustrating a method 300 of
operation during phase 202, according to one exemplary embodiment.
Method 300 may begin at step 302.
[0067] At step 302, organization computing system 104 may receive a
request for analysis from client device 102. In some embodiments,
organization computing system 104 may receive the request for
climate impact prediction via a website accessed by client device
102 via application 110 executing thereon.
[0068] At step 304, organization computing system 104 may prompt
the user of client device 102 to select one or more scenarios for
the climate impact prediction. Possible scenarios may include a
carbon-minimum scenario, a carbon-maximum scenario, and a business
as usual scenario. In one embodiment, the scenario may be selected
by an automated program. A carbon-minimum scenario may be
associated with an approximately 2.8 degree temperature increase in
average global temperature. A carbon-maximum scenario may be
associated with an approximately 4.6 degree temperature increase in
average global temperature. A business as usual scenario may be
determined by the United States Department of Energy.
[0069] The scenario selection may specify metrics related to the
growth of population, gross domestic product (GDP) per capita,
greenhouse gas (GHG) emissions, and removals other than CO.sub.2
emissions. The user may also specify policy changes, including a
carbon price, subsidy, or tax aimed at a particular energy source,
policies for more rapid improvement in end-use energy efficiency,
utility performance standards, technical breakthroughs affecting
the cost and carbon intensity of particular energy sources, or an
earlier retirement of existing energy supply sources. The climate
data input into the climate model 111 may include indicators such
as gross domestic product (GDP), energy source tax and subsidies,
interest rates, and capital efficiency percentage. The energy
sources may include, without being limited to, coal, gas, oil,
renewables, hydro, nuclear, new tech, and biofuel. Climate models
including ensemble models may be used to generate the profitability
indicators.
[0070] At step 306, organization computing system 104 may input one
or more metrics associated with the selected one or more scenarios
to generate one or more environmental metrics for each energy
source. For example, climate module 130 may input the one or more
metrics into an energy-climate policy simulator.
[0071] In some embodiments, climate module 130 may input the one or
more metrics into an Energy-Rapid Overview and Decision-Support
(En-ROADS) simulator, available from Climate Interactive.COPYRGT..
Climate module 130 may leverage En-ROADS simulator to provide
information relating to changes in global GDP, energy efficiency,
R&D results, carbon price, fuel mix, and other factors will
change carbon emissions and impacts on global climate, such as
temperature change. En-ROADS simulator may receive, as input from
climate module 130, a measure of global GDP, energy efficiency,
R&D results, carbon price, fuel mix, and the like. Such inputs
may be received by climate module 130 from climate data integrator
116.
[0072] In some embodiments, climate module 130 may utilize a
specialized software configured to execute simulations with
improved model quality, data connections, and advanced algorithms.
In some embodiments, En-ROADS simulator may be utilized with Vensim
programming. En-ROADS simulator and other energy-climate policy
simulations may apply the principles of system dynamics modeling.
In some embodiments, the En-ROADS model may solve a system of
nonlinear ordinary difference equations by Euler integration to
generate one or more output values. In some embodiments, the
En-ROADS model may be configured to solve a system of nonlinear
ordinary difference equations by Euler integration using a time
step of one-eighth of a year. In some embodiments, the En-ROADS
simulator may start at 1990 and simulate through 2100.
[0073] Using the En-ROADS simulator, climate module 130 may produce
a solution horizon that is recursive-dynamic, and determine a
solution that is a partial equilibrium that accounts for price
elastic demand, and incorporating short and long term supply,
demand, and pricing effects. Although the En-ROADS simulator may be
capable of performing computationally intensive sensitivity
analysis using, for example, Monte Carlo optimization, in some
embodiments, climate module 130 may be configured to utilize simple
scenario simulations. In some embodiments, the climate module 130
may execute En-ROADS simulator locally within organization
computing system 104. In some embodiments, climate module 130 may
execute En-ROADS simulator as a part of a networked computing
environment 100.
[0074] Climate module 130 may generate a market price of delivered
fuels, demand of delivered fuels, average embodied fixed cost of
fuel capacity, instant variable cost of fuel supply, carbon tax for
delivered fuels, source subsidy for delivered fuels over time, fuel
completing capacity, actual retirement rate, and delivered fuel
capital lifetime for each fuel type. Climate module 130 may also
generate a measure of the market price of electricity and the
production for electricity, average embodied fixed cost of electric
capacity, instant variable coast of electric supply, source subsidy
to electric producers over time, electricity completing capacity,
actual retirement rate, and electric supply capital lifetime for
each electric source.
[0075] At step 308, organization computing system 104 may receive,
as output, one or more environmental metrics for each of the one or
more energy sources. For example, climate module 130 may generate
one or more environmental metrics for each of oil, gas, coal,
biofuels, renewable fuel, renewable electric, hydro, new
technology, and nuclear energy sources.
[0076] FIG. 4 is a flow diagram illustrating a method 400 of
operation during phase 204, according to one exemplary embodiment.
Method 400 may begin at step 402.
[0077] At step 402, climate data analytics module 114 may identify
one or more values in one or more environmental metrics. For
example, EF converter 124 may identify one or more profitability
indicators to be generated. For each profitability indicator,
optimization module 120 may identify those environmental metrics
output by climate module 130 that may be used to generate each of
the one or more profitability indicators. Using a specific example,
to generate net present value (NPV), EF converter 124 may identify
one or more metrics associated with internal cost of the energy
source.
[0078] At step 404, climate data analytics module 114 may input the
one or more metrics output by climate module 130 into EF converter
124. For example, EF converter 124 may generate one or more
profitability indicators for each energy source under each scenario
based on the one or more metrics output by climate module 130.
[0079] EF converter 124 may generate one or more profitability
indicators, such as absolute annualized capital cost, absolute
variable cost, absolute revenue, gross margin, and unit gross
margin using algorithm 126. Additionally, for capital installed at
a specified year for each delivered fuel type and for each source
of electricity EF converter 124 may generate one or more
profitability indicators related to, without being limited to, the
capacity of the investor, the investor gross margin, the equivalent
overnight capital costs, investment, equity, debt, investor net
cash flow, net present value (NPV), internal rate of return (IRR),
and the return on investment (ROI). In some embodiments, EF
converter 124 may generate the total NPV and a weighted average of
IRR and ROI for all of the constituents.
[0080] Accordingly, the profitability indicators generated by the
EF converter 124 may include a measure of the NPV, ROI, DCR, IRR
and the like for each of the one or more energy sources. In one
embodiment the generated profitability indicators may be reflect
regional variability.
[0081] At step 406, climate data analytics module 114 may receive,
as output, one or more profitability indicators based on the
environmental metrics. For example, climate data analytics module
114 may receive from EF converter 124 one or more profitability
indicators generated by providing metrics generated by climate
module 130 into algorithm 126. The one or more profitability
indicators provide a financial outlook for each energy source as if
each energy source was a given financial subsector or company.
[0082] FIG. 8 is a screen shot illustrating example graphical user
interface (GUI) 800, according to one exemplary embodiment. As
illustrated in GUI 800, there may be one or more inputs 802 and one
or more outputs 804. One or more inputs 802 may correspond to the
one or more environmental metrics generated by climate module 130,
discussed above in conjunction with FIG. 3. One or more outputs 804
may correspond to the one or more profitability indicators
generated by climate data analytics module 114.
[0083] FIG. 5 is a flow chart illustrating a method 500 of
operations during phase 206, according to one exemplary embodiment.
Method 500 may begin at step 502.
[0084] At step 502, climate data analytics module 114 may retrieve
(e.g., pull) financial information from one or more subsector
indices. For example, financial data integrator 118 may retrieve
from one or more financial data sources 106 the financial data of
one or more subsector indices. In some embodiments, the one or more
data sources 106 hosting the financial data may be global industry
classification standards (GICS) developed by Morgan Stanley Capital
International (MSCI). In some embodiments, financial data
integrator 118 may retrieve financial information for 24 GICs
subdivisions). Climate data analytics module 114 may further
retrieve ROI information under one or more pre-selected scenarios
(e.g., business-as-usual).
[0085] In some embodiments, financial data integrator 118 may
identify a portfolio including a list of securities. In some
embodiments, the list of securities may correspond with a benchmark
such as the S&P 500, Russell 100 or the like. In some
embodiments, the climate data analytics module 114 may be provided
with the data set from a user computer. In some embodiments, the
portfolio or benchmark of interest may be selected based on an
index requirement. The index requirement may be based on a Market
Focus (i.e., Domestic, International), Fund theme (i.e., Sector of
Interest: Technology, Energy), Factor Choices (i.e., Growth, Value)
and the like. Once identified, climate data analytics module 114
may request data and information related to the list of securities
from one or more financial data source 106 in communication with
the climate data analytics module 114. Data sources that provide
data and information related to the list of securities may include
financial data and software resources that provide real-time news
and quotes, company and portfolio analysis, multi-company
comparisons, industry analysis, company screening, portfolio
optimization and simulation, predictive risk measurements,
alphatesting and tools to value and analyze fixed income securities
and portfolios. This data and information may include financial
data and information.
[0086] In some embodiments, the financial data integrator 118 may
perform the discussed identification and retrieval steps. Financial
data integrator 118 may pull time series data on stock prices for a
benchmark, calculate the returns from the time series data on stock
prices for the benchmark, calculate the average returns for
specific securities within the benchmark and find the downside
variance-covariance of all individual securities within the
benchmark based on Sortino ratios. The financial data integrator
118 may also determine one or more of a data sets' minimum and
maximum weights, sector diversity and financial risk
coefficients.
[0087] At step 504, climate data analytics module 114 may smooth
the retrieved financial information within a pre-defined window.
For example, optimization module 120 may smooth the retrieved
financial information for the one or more subsector indices with a
five-point window locally weighted regression using, for example,
data from a previous range of years (e.g., the previous 6 years).
Exemplary weighted regressions may include locally weighted
regressions such as, but not limited to, a locally weighted
scatterplot smoothing (LOWESS). Smoothing the retrieved financial
information aids in identifying relationships among variables, as
well as trends in the information.
[0088] At step 506, climate data analytics module 114 may downward
deploy the one or more profitability indicators generated by EF
converter 124 to one or more financial subsectors, to identify
sector level correlations. Optimization module 120 may identify
correlations between the one or more energy sources and one or more
financial subsectors using, for example, Kendall's tau coefficient.
Kendall's tau coefficient is used to measure association between
the one or more profitability indicators of each energy source and
the financial information of the one or more financial subsectors.
Kendall's tau coefficient may provide a ranking of relevance
between each financial subsector and each energy source.
[0089] Such operations may include:
TABLE-US-00001 def plot_smoothing( ) : fig = plt.figure
(figsize=(20,12)) timely_sec = sec.loc [2003:2018] for idx, ind in
enumerate (timely_sec.columns [20:24]): smoothed = ent. smooth
(timely_sec.loc[:,ind],5) ax = fig.add_subplot (2,2,idx+1)
ax.plot(timely_sec.loc[:,ind], `k=.`, linewidth=1)
ax.plot(smoothed, `r`, linewidth=2)
ax.set_title(timely_sec.loc[:,ind].name, size=18)
ax.set_xlabel(`Year`,size=18) ax.set_ylabel(`Qtrly Period Ret
Propn`,size=18) ax.tick_params(labelsize=18) ax.grid(False)
fig.suptitle(`Smoothed Returns`,size=30)
fig.subplots_adjust(top=.9,bottom=0.08,left=0.1,right=0.9m
hspace=0.4, wspace=0.3) return
[0090] FIG. 9 is a screen shot illustrating example GUI 900,
according to one exemplary embodiment. As illustrated in FIG. 9,
GUI 900 may include one or more histograms. Each histogram
represents a correlation between one or more energy sources and one
or more financial subsectors. The peaks of each histogram may
represent the most correlated energy sources and financial
subsectors.
[0091] Referring back to FIG. 5, at step 508, climate data
analytics module 114 may identify one or more energy sources that
correspond to each of the one or more financial subsectors. In some
embodiments, optimization module 120 may identify the four most
relevant energy sources to each of the twenty-four financial
subsectors. In those embodiments in which all energy sources are
equally correlated with a particular financial subsector,
optimization module 120 may select those four energy sources that
are the "greenest." Greenest, for example, may correspond to the
energy source have the smallest Carbon footprint.
[0092] At step 510, climate data analytics module 114 may retrieve
financial data related to one or more securities. For example,
financial data integrator 118 may retrieve from one or more
financial data sources 106 monthly return information for one or
more securities.
[0093] At step 512, climate data analytics module 114 may smooth
the retrieved financial information. For example, optimization
module 120 may smooth the security level information using a
weighted regression. Such weighting regression may include LOWESS
regressions. Smoothing the retrieved financial information aids in
identifying relationships among variables, as well as trends in the
information.
[0094] At step 514, climate data analytics module 114 may map the
one or more securities to a respective financial subsector. For
example, optimization module 120 may identify one or more financial
subsectors to which each respective securities applies. In some
embodiments, climate data analytics module 114 may procure economic
classification data for a given security, such as, without being
limited to, economic classification according to the Global
Industry Classification Standard (GICS). Each economic sector may
be mapped to a subset of an energy source. Using this mapping,
climate data analytics module 114 may map each security to a given
energy source from a financial subsector of its economic
sector.
[0095] At step 516, climate data analytics module 114 may correlate
the security level information with the mappings from step 508. For
example, optimization module 120 may correlate the security level
information with the four most relevant energy sources identified
above in conjunction with step 508.
[0096] At step 518, climate data analytics module 114 may identify
one or more energy sources that correspond to each of the one or
more securities. For example, continuing with the example in step
508 above, optimization module 120 may identify the two most
relevant energy sources for each of the one or more securities. In
those embodiments in which three or more energy sources are equally
relevant to a respective security, optimization module 120 may
break any ties by, for example, selecting the "greenest" energy
source.
[0097] FIG. 10 is a screen shot illustrating example GUI 1000,
according to one exemplary embodiment. As illustrated, GUI 1000 may
include one or more graphical representations of returns by
subsector from 2016 to 2018. Such GUI 1000 may be accessible to
user via client device 102.
[0098] FIG. 6 is a flow diagram illustrating a method 600 of
operations during phase 208, according to one exemplary embodiment.
Method 600 involves predicting future climate impact for each
security (or financial subsector) and presenting that information
to the requestor. In general, according to method 600, one or more
mapped energy sector(s) may be used as input variable(s) to
forecast a performance of a security. Method 600 may use
correlations (described below) to identify energy mappings. Once
energy mappings are identified, profitability indicators of the
mapped energy sources (e.g., a ROI) may be used as an input (e.g.,
explanatory variables) to predict scenario-specific return
performance of the securities. Method 600 may begin at step
602.
[0099] At step 602, climate data analytics module 114 may be
configured to generate future climate predictions for each security
using a regression model. Such regression models may include a
linear regression model. For example, climate data analytics module
114 may use the identified energy mappings by identifying one or
more profitability indicators associated with a given security. As
input to the regression model, climate data analytics module 114
may provide the one or more profitability indicators to predict a
scenario-specific return performance for the security. Further, in
some embodiments, for each security, output module 122 may retrieve
climate data generated by climate module 130 for each mapped energy
source. In some embodiments, output module 122 may retrieve the
climate data according to one or more selected scenarios. The
retrieved climate data includes future climate data projections.
Output module 122 may then project the future climate impact for
each security using the future climate data projections and
historical price returns by leveraging one or more linear
regression algorithms. The projections may be compiled in a data
set to be provided to the user.
[0100] At step 604, output module 122 may generate a sensitivity
measure for each security by analyzing the spread of each
security's projected returns across each of the given
scenarios.
[0101] In some embodiments, output module 122 may generate one or
more scores for each securities' returns across one or more
scenarios. For example, output module 122 may generate three scores
for each of a business as usual (BAU) scenario, a carbon max
scenario, and a carbon min scenario.
[0102] In some embodiments, output module 122 may generate a spread
of a security's expected performance using expected returns. A
higher spread indicates a higher sensitivity towards economic and
policy changes. A low spread indicates lower risk towards these
changes. A min max area value indicates the min max area value for
stocks in a security's portfolio. A min max area value may be
calculated using:
MMA s = 1 R s _ ( BAU ) t = t * T R x , ts - R m , ts
##EQU00001##
where t*>TOD(Time of Day), R.sub.x,ts are expected returns
computing using regression to carbon max scenario, and R.sub.m,ts
are expected returns computing using carbon min scenario.
[0103] In some embodiments, output module 122 may generate a min
max draw down score. The min max draw down score may reflect the
spread of a constituent's expected performance as computed using
Expected Returns for the Carbon Minimum and Carbon Maximum
scenarios. A higher spread may be indicative of a higher risk
towards economic and policy changes.
[0104] In some embodiments, output module 122 may generate an
affinity towards carbon minimum score (ACM). The ACM may reflect
the spread of difference between the expected returns for the
carbon minimum scenario and the expected returns of the business as
usual scenario. A positive score for the ACM may indicate that a
constituent is expected to perform better a carbon minimum
scenario. A negative score for the ACM may indicate that the
constituent is expected to perform better for a carbon maximum
scenario.
[0105] The ACM may be defined as follows:
ACM s = 1 R s _ ( BAU ) t = t * T ( R m , ts , R BAU , ts )
##EQU00002##
where t*>TOD (Time of Deviation), R.sub.BAU,ts are the expected
returns computed using a regression to carbon business as usual
scenario, R.sub.m,ts are the expected returns computed using a
carbon min scenario, and R.sub.S are the mean returns computed
using the business as usual scenario.
[0106] In one embodiment, output module 122 may generate an
affinity towards carbon maximum score (AXM). The AXM may reflect
the spread of difference between the expected returns for the
carbon maximum scenario and the expected returns of the business as
usual scenario. A positive AXM may indicate that a constituent is
expected to perform better under a carbon maximum scenario while a
negative score may indicate that a constituent is expected to
perform better under a carbon minimum scenario.
[0107] The AXM may be defined as follows:
AXM s = 1 R s _ ( BAU ) t = t * T ( R X , ts , R BAU , ts )
##EQU00003##
where t*>TOD (Time of Deviation) R.sub.x,ts are the expected
returns computed using a regression to carbon max scenario,
R.sub.BAU,ts are the expected returns computed using a business as
usual scenario, and R.sub.S(BAU) are the expected returns computed
using the business as usual scenario.
[0108] In some embodiments, output module 120 may compute the
securities' energy dependency using specialized software that is
configured for computationally intensive calculations. In one
embodiment, python Scikit-learn may be used to run multiple support
vector machine (SVM) regressions in order to compute the
constituents' energy dependency. SVMs may be used for their
effectiveness in high dimensional spaces, ability to be run in
cases where the number of dimensions is greater than the number of
samples. SVMs may also be memory efficient as they may use support
vectors including a subset of training points in the training
function. Additionally SVMs may be versatile as different kernel
functions can be specified for the decision function.
[0109] At step 606, output module 122 may optimize the one or more
projected values for each security. For example, output module 122
may generate one or more data sets that comprise the one or more
projected values for each security. Such optimization equation may
be:
{grave over (W)}R-(1-){grave over (W)}.SIGMA.W
where (0,1)|.A-inverted.s S, w.sub.s.gtoreq.0.001 (Min weight
constraint), w.ltoreq.0.05 (Max weight constraint), and
.SIGMA.w.sub.s=1.0
[0110] In one embodiment, output module 122 may use securities
sensitivities as a constraint and optimize the data set using a
Markowitz Optimization Process. In one embodiment, statistical
analysis and big data may be used to determine the objective
function in the Markowitz Optimization process. In one embodiment,
a convex optimizer package with a beta reduction component may be
used. In general, any suitable optimization model may be used to
optimize the data in accordance with embodiments described
herein.
[0111] In some embodiments, output module 122 may optimize the data
set for higher returns, less risk and higher carbon reductions.
Output module 122 may provide a weighting for constituents of the
data set such that any data set constructed from the provided
weightings would have a lower aggregate climate risk in comparison
to the benchmark.
[0112] In some embodiments, output module 122 may optimize the data
set towards preferred scenarios. For example, if a user were
interested in investing in a portfolio that outperforms in
carbon-min scenario output module 122 may re-weight the securities
in accordance with the user's socioeconomic, policy and climate
preferences.
[0113] In some embodiments, output module 122 may utilize a convex
optimization utility that is based on Python. For example, output
module 122 may use a Basic Linear Algebra Subprograms (BLAS) that
are capable of efficiently performing matrix based mathematical
operations; thereby allowing the output module 122 to optimize
larger amounts of data using algorithms that utilize matrices
without taxing either the processor or memory components of the
server system 102. In addition or as an alternative, output module
122 may solve a non-convex optimization problem using a Generalized
Simulated Annealing for Global Optimization package. In one
embodiment, output module 122 may analyze the conditional value at
risk to assess the likelihood that a specific loss will exceed the
value at risk.
[0114] In some embodiments, output module 122 may optimize the data
set and provide a set of weights. The set of weights may be
generated on various time scales including every week, month,
quarter, or year. The weights may be used to compute indexes,
portfolios, and holdings for exchange traded funds.
[0115] In one embodiment, the weightings produced by output module
122 may be used to construct smart climate indices that are
designed to track well established global indices such as the
S&P500, Russell 1000 and the like. However, the smart climate
indices may be optimized to lower beta, volatility and tracking
error while increasing climate impact and financial performance.
The smart climate indices may also be used for ETF creation and
benchmarked performance evaluations.
[0116] In some embodiments, the weightings produced by the output
module 122 may be used to construct a sensitivity restricted
portfolio. The sensitivity restricted portfolio may be configured
to maximize the returns of the portfolio subject to an ESG based
constraint and an Energy Mix Transition Risk (EMTR) score
constraint.
[0117] In some embodiments, the weightings produced by the output
module 122 may be used to construct a carbon minimum biased
portfolio by maximizing the returns of the portfolio subject to an
ESG based constraints and an ACM constraint.
[0118] In some embodiments, the weightings produced by output
module 122 may be used to construct a carbon minimum biased profile
by maximizing the returns of the portfolio subject to ESG
constraints and an AXM constraint.
[0119] In some embodiments, the weightings produced by the output
module 122 may be used to provide portfolio optimization for
climate and energy volatility, and may include scenario analysis
and shock testing. The weightings may be used to evaluate client
portfolios and climate impact.
[0120] In some embodiments, output module 122 may provide users
with new weights with no addition or subtraction of constituents
from the data set. In one embodiment, output module 122 may produce
optimized data sets that may provide the best opportunities for
gain with the least risk, materially reduce risk of divergence in
the global economy from baseline to high/low carbon scenarios,
contain constituents having greater stability and improved
performance under more climate friendly policies and activities,
contain constituents having a lower predicted tracking error
against a benchmark data set, and/or contain constituents that have
the highest average carbon reductions over time frames.
[0121] At step 608, output module 122 may generate an output data
set and/or values in accordance with user conditions. In some
embodiments, output module 122 may output a set of weights that are
provided quarterly to a separate index computation entity. The
index computation entity may use the weights to compute daily index
performance and provide the computed index to one or more financial
portals.
[0122] In some embodiments, individual public security level risk
and optimization scores (weightings and related data) produced by
the climate module 130, EF converter 124, optimization module 120,
and output module 122 may be provided to third parties such as data
providers for inclusion into their own system.
[0123] In some embodiments, output module 122 transmit information
to client device 102, such that client device may generate or
render a graphical user interface (GUI) that is configured to
display data and information related to the climate module 130, EF
converter 124, optimization module 120, and output module 122. In
some embodiments, the information may be accessible to a user via
application 110 executing on client device 102.
[0124] FIGS. 11A and 11B are screen shots illustrating example GUI
1100 and GUI 1101, respectively, according to one exemplary
embodiment. GUI 1100 may include graph 1102. GUI 1101 may include
graph 1104. Each graph 1102, 1104 may represent projections for a
respective company. As illustrated, each graph 1102 and 1104
visually depicts projections for each company under a plurality of
scenarios.
[0125] FIG. 12 is a screen shot illustrating example GUI 1200,
according to one exemplary embodiment. As illustrated, a user may
login to a website to view a dashboard containing data and
information related to climate data analytics module 104 from
client device 102 via application 110. In some embodiments, the
dashboard may display time series information for climate and
financial data. The time series information for climate and
financial data may be generated by climate data integrator 116 and
financial data integrator 118. The dashboard may also display
financial index information.
[0126] Systems and methods of the present disclosure may include
and/or may be implemented by one or more specialized computers or
other suitable components including specialized hardware and/or
software components. For purposes of this disclosure, a specialized
computer may be a programmable machine capable of performing
arithmetic and/or logical operations and specially programmed to
perform the functions described herein. In some embodiments,
computers may comprise processors, memories, data storage devices,
and/or other commonly known or novel components. These components
may be connected physically or through network or wireless links.
Computers may also comprise software which may direct the
operations of the aforementioned components. Computers may be
referred to with terms that are commonly used by those of ordinary
skill in the relevant arts, such as servers, personal computers
(PCs), mobile devices, and other terms. It will be understood by
those of ordinary skill that those terms used herein are
interchangeable, and any special purpose computer capable of
performing the described functions may be used.
[0127] Computers may be linked to one another via one or more
networks. A network may be any plurality of completely or partially
interconnected computers wherein some or all of the computers are
able to communicate with one another. It will be understood by
those of ordinary skill that connections between computers may be
wired in some cases (e.g., via wired TCP connection or other wired
connection) and/or may be wireless (e.g., via a WiFi network
connection). Any connection through which at least two computers
may exchange data can be the basis of a network. Furthermore,
separate networks may be able to be interconnected such that one or
more computers within one network may communicate with one or more
computers in another network. In such a case, the plurality of
separate networks may optionally be considered to be a single
network.
[0128] The term "computer" shall refer to any electronic device or
devices, including those having capabilities to be utilized in
connection with an electronic exchange system, such as any device
capable of receiving, transmitting, processing and/or using data
and information. The computer may comprise a server, a processor, a
microprocessor, a personal computer, such as a laptop, palm PC,
desktop or workstation, a network server, a mainframe, an
electronic wired or wireless device, such as for example, a
telephone, a cellular telephone, a personal digital assistant, a
smartphone, an interactive television, such as for example, a
television adapted to be connected to the Internet or an electronic
device adapted for use with a television, an electronic pager or
any other computing and/or communication device.
[0129] The term "network" shall refer to any type of network or
networks, including those capable of being utilized in connection
with environment 100 described herein, such as, for example, any
public and/or private networks, including, for instance, the
Internet, an intranet, or an extranet, any wired or wireless
networks or combinations thereof.
[0130] The term "computer-readable storage medium" should be taken
to include a single medium or multiple media that store one or more
sets of instructions. The term "computer-readable storage medium"
shall also be taken to include any medium that is capable of
storing or encoding a set of instructions for execution by the
machine and that causes the machine to perform any one or more of
the methodologies of the present disclosure.
[0131] FIG. 7 is a block diagram illustrating an exemplary
computing environment 700, according to some embodiments. Computing
environment 700 includes computing system 702 and computing system
752. Computing system 702 may be representative of client device
102. Computing system 752 may be representative of organization
computing system 104.
[0132] Computing system 702 may include processor 704, memory 706,
storage 708, and network interface 710. In some embodiments,
computing system 702 may be coupled to one or more input/output
(I/O) device(s) 722 (e.g., keyboard, mouse, display, etc.).
[0133] Processor 704 may retrieve and execute program code 716
(i.e., programming instructions) stored in memory 706, as well as
store and retrieve application data. Processor 704 may be included
to be representative of a single processor, multiple processors, a
single processor having multiple processing cores, and the like.
Network interface 710 may be any type of network communications
allowing computing system 702 to communicate externally via
computing network 705. For example, network interface 710 may be
configured to enable external communication with computing system
752.
[0134] Storage 708 may be, for example, a disk storage device.
Although shown as a single unit, storage 708 may be a combination
of fixed and/or removable storage devices, such as fixed disk
drives, removable memory cards, optical storage, network attached
storage (NAS), storage area network (SAN), and the like.
[0135] Memory 706 may include application 712, operating system
714, and program code 716. Program code 716 may be accessed by
processor 704 for processing (i.e., executing program
instructions). Program code 716 may include, for example,
executable instructions for communicating with computing system 752
to display one or more pages of website 768. Application 712 may
enable a user of computing system 702 to access a functionality of
computing system 752. For example, application 712 may access
content managed by computing system 752, such as website 768. The
content that is displayed to a user of computing system 702 may be
transmitted from computing system 752 to computing system 702, and
subsequently processed by application 712 for display through a
graphical user interface (GUI) of computing system 702.
[0136] Computing system 752 may include processor 754, memory 756,
storage 758, and network interface 760. In some embodiments,
computing system 752 may be coupled to one or more I/O device(s)
762.
[0137] Processor 754 may retrieve and execute program code 766
(i.e., programming instructions) stored in memory 756, as well as
store and retrieve application data. Processor 754 is included to
be representative of a single processor, multiple processors, a
single processor having multiple processing cores, and the like.
Network interface 760 may be any type of network communications
enabling computing system 752 to communicate externally via
computing network 705. For example, network interface 760 allows
computing system 752 to communicate with computer system 702.
[0138] Storage 758 may be, for example, a disk storage device.
Although shown as a single unit, storage 758 may be a combination
of fixed and/or removable storage devices, such as fixed disk
drives, removable memory cards, optical storage, network attached
storage (NAS), storage area network (SAN), and the like.
[0139] Memory 756 may include climate data analytics module 762,
operating system 764, program code 766, and website 768. Program
code 766 may be accessed by processor 754 for processing (i.e.,
executing program instructions). Program code 766 may include, for
example, executable instructions configured to perform steps
discussed above in conjunction with FIGS. 2-6. As an example,
processor 754 may access program code 766 to perform operations for
assessing climate change risk at a security level. Website 768 may
be accessed by computing system 702. For example, website 768 may
include content accessed by computing system 702 via a web browser
or application.
[0140] Climate data analytics module 762 may be configured to
predict the environmental performance of one or more companies
based one or more energy sources. For example, climate data
analytics module 762 may generate one or more environmental metrics
for each energy source based on one or more scenarios selected by
an end user. Climate data analytics module 762 may then convert the
generated climate data for each energy source into one or more
profitability indicators. The climate data to financial data
conversion allows climate data analytics module 762 to downward
correlate each energy source to a respective industry subsector
using historical price returns of each industry subsector. Within
each industry subsector, the present system can identify one or
more companies to which each energy source maps. From this
information, the present system is able to assess climate change
risk associated with each company based on operations of the
company.
[0141] Referring next to FIGS. 13 and 14, example graphical user
interfaces illustrating various scenario-predictive/forecast return
performance indicators that may be created by climate data
analytics module 114. FIG. 13 is a screen shot illustrating an
example graphical user interface (GUI) 1300, according to one
exemplary embodiment. As illustrated, GUI 1300 may represent a user
interface that may be generated by climate data analytics module
114 to illustrate one or more outputs of climate data analytics
module 114. For example, an energy mixed transition risk-forecast
transition risk interaction may be displayed via GUI 1300. As
illustrated, climate data analytics module 114 may integrate one or
more outputs generated, for example, using the one or more
operations discussed above in conjunction with FIGS. 2-6 with
traditional stock screening variables that may be retrieved using
financial data integrator 118.
[0142] Via GUI 1300, an end user may be able to filter various
stocks using traditional stock characters, such as, but not limited
to, market cap, GICS economic sector, and style, along with one or
more climate risk scores and rankings generated by climate data
analytics module 114. Accordingly, GUI 1300 provides an integrated
interface that allows users to filter stock information based on
climate risk scores and ranking, as well as more traditional
financial variables. Conventional systems are simply unable to
produce such an interface due to inaccuracies in reported
environmental data, and non-uniformity in the portions of
environmental data that are accurate.
[0143] Climate data analytics module 114 may transmit the generated
GUI 1300 to, for example, client device 102, such that client
device 102 may render GUI 1300 and display GUI 1300 to the user via
a display of client device 102. For example, client device 102 may
display GUI 1300 via application 110 executing thereon.
[0144] FIG. 14 is a screen shot illustrating an example graphical
user interface (GUI) 1400, according to one exemplary embodiment.
As illustrated, GUI 1400 may represent a user interface that may be
generated by climate data analytics module 114 to illustrate one or
more outputs of climate data analytics module 114. For example, GUI
1400 may include a comparison between actual and forecasted stock
performance.
[0145] Climate data analytics module 114 may transmit the generated
GUI 1400 to, for example, client device 102, such that client
device 102 may render GUI 1400 and display GUI 1400 to the user via
a display of client device 102. For example, client device 102 may
display GUI 1400 via application 110 executing thereon.
[0146] While the foregoing is directed to embodiments described
herein, other and further embodiments may be devised without
departing from the basic scope thereof. For example, aspects of the
present disclosure may be implemented in hardware or software or a
combination of hardware and software. One embodiment described
herein may be implemented as a program product for use with a
computer system. The program(s) of the program product define
functions of the embodiments (including the methods described
herein) and can be contained on a variety of computer-readable
storage media. Illustrative computer-readable storage media
include, but are not limited to: (i) non-writable storage media
(e.g., read-only memory (ROM) devices within a computer, such as
CD-ROM disks readably by a CD-ROM drive, flash memory, ROM chips,
or any type of solid-state non-volatile memory) on which
information is permanently stored; and (ii) writable storage media
(e.g., floppy disks within a diskette drive or hard-disk drive or
any type of solid state random-access memory) on which alterable
information is stored. Such computer-readable storage media, when
carrying computer-readable instructions that direct the functions
of the disclosed embodiments, are embodiments of the present
disclosure.
[0147] While the present disclosure has been discussed in terms of
certain embodiments, it should be appreciated that the present
disclosure is not so limited. The embodiments are explained herein
by way of example, and there are numerous modifications, variations
and other embodiments that may be employed that would still be
within the scope of the present disclosure.
* * * * *