U.S. patent application number 14/279310 was filed with the patent office on 2014-11-20 for systems and methods for data mining and modeling.
This patent application is currently assigned to KENSHO LLC. The applicant listed for this patent is KENSHO LLC. Invention is credited to Daniel NADLER.
Application Number | 20140344186 14/279310 |
Document ID | / |
Family ID | 51059542 |
Filed Date | 2014-11-20 |
United States Patent
Application |
20140344186 |
Kind Code |
A1 |
NADLER; Daniel |
November 20, 2014 |
SYSTEMS AND METHODS FOR DATA MINING AND MODELING
Abstract
Techniques for prediction of financial instrument returns,
identifying statistical history, the discovery of pricing
anomalies, and financial instrument visualization are disclosed. In
one particular exemplary embodiment, the techniques may be realized
as a method for identifying financial instrument returns and
pricing anomalies including matching, using at least one computer
processor one or more portions of current market data associated
with a financial instrument with historical market data, averaging
outcomes of matched historical market data, and providing a
probabilistic outcome for financial instrument returns, pricing
anomalies, or other metrics based on the matched historical market
data and the current market data. Techniques for financial
instrument analysis may also include processing event data,
correlating the event data using a large volume of historical
market data to identify a predicted impact on returns of a
financial instrument and/or pricing anomalies, and presenting the
predicted impact to a user (e.g., in near real time).
Inventors: |
NADLER; Daniel; (Cambridge,
MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
KENSHO LLC |
Cambridge |
MA |
US |
|
|
Assignee: |
KENSHO LLC
Cambridge
MA
|
Family ID: |
51059542 |
Appl. No.: |
14/279310 |
Filed: |
May 15, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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61899649 |
Nov 4, 2013 |
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61823793 |
May 15, 2013 |
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Current U.S.
Class: |
705/36R |
Current CPC
Class: |
G06Q 10/067 20130101;
G06Q 40/06 20130101 |
Class at
Publication: |
705/36.R |
International
Class: |
G06Q 40/06 20120101
G06Q040/06 |
Claims
1. A method for financial instrument return analysis comprising:
processing event data using at least one computer processor;
correlating the event data using a large volume of historical
market data to identify a predicted impact to a financial
instrument; and presenting the predicted impact to a user, wherein
the predicted impact to the financial instrument is presented
within a same day the event data was received.
2. The method of claim 1 wherein the predicted impact to a
financial instrument comprises a change to a return of the
financial instrument for an observation period.
3. The method of claim 1, wherein the event data comprises at least
one of: user entered event data to model an impact of a potential
event on a financial instrument, an actual event received from a
data feed, and an event generated by a system to model an impact of
an upcoming potential event, and an actual event entered by a
user.
4. The method of claim 1, wherein events of the event data include
at least one of geopolitical events, earnings events, weather
events or other natural world events, news events, economic data
surprises, central bank statements, central bank actions, product
releases, earnings surprises, mergers and acquisitions, IPOs,
corporate governance changes, regulatory approvals, regulatory
denials, seasonality, and surprises relative to expectations for
one or more events.
5. The method of claim 1 wherein the large volume of historical
market data comprises time series financial data.
6. The method of claim 1 wherein the predicted impact is provided
as a notification to a user.
7. The method of claim 6, wherein the notification comprises at
least one of an alert, an email, a text message, a blog post, a web
based ticker, a web based animated banner, a transmitted recorded
audio message, and an electronic notification.
8. The method of claim 6, wherein the notification contains one or
more of: a frequency of positive returns, a rank order of returns,
a number of prior observations, and a confidence indicator.
9. The method of claim 8, wherein the confidence indicator is
derived from inputs comprising one or more of: a number of
observations in the alert, a probability that a returns of assets
for a period of time are statistically anomalous compared to all
other days during the same period of time, a frequency distribution
of returns, and other relevant factors
10. The method of claim 1, further comprising: providing an
interactive analysis environment allowing development of one or
more queries.
11. The method of claim 10 wherein the interactive analysis
environment includes a natural language based query interface for
generating studies.
12. The method of claim 10 wherein the interactive analysis
environment allows generation of queries using associations between
near real time event data and historical financial data.
13. The method of claim 10, wherein the interactive analysis
environment comprises one or more templates for generating
reports.
14. The method of claim 1 wherein the identification of a predicted
impact allows a user to create and test optimal investment
strategies without programming.
15. The method of claim 1, further comprising: analyzing historical
event data to generate a set of precedent events for the event data
being processed.
16. The method of claim 15, wherein generating a set of precedent
events comprises ranking the magnitude of the event data being
processed versus the magnitude of similar historical event
data.
17. The method of claim 16, wherein ranking the magnitude of the
event data being processed comprises determining a standard
deviation of the event data being processed with respect to similar
historical event data.
18. The method of claim 15, wherein the predicted impact to the
financial instrument is determined using financial instrument
pricing anomalies associated with the set of precedent events.
19. The method of claim 18, wherein a statistical average of
pricing anomalies associated with the set of precedent events is
used to calculate the predicted impact to the financial
instrument.
20. A method for financial instrument return prediction comprising:
determining a baseline probability for at least one financial
instrument return of a financial instrument; inputting current
market data associated with the financial instrument; matching,
using at least one computer processor, one or more portions of the
current market data with historical market data; averaging outcomes
of matched historical market data; and providing a probabilistic
outcome for the at least one financial instrument return based on
the matched historical market data and the current market data.
21. The method of claim 20, wherein the return is expressed as an
overall market percentage change for the financial instrument since
the opening of the trading day.
22. The method of claim 20, wherein the current market data
comprises an amount of time left in a current trading day.
23. The method of claim 20, wherein the current market data
comprises at least one of: an indication of market volume since the
opening of the market for the financial instrument and an
indication of volatility of the financial instrument.
24. The method of claim 23, wherein the volatility comprises a
standard deviation of recent daily returns for the financial
instrument.
25. The method of claim 20, wherein the historical market data
includes at least one of: an average historical performance for a
current month of a year, an average historical performance for a
current calendar day, an average historical performance for a
numerical trading day of a week, a number of positive closes for
the financial instrument during previous trading days, and a number
of positive closes of a financial market associated with the
financial instrument during previous trading days.
26. The method of claim 20, further comprising: increasing an
amount of historical market data by identifying additional
historical market data based on a correlation of the additional
historical market data.
27. The method of claim 26, wherein the financial instrument
comprises a first financial instrument and the additional
historical market data comprises historical market data of a second
financial instrument and correlation is based upon price
behavior.
28. The method of claim 26, further comprising: setting a minimum
level of correlation required for identification of additional
historical market data.
29. The method of claim 28, wherein the minimum level of
correlation required is based at least in part on an amount
available historical data for the financial instrument.
30. The method of claim 28, wherein the minimum level of
correlation required is set statically.
31. The method of claim 27, wherein the historical market data of
the second financial instrument is weighted based on a level of
correlation to the first financial instrument.
32. The method of claim 20, wherein matching, using at least one
computer processor one or more portions of the current market data
with historical market data comprises matching on one or more
market data portions including at least one of price, minutes left
in a trading day, volume, and volatility.
33. The method of claim 31, wherein a strength of a match is
weighted based on a number of market data portions matched.
34. The method of claim 31, wherein the market data portions are
weighted individually and a strength of a match is based on which
market data portions match.
35. An article of manufacture for financial instrument return
analysis, the article of manufacture comprising: at least one
non-transitory processor readable storage medium; and instructions
stored on the at least one medium; wherein the instructions are
configured to be readable from the at least one medium by at least
one processor and thereby cause the at least one processor to
operate so as to: process event data using at least one computer
processor; correlate the event data using a large volume of
historical market data to identify a predicted impact to a
financial instrument; and present the predicted impact to a user,
wherein the predicted impact to the financial instrument is
presented within a same day the event data was received.
36. A system for financial instrument return analysis comprising:
one or more processors communicatively coupled to a network;
wherein the one or more processors are configured to: process event
data using at least one computer processor; correlate the event
data using a large volume of historical market data to identify a
predicted impact to a financial instrument; and present the
predicted impact to a user, wherein the predicted impact to the
financial instrument is presented within a same day the event data
was received.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This patent application claims priority to U.S. Provisional
Patent Application No. 61/823,793, filed May 15, 2013, which is
hereby incorporated by reference herein in its entirety.
[0002] This patent application claims priority to U.S. Provisional
Patent Application No. 61/899,649, filed Nov. 4, 2013, which is
hereby incorporated by reference herein in its entirety.
BACKGROUND
[0003] The ability to monitor, track and predict financial
instrument characteristics, including returns, is useful to make
informed decisions about such financial instruments, especially in
the service of managing risk, constructing diversified and balanced
portfolios, and identifying excess returns. Identifying, analyzing,
and conveying financial information in a meaningful and timely
manner is a challenge due to the volume of the data to be analyzed
and comprehended. Comparing financial data with non-financial
statistics (e.g., events such as for example, weather) is a
significant data management problem and challenging computational
problem.
SUMMARY OF THE DISCLOSURE
[0004] Techniques for financial instrument visualization and
modeling are disclosed. Modeling financial data to understand a
distribution of financial instrument performance has traditionally
presented a challenge (e.g., understanding returns, a probability
of returns, and pricing anomalies which arise for a plurality of
reasons but are frequently undiscovered statistically). Due to
human and interface limitations displaying a significant amount of
financial data in a timely and meaningful manner has not been
performed. Additionally, discovering, in a large volume of data,
meaningful statistical anomalies which may impact returns and
presents them in a comprehensible and timely manner is a
significant challenge. Technical considerations are also
significant and include overcoming challenges in processing large
volumes of data in a short period of time to handle
standardization, scrubbing, error correction, processing, analysis,
and modeling. In an exemplary embodiment of the present disclosure,
presenting a large amount of financial data in a timely manner
allowing visualization of a distribution of instrument performance
is provided. Event data may be received from one or more feeds and
may be processed and analyzed to provide projected outcomes based
on historical data. In some embodiments, event data may be
constructed (e.g., automatically by a system, by veteran quants,
etc.). Constructed event data may include event ranking data (e.g.,
a prioritization of historical event data due to a similarity of
historical event data to a current event, a prioritization of
historical event data due to an impact on returns or pricing caused
by the historical event, a prioritization of a historical event due
to a similarity in market conditions at a time of the historical
event and a time of the current event, and other factors).
Constructed event data may also include building associations
between historical event data based on correlations. Constructed
event data may also include building associations between events
and one or more of: asset prices, asset performance, asset returns,
and pricing anomalies associated with assets.
Large volumes of historical market data may be analyzed (e.g., time
series data) to correlate with event data (e.g., in real time or in
near real time). As actual event data is received or constructed
(e.g., for modeling), to correlate the event data with historical
event data, a set of historical event data may be defined. The set
of historical events may be derived by a level of correlation of
such historical events with the actual event. Based on a defined
set of historical events, associated asset price returns and
anomalies may be identified. These asset price returns and/or
anomalies may be used to predict an asset price return or pricing
anomaly associated with the actual event. Notifications may be
pushed or provided to present studies or likely impacts of
monitored events (e.g., financial asset performance). Events may
include for example, economic data surprises, weather anomalies,
central bank statements and actions, product releases, earnings
surprises, mergers and acquisitions and IPOs, corporate governance
changes, regulatory approvals and denials, and seasonality.
Probabilistic impacts may be provided as notifications (e.g.,
alerts, emails, a ticker or other dynamic user interface display,
and a blog post). A user may drill down on notifications to receive
further detail and access to detailed statistics (e.g., studies or
trade analysis on assets affected by an event in a notification).
Techniques may also include an interactive user interface
presenting a chart, graph, or other visualization of a large volume
of financial data ordered to illustrate a distribution indicative
of financial instrument performance. Such an interactive user
interface may provide an ability to zoom or focus on an area of a
distribution performance (e.g., via a touchpad, mouse wheel, arrow
key, function key, etc.). A user of an interactive interface may be
able to view information associated with a particular instrument
(e.g., a stock) by hovering over, mousing over, clicking on, or
otherwise indicating a portion of the user interface at a point in
the distribution where the instrument is plotted. As a user zooms
in on a segment of a distribution plotted in an interactive
interface, data for individual distribution components may become
visible (e.g., labels, equity symbols, return rates, or other
information may be plotted on a bar representing a particular
financial instrument).
[0005] In accordance with further aspects of this exemplary
embodiment, a user may also click on an indicator for a particular
financial instrument (e.g., a bar in a bar chart) and may be
presented with options and/or additional data associated with that
financial instrument. For example, a user may be presented with
options to trade the financial instrument, add the financial
instrument to a portfolio, and remove the financial instrument from
a portfolio. Additional data regarding a financial instrument and
its performance may also be displayed.
[0006] In accordance with further aspects of this exemplary
embodiment, an interactive user interface displaying a range of
distributions for financial instrument performance may also display
one or more benchmarks relative to the distribution (e.g., S&P
500). A benchmark may be plotted in a distribution and may contain
a distinctive indicator (e.g., a color, a shading, a pattern, a
symbol, etc.) so that it may be easily observed in a distribution
of a large number of financial instruments. Clicking on a benchmark
may provide further information and/or may allow a user to drill
down into a benchmark. For example, clicking on a benchmark may
allow a user to view sectors and/or individual components or
financial instruments of a benchmark.
[0007] In accordance with further aspects of this exemplary
embodiment, a distribution may use color indicators, shading,
patterns, symbols, or other indicators to indicate relative
performance in a distribution (e.g., positive returns may be green,
negative returns may be red, returns outperforming a benchmark may
be a first pattern, returns underperforming a benchmark may be a
second pattern, etc.).
[0008] Other types of visualizations may be utilized. In accordance
with another exemplary embodiment a line graph may be utilized to
visualize a distribution of results. The line graph may include
vertical or angled lines (either up or down) which may indicate
that a given asset is being held during this time period, because a
condition in a study defined by a user was active during that time
period. Perfectly horizontal lines may indicate that the given
asset is not being held by the simulated study or strategy during
this time period, because the necessary conditions defined by the
user in the study were not all active during that time period.
Therefore in the horizontal sections of the line, price changes
during that period are not contributing to the total cumulative
return or loss of the strategy, and are not counted. An individual
component or line of a graph may be highlighted and corresponding
metadata for that component may be displayed.
[0009] A line graph visualization may provide an ability for a user
to zoom in or otherwise navigate view individual component or
sector performance. Line graphs may also contain one or more
benchmarks (e.g., S&P 500) that may be provided in a different
color, a different line pattern, or with another distinctive
indicator.
[0010] In accordance with other aspects of the disclosure,
techniques for producing a study of financial instruments are
disclosed. Techniques may include the provision of templates
facilitating the querying of large amounts of financial data to
produce a visualization of a distribution of financial instrument
performance. According to some embodiments, a plurality of
templates may be provided accepting user parameters to create
studies and visualizations of financial data in near real time
and/or real time.
[0011] Techniques for financial instrument return analysis may
include analyzing one or more events (e.g., geopolitical events,
earnings events, weather or natural world events, news events,
product events, including surprises relative to expectations for
one or more types of events) to correlate one or more events with a
large volume of historical market data (e.g., time series financial
data) to identify a potential impact on at least one of: a
financial instrument, a predicted return of a financial instrument,
and performance of a financial instrument.
[0012] In accordance with further aspects of this embodiment, the
potential impact may be provided as a notification to a user (e.g.,
an alert, an email, a text message, a blog post, a web based
ticker, a web based animated banner, a transmitted recorded audio
message, or other electronic notification).
[0013] In accordance with further aspects of this embodiment, a
user-friendly interactive analysis environment may be provided. An
analysis environment may include a natural language based query
interface for generating studies.
[0014] In accordance with further aspects of this embodiment, an
analysis environment may allow the generation of queries using
associations between near real time event data and historical
impacts on financial data. Queries may be back tested against
decades of multi-asset market data.
[0015] In accordance with further aspects of this embodiment, an
analysis environment may contain one or more templates for
generating studies or reports. Templates may use analysis performed
by veteran quants.
[0016] In accordance with further aspects of this embodiment,
identification of impacts may allow a user to create and test
optimal investment strategies without depending on software
engineers or quants.
[0017] Techniques for financial instrument attribute prediction and
financial instrument visualization are disclosed. In one exemplary
embodiment, the techniques may be realized as a method for
financial instrument attribute prediction including determining a
baseline probability for at least one financial instrument
attribute of a financial instrument, inputting current market data
associated with the financial instrument, matching, using at least
one computer processor one or more portions of the current market
data with historical market data, averaging outcomes of matched
historical market data, and providing a probabilistic outcome for
the at least one financial instrument attribute based on the
matched historical market data and the current market data.
[0018] In accordance with further aspects of this exemplary
embodiment, the financial instrument attribute may be price.
[0019] In accordance with further aspects of this exemplary
embodiment, the price may be expressed as an overall market
percentage change for the financial instrument since the opening of
the trading day.
[0020] In accordance with further aspects of this exemplary
embodiment, the current market data may include an amount of time
left in a current trading day.
[0021] In accordance with further aspects of this exemplary
embodiment, the current market data may include at least one of: an
indication of market volume since the opening of the market for the
financial instrument and an indication of volatility of the
financial instrument.
[0022] In accordance with further aspects of this exemplary
embodiment, the volatility may be a standard deviation of recent
daily returns for the financial instrument.
[0023] In accordance with further aspects of this exemplary
embodiment, the historical market data may include at least one of:
an average historical performance for a current month of a year, an
average historical performance for a current calendar day, an
average historical performance for a numerical trading day of a
week, a number of positive closes for the financial instrument
during previous trading days, and a number of positive closes of a
financial market associated with the financial instrument during
previous trading days. In some embodiments, historical performance
may include an arbitrary time during the history of a financial
instrument's trading.
[0024] In accordance with further aspects of this exemplary
embodiment, the techniques may include increasing an amount of
historical market data by identifying additional historical market
data based on a correlation of the additional historical market
data.
[0025] In accordance with further aspects of this exemplary
embodiment, the financial instrument may include a first financial
instrument and the additional historical market data may comprise
historical market data of a second financial instrument and
correlation is based upon price behavior.
[0026] In accordance with further aspects of this exemplary
embodiment, the techniques may further include setting a minimum
level of correlation required for identification of additional
historical market data.
[0027] In accordance with further aspects of this exemplary
embodiment, the minimum level of correlation required may be based,
at least in part, on an amount of available historical market data
for the financial instrument.
[0028] In accordance with further aspects of this exemplary
embodiment, the minimum level of correlation required may be set
statically.
[0029] In accordance with further aspects of this exemplary
embodiment, the historical market data of the second financial
instrument may be weighted based on a level of correlation to the
first financial instrument.
[0030] In accordance with further aspects of this exemplary
embodiment, matching, using at least one computer processor one or
more portions of the current market data with historical market
data may include matching on one or more market data portions
including at least one of price, minutes left in a trading day (or
another period of time left or elapsed in a trading session such
as, for example, hours or seconds remaining in a trading day or
elapsed since an opening of a trading session), volume, and
volatility.
[0031] In accordance with further aspects of this exemplary
embodiment, a strength of a match may be weighted based on a number
of market data portions matched.
[0032] In accordance with further aspects of this exemplary
embodiment, the market data portions may be weighted individually
and a strength of a match may be based on which market data
portions match.
[0033] In accordance with further aspects of this exemplary
embodiment, the techniques may comprise as an article of
manufacture for financial instrument attribute prediction, the
article of manufacture including at least one non-transitory
processor readable storage medium and instructions stored on the at
least one medium. The instructions may be configured to be readable
from the at least one medium by at least one processor and thereby
cause the at least one processor to operate so as to determine a
baseline probability for at least one financial instrument
attribute of a financial instrument, input current market data
associated with the financial instrument, match one or more
portions of the current market data with historical market data,
average outcomes of matched historical market data, and provide a
probabilistic outcome for the at least one financial instrument
attribute based on the matched historical market data and the
current market data.
[0034] In accordance with further aspects of this exemplary
embodiment, the techniques may comprise as a system for financial
instrument attribute prediction comprising one or more processors
communicatively coupled to a network. The one or more processors
may be configured to determine a baseline probability for at least
one financial instrument attribute of a financial instrument, input
current market data associated with the financial instrument, match
one or more portions of the current market data with historical
market data, average outcomes of matched historical market data,
and provide a probabilistic outcome for the at least one financial
instrument attribute based on the matched historical market data
and the current market data.
[0035] The present disclosure will now be described in more detail
with reference to exemplary embodiments thereof as shown in the
accompanying drawings. While the present disclosure is described
below with reference to exemplary embodiments, it should be
understood that the present disclosure is not limited thereto.
Those of ordinary skill in the art having access to the teachings
herein will recognize additional implementations, modifications,
and embodiments, as well as other fields of use, which are within
the scope of the present disclosure as described herein, and with
respect to which the present disclosure may be of significant
utility.
BRIEF DESCRIPTION OF THE DRAWINGS
[0036] In order to facilitate a fuller understanding of the present
disclosure, reference is now made to the accompanying drawings, in
which like elements are referenced with like numerals. These
drawings should not be construed as limiting the present
disclosure, but are intended to be exemplary only.
[0037] FIG. 1 shows a block diagram depicting a network
architecture 100 for financial instrument attribute prediction and
attribute visualization, in accordance with an embodiment of the
present disclosure.
[0038] FIG. 2 depicts a block diagram of a computer system in
accordance with an embodiment of the present disclosure.
[0039] FIG. 3 shows a module for financial instrument attribute
prediction and attribute visualization, in accordance with an
embodiment of the present disclosure.
[0040] FIG. 4A depicts a method for financial instrument attribute
prediction and attribute visualization, in accordance with an
embodiment of the present disclosure.
[0041] FIG. 4B depicts a method for analyzing event data to predict
an impact on the performance of an asset, in accordance with an
embodiment of the disclosure.
[0042] FIG. 5 depicts a user interface for pushing statistical
market content to a user, in accordance with an embodiment of the
disclosure.
[0043] FIG. 6 depicts a user interface for pushing statistical
market content to a user, in accordance with an embodiment of the
disclosure.
[0044] FIG. 7 depicts a user interface for pushing statistical
market content to a user, in accordance with an embodiment of the
disclosure.
[0045] FIG. 8 depicts a detailed report provided via a
notification, in accordance with an embodiment of the
disclosure.
[0046] FIG. 9 depicts a detailed report chart provided via a
notification, in accordance with an embodiment of the
disclosure.
[0047] FIG. 10 depicts a detailed report chart provided via a
notification, in accordance with an embodiment of the
disclosure.
[0048] FIG. 11 shows a listing of study results associated with an
event notification, in accordance with an embodiment of the
disclosure.
[0049] FIG. 12 shows a trade history associated with an event
notification, in accordance with an embodiment of the
disclosure.
[0050] FIG. 13 depicts a listing of trading ranges of assets in a
study, in accordance with an embodiment of the disclosure.
[0051] FIG. 14 depicts a menu for selecting events for analysis, in
accordance with an embodiment of the disclosure.
[0052] FIG. 15 depicts a help menu on an event analysis user
interface, in accordance with an embodiment of the disclosure.
[0053] FIG. 16 depicts a help menu on an event analysis user
interface, in accordance with an embodiment of the disclosure.
[0054] FIG. 17 depicts a help menu on an event analysis user
interface, in accordance with an embodiment of the disclosure.
[0055] FIGS. 18A and 18B show a user interface controls for pushing
statistical market content to a user, in accordance with an
embodiment of the disclosure.
[0056] FIG. 19 depicts an event analysis user interface, in
accordance with an embodiment of the disclosure.
[0057] FIG. 20 depicts a method for establishing baseline
probabilities for financial instrument attributes, in accordance
with an embodiment of the present disclosure.
[0058] FIG. 21 shows a method for gathering financial marketplace
data, in accordance with an embodiment of the present
disclosure.
[0059] FIG. 22 depicts a method for identifying relevant financial
marketplace data, in accordance with an embodiment of the present
disclosure.
[0060] FIGS. 23A-23J depict a user interface for viewing predicted
financial instrument attributes, in accordance with an embodiment
of the present disclosure.
[0061] FIG. 24 depicts a process flow for a method of financial
instrument attribute prediction, in accordance with an embodiment
of the present disclosure.
[0062] FIGS. 25A-D depict a user interface for financial instrument
visualization, in accordance with an embodiment of the present
disclosure.
[0063] FIG. 26 depicts a user interface illustrating a tradeoff
between risk correlated to a market and returns in excess of the
market, in accordance with an embodiment of the present
disclosure.
[0064] FIG. 27 depicts a user interface illustrating a scatterplot
of financial instruments charted along a tradeoff between risk
correlated to a market and returns in excess of the market, in
accordance with an embodiment of the present disclosure.
[0065] FIG. 28 depicts a user interface illustrating a scatterplot
of financial instruments charted along a tradeoff between risk
correlated to a market and returns in excess of the market, in
accordance with an embodiment of the present disclosure.
[0066] FIG. 29 shows a user interface for evaluating the
performance of a plurality of financial instruments, in accordance
with an embodiment of the present disclosure.
[0067] FIG. 30 shows a user interface for evaluating the
performance of a financial instrument, in accordance with an
embodiment of the present disclosure.
[0068] FIG. 31 depicts a user interface for evaluating the
performance of a financial instrument, in accordance with an
embodiment of the present disclosure.
[0069] FIG. 32 depicts a user interface for evaluating the
performance of a financial instrument, in accordance with an
embodiment of the present disclosure.
[0070] FIG. 33 depicts a user interface for evaluating the
performance of a financial instrument, in accordance with an
embodiment of the present disclosure.
[0071] FIG. 34 depicts a user interface for evaluating the
performance of a financial instrument, in accordance with an
embodiment of the present disclosure.
[0072] FIG. 35 depicts a user interface for evaluating the
performance of a financial instrument, in accordance with an
embodiment of the present disclosure.
[0073] FIG. 36 depicts a user interface for evaluating the
performance of a financial instrument, in accordance with an
embodiment of the present disclosure.
[0074] FIG. 37 depicts a user interface for evaluating the
performance of a financial instrument, in accordance with an
embodiment of the present disclosure.
[0075] FIG. 38 depicts a user interface for evaluating the
performance of a financial instrument, in accordance with an
embodiment of the present disclosure.
[0076] FIG. 39 depicts a user interface for evaluating the
performance of a financial instrument, in accordance with an
embodiment of the present disclosure.
[0077] FIG. 40 depicts a user interface for evaluating the
performance of a financial instrument, in accordance with an
embodiment of the present disclosure.
[0078] FIG. 41 depicts a user interface for evaluating the
performance of a financial instrument, in accordance with an
embodiment of the present disclosure.
[0079] FIG. 42 depicts a user interface for embedding within or
associating with another user interface, in accordance with an
embodiment of the present disclosure.
[0080] FIG. 43 depicts an embodiment of a user interface utilizing
a Z axis to depict a metric of market Beta in relation to risk and
return, in accordance with an embodiment of the present
disclosure.
[0081] FIG. 44 depicts a user interface for navigating studies of
financial instruments, in accordance with an embodiment of the
present disclosure.
[0082] FIG. 45 depicts a user interface for navigating studies of
financial instruments, in accordance with an embodiment of the
present disclosure.
[0083] FIG. 46 depicts a user interface for viewing details of a
study of financial instruments, in accordance with an embodiment of
the present disclosure.
[0084] FIG. 47 depicts a user interface for a financial instrument
visualization, in accordance with an embodiment of the present
disclosure.
[0085] FIG. 48 depicts a user interface for a financial instrument
visualization, in accordance with an embodiment of the present
disclosure.
[0086] FIG. 49 depicts a user interface for viewing component
information of a financial instrument visualization, in accordance
with an embodiment of the present disclosure.
[0087] FIG. 50 depicts a user interface for viewing component
information of a financial instrument visualization, in accordance
with an embodiment of the present disclosure.
[0088] FIG. 51 depicts a user interface for focusing a financial
instrument visualization, in accordance with an embodiment of the
present disclosure.
[0089] FIG. 52 depicts a user interface for focusing a financial
instrument visualization, in accordance with an embodiment of the
present disclosure.
[0090] FIG. 53 depicts a user interface for focusing a financial
instrument visualization, in accordance with an embodiment of the
present disclosure.
[0091] FIG. 54 depicts a user interface for viewing financial
instrument visualization component details, in accordance with an
embodiment of the present disclosure.
[0092] FIG. 55 depicts a user interface for creating a study of
financial instruments, in accordance with an embodiment of the
present disclosure.
[0093] FIG. 56 depicts a user interface for account access, in
accordance with an embodiment of the present disclosure.
[0094] FIG. 57 depicts a user interface for creating a study of
financial instruments, in accordance with an embodiment of the
present disclosure.
[0095] FIG. 58 depicts a user interface for creating a study of
financial instruments, in accordance with an embodiment of the
present disclosure.
[0096] FIG. 59 depicts a user interface for creating a study of
financial instruments, in accordance with an embodiment of the
present disclosure.
[0097] FIG. 60 depicts a user interface for entering parameters for
creating a study of financial instruments, in accordance with an
embodiment of the present disclosure.
[0098] FIG. 61 depicts a user interface for entering parameters for
creating a study of financial instruments, in accordance with an
embodiment of the present disclosure.
[0099] FIG. 62 depicts a user interface for creating a study of
financial instruments, in accordance with an embodiment of the
present disclosure.
[0100] FIG. 63 depicts a user interface for creating a study of
financial instruments, in accordance with an embodiment of the
present disclosure.
[0101] FIG. 64 depicts a user interface for creating a study of
financial instruments, in accordance with an embodiment of the
present disclosure.
[0102] FIG. 65 depicts a user interface for creating a study of
financial instruments, in accordance with an embodiment of the
present disclosure.
[0103] FIG. 66 depicts a user interface for a financial instrument
visualization, in accordance with an embodiment of the present
disclosure.
[0104] FIG. 67 depicts a user interface for a financial instrument
visualization, in accordance with an embodiment of the present
disclosure.
[0105] FIG. 68 depicts a user interface for a financial instrument
visualization, in accordance with an embodiment of the present
disclosure.
[0106] FIG. 69 depicts a user interface for a financial instrument
visualization, in accordance with an embodiment of the present
disclosure.
[0107] FIG. 70 depicts a user interface for a financial instrument
visualization, in accordance with an embodiment of the present
disclosure.
[0108] FIG. 71 depicts a platform for financial instrument
visualization and modeling, in accordance with an embodiment of the
present disclosure.
[0109] FIG. 72 depicts a platform for correlation of non-asset
metrics to asset prices and metrics, in accordance with an
embodiment of the disclosure.
[0110] FIG. 73 depicts a platform for dynamic resharding of data
based on demand, in accordance with an embodiment of the
disclosure.
[0111] FIG. 74 depicts a user interface for pushing statistical
market content to a user, in accordance with an embodiment of the
disclosure.
[0112] FIG. 75 depicts a user interface for pushing statistical
market content to a user, in accordance with an embodiment of the
disclosure.
[0113] FIG. 76 depicts a user interface for modeling the impact of
breaking political events, in accordance with an embodiment.
[0114] FIG. 77 depicts a user interface for modeling the impact of
breaking political events, in accordance with an embodiment.
[0115] FIG. 78 depicts a user interface for modeling the impact of
breaking political events, in accordance with an embodiment.
[0116] FIG. 79 depicts a user interface for modeling the impact of
breaking political events, in accordance with an embodiment.
[0117] FIG. 80 depicts a user interface for modeling the impact of
breaking political events, in accordance with an embodiment.
[0118] FIG. 81 depicts a notification modeling a market impact of a
potential event, in accordance with an embodiment.
[0119] FIG. 82 depicts a notification modeling a market impact of a
potential event, in accordance with an embodiment.
[0120] FIG. 83 depicts a notification modeling a market impact of a
potential event, in accordance with an embodiment.
[0121] FIG. 84 depicts a notification modeling a market impact of a
potential event, in accordance with an embodiment.
[0122] FIG. 85 depicts a notification modeling a market impact of a
potential event, in accordance with an embodiment.
[0123] FIG. 86 depicts a notification modeling a market impact of a
potential event, in accordance with an embodiment.
[0124] FIG. 87 illustrates a user interface for modeling consensus
and surprise analysis, in accordance with an embodiment.
[0125] FIG. 88 depicts a user interface for economic regime
analysis in accordance with an embodiment.
[0126] FIG. 89 depicts illustrates a user interface for modeling
consensus and surprise analysis, in accordance with an
embodiment.
[0127] FIG. 90 depicts a user interface for economic regime
analysis in accordance with an embodiment.
DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS
[0128] The present disclosure relates to systems for and methods of
financial instrument attribute prediction and financial instrument
visualization. According to some embodiments, a real-time
performance evaluation and monitoring system may include providing
a probability of a financial instruments price change based at
least in part on historical and current market data. In one or more
embodiments, financial instrument visualization may provide charts
and analysis depicting variance in financial instrument returns
versus an annualized return. Accurate estimations of the
near-future performance of a financial instrument may help the
owner or a financial instrument trader evaluate the risks and
benefits of holding the financial instrument. The near-future
performance of a financial instrument may be determined by way of
mathematical models and a high-speed computational process, system,
and method that may utilize extremely large historical market
data-sets in real-time.
[0129] Turning now to the drawings, FIG. 1 shows a block diagram
depicting a network architecture 100 for financial instrument
attribute prediction and attribute visualization, in accordance
with an embodiment of the present disclosure. FIG. 1 is a
simplified view of network architecture 100, which may include
additional elements that are not depicted. Network architecture 100
may contain client systems 110 and 120, as well as servers 140A and
140B (one or more of which may be implemented using computer system
200 shown in FIG. 2). Client systems 110 and 120 may be
communicatively coupled to a network 190. Server 140A may be
communicatively coupled to storage devices 160A(1)-(N), and server
140B may be communicatively coupled to storage devices 160B(1)-(N).
Servers 140A and 140B may contain a management module (e.g., Data
Analysis and Visualization Module 154). Data providers 192(1)-(N)
may be communicatively coupled to network 190.
[0130] With reference to computer system 200 of FIG. 2, modem 247,
network interface 248, or some other method may be used to provide
connectivity from one or more of client systems 110 and 120 to
network 190. Client systems 110 and 120 may be able to access
information on server 140A or 140B using, for example, a web
browser or other client software (not shown) as a platform. Such a
platform may allow client systems 110 and 120 to access data hosted
by server 140A or 140B or one of storage devices 160A(1)-(N) and/or
160B(1)-(N).
[0131] Network 190 may be a local area network (LAN), a wide area
network (WAN), the Internet, a cellular network, a satellite
network, or other networks that permit communication between
clients 110, 120, servers 140, and other devices communicatively
coupled to network 190. Network 190 may further include one, or any
number, of the exemplary types of networks mentioned above
operating as a stand-alone network or in cooperation with each
other. Network 190 may utilize one or more protocols of one or more
clients or servers to which they are communicatively coupled.
Network 190 may translate to or from other protocols to one or more
protocols of network devices. Although network 190 is depicted as
one network, it should be appreciated that according to one or more
embodiments, network 190 may comprise a plurality of interconnected
networks.
[0132] Storage devices 160A(1)-(N) and/or 160B(1)-(N) may be
network accessible storage and may be local, remote, or a
combination thereof to server 140A or 140B. Storage devices
160A(1)-(N) and/or 160B(1)-(N) may utilize a redundant array of
inexpensive disks ("RAID"), magnetic tape, disk, a storage area
network ("SAN"), an internet small computer systems interface
("iSCSI") SAN, a Fibre Channel SAN, a common Internet File System
("CIFS"), network attached storage ("NAS"), a network file system
("NFS"), optical based storage, or other computer accessible
storage. Storage devices 160A(1)-(N) and/or 160B(1)-(N) may be used
for backup or archival purposes.
[0133] According to some embodiments, clients 110 and 120 may be
smartphones, PDAs, desktop computers, a laptop computers, servers,
other computers, or other devices coupled via a wireless or wired
connection to network 190. Clients 110 and 120 may receive data
from user input, a database, a file, a web service, and/or an
application programming interface.
[0134] Servers 140A and 140B may be application servers, archival
platforms, backup servers, network storage devices, media servers,
email servers, document management platforms, enterprise search
servers, databases or other devices communicatively coupled to
network 190. Servers 140A and 140B may utilize one of storage
devices 160A(1)-(N) and/or 160B(1)-(N) for the storage of
application data, backup data, or other data. Servers 140A and 140B
may be hosts, such as an application server, which may process data
traveling between clients 110 and 120 and a backup platform, a
backup process, and/or storage. According to some embodiments,
servers 140A and 140B may be platforms used for backing up and/or
archiving data. One or more portions of data may be backed up or
archived based on a backup policy and/or an archive applied,
attributes associated with the data source, space available for
backup, space available at the data source, or other factors.
[0135] Data providers 192(1)-(N) may provide financial instrument
data from one or more sources. According to some embodiments, data
providers 192(1)-(N) may be external financial instrument market
data providers (e.g., Interactive Data Corporation, Image Master,
or another financial market data provider). Data providers
192(1)-(N) may provide one or more interfaces, filters, converters,
formatting modules, or other data processing components to prepare
data for Server 140 and/or Server 140B. Data may be provided
periodically (e.g., daily, hourly, real time, or other increments),
in batch or bulk, in response to a query or request (e.g.,
initiated by Server 140A), or event driven (e.g., in response to
market opening).
[0136] According to some embodiments, clients 120 and 130 may be
mobile devices and Data Analysis and Visualization Module 154 may
be implemented on one or more mobile platforms including, but not
limited to Android, iOS, WebOS, Windows Mobile, Blackberry OS, and
Symbian. Data Analysis and Visualization Module 154 may be
implemented on top of one or more platforms such as, for example,
Internet Explorer, FireFox, Chrome, and Safari. In some
embodiments, Data Analysis and Visualization Module 154 may
implemented on a desktop client.
[0137] In some embodiments, Data Analysis and Visualization Module
154 may provide real-time probabilistic predictions of financial
instrument price changes. For example, data analysis and
visualization module 154 may calculate real-time changing odds
(over the course of a trading session or a different time period)
that a given financial instrument will close positive by the end of
its trading session or another time period. Data Analysis and
Visualization Module 154 may incorporate 1) real-time price and
live back-testing of the probability of a price reversal for a
particular financial instrument under similar historical
conditions, including, for example, A) an amount of time left in
the trading day, and B) how much a ticker for the financial
instrument has already gained or lost over the day; 2) the
historical odds of closing positive on this particular calendar
date, and 3) the back-tested historical odds of a positive day
today as a function of the performance of the previous trading
days.
[0138] In some embodiments, data analysis and visualization module
154 may provide a user interface to model one or more economic
scenarios. For example, a user may select one or more values for a
macroeconomic environment to query how asset prices historically
performed under a similar set of conditions. Financial analysts,
investors, economists, researchers and other market participants
may want to understand how macroeconomic variables have affected
asset prices in the past, in order, for example, to inform views
about possible future trends. Current research tools do not permit
rapid discovery of prevailing historic economic conditions. Current
research tools do not allow interactive backtesting to calculate
the performance of a large (e.g., n>1000) basket of assets
during periods in which those conditions obtained.
[0139] In some embodiments, a user interface provided by data
analysis and visualization module 154 may allow a user to select
one or more combinations of past economic variables for a query by
use of simple onscreen sliders. A query may obtain confirmation
(e.g., provided in near real time) of how many days existed during
which the selected combinations of past economic variables
exhibited the selected values, and then generate a backtesting
model on one or more baskets of assets that calculates the assets'
performance during those days. The baskets can contain an arbitrary
number of assets.
[0140] In addition to probabilistic predictions, according to some
embodiments, data analysis and visualization module 154 may provide
a real-time performance evaluation and monitoring system for
financial instruments. A financial instrument's probability of a
given price change may be calculated using one or more of a
plurality of inputs. Each input may correspond to one of a
plurality of present or historical data points. Data analysis and
visualization module 154 may provide a real-time monitoring and
visualization system for financial instrument performance. Data
analysis and visualization module 154 may include, for example, one
or more of monitoring, recording, and comparing to historical data
at least one of price metrics, volatility metrics, volume metrics,
time left in trading day metrics, overall market metrics, and
cross-instrument correlation metrics for a financial instrument.
Data for metrics being monitored by data analysis and visualization
module 154 may be stored in a database or other electronic storage,
and a visualization of the metrics may be displayed or otherwise
output.
[0141] In one or more embodiments, multiple dimensions of
probability data associated with a future performance of a
financial instrument may be presented to a user in a concise manner
by data analysis and visualization module 154. Numerical odds
ratios may be used to display probability data associated with the
future performance of a financial instrument so that a user can
identify and understand hidden patterns and information in the
financial data associated with the financial instrument. Data
analysis and visualization module 154 may model systems using
multi-factor and multi-dimensional probabilistic models and more
particularly to the display of probabilities associated with
multi-factor and multi-dimensional probabilistic models.
[0142] Data analysis and visualization module 154 may determine the
conditional probabilities associated with the near-future
performance of a financial instrument. The interplay of multiple
present and historical dimensions of data, such as price metrics,
volatility metrics, volume metrics, time left in trading day
metrics, overall market metrics, and cross-instrument correlation
metrics may be factored to yield a more accurate forecast of the
near-future performance of a financial instrument.
[0143] Data analysis and visualization module 154 may provide
information visualization by graphically representing data
according to a method or scheme. A graphical representation of data
resulting from an information visualization technique may be called
a visualization. Exemplary visualizations may include scatterplots,
pie charts, treemaps, bar charts, graphs, histograms, and so
on.
[0144] Data analysis and visualization module 154 may facilitate
visualizing complex financial data sets, where visually striking
and useful displays may improve business operations, economic
forecasting, and so on. For example, financial data may be any
information pertaining to a business operation or financial
transaction(s). Financial data may include, for example, financial
instrument prices, measures of financial instrument volatility,
such as the standard deviation of returns over some period,
measures of return of a financial instrument, such as annualized
return, market data, and so on.
[0145] Data analysis and visualization module 154 may provide
visualization and interaction with financial data using scatterplot
visualizations. For example, data may be grouped according to two
or more specified dimensions and determining one or more
hierarchical, relational, spatial, relative, or temporal,
relationships between the two or more user-specified dimensions. A
position of a financial instrument intersecting an X and a Y axis
may be depicted in a first order based on the one or more metrics
measuring the relationships between return and risk associated with
the financial instrument. In an illustrative embodiment, the data
includes financial data. Data analysis and visualization module 154
may automatically visually highlight a featured financial
instrument's placement along the spatial relation between risk and
return. A first user option may enable a user to selectively
visually query the identity of the financial instrument in the
scatterplot space, as well as the data associated with its
placement along the spatial relation between risk and return. A
second user option may enable a user to selectively visually query
the identity of comparative financial instruments in the
scatterplot space, as well as the data associated with their
placement along the spatial relation between risk and return.
Additional user options may enable a user to select or input the
time horizon and/or calculation method on the basis of which return
is measured. Further user options may enable a user to select or
input the time horizon and/or calculation method on the basis of
which risk is measured. Further user options may enable a user to
click, tap, or drag and select a region of the risk-return
scatterplot and have the scatterplot dynamically `zoom` to that
region and automatically re-size such that that region becomes the
entirety, or a different proportion, of the display of the
scatterplot and such that the scatterplot dynamically populates
additional financial instruments at the higher level of resolution.
Further user options may enable the reverse process (e.g., a user
may remove a focus or zoom out to see a greater number of financial
instruments). A permutation of this embodiment involves the
interaction being a touch screen motion, including but not limited
to the touch screen motion being some sort of pinch open and pinch
close. A permutation of this embodiment involves the interaction
being a hand gesture via a device that translates the hand-gesture
into the exploration of a spatial representation of the relation
between risk and return on the scatterplot.
[0146] One or more of the above interface embodiments may utilize
hand gestures that translate into controls for exploration of a
spatial representation of a relation between risk and return on a
scatterplot.
[0147] A permutation of some embodiments involves the
possibility/option of adding a Z axis to one or more of the above
described processes and/or options to create a three dimensions
spatial representation of the relation between risk and return in a
financial instrument, where the Z axis=some additional and/or
different metric of risk; some additional and/or different metric
of return, and/or some additional or different metric, including,
but not limited to: a metric of time, a metric of market Alpha, a
metric of market Beta, some other metric of correlation (including
a dynamic correlation) to one or more financial instruments; a
metric of volatility, a metric of volume, a metric of market
capitalization. An embodiment of a user interface utilizing a Z
axis to depict a metric of market Beta in relation to risk and
return is illustrated in FIG. 43.
[0148] Returning to FIG. 1, in one or more embodiments, the data
includes financial data. Data analysis and visualization module 154
may automatically visually highlight a placement of a featured
financial instruments, a placement of a portfolio, which the user
might import and/or construct via selection, or a placement of a
financial strategy along the spatial relation between Alpha and
Beta.
[0149] Beta may be exposure to the global market portfolio. And,
any expected return from exposure to a risk uncorrelated with this
portfolio may be Alpha. Returns may exist along a continuum--from
Beta, to exotic Beta and ultimately, to Alpha. By optimizing this
spectrum of return sources, investors can achieve a more efficient
portfolio. Portfolios may contain a complete spectrum of return
sources.
[0150] Additional user options may enable a user to select or input
the time horizon and/or calculation method on the basis of which
Alpha is measured. Further user options may enable a user to select
or input the time horizon and/or calculation method on the basis of
which Beta is measured.
[0151] One or more embodiments may provide financial instrument
visualization technology including a fully-featured risk
management, risk analysis, and statistical arbitrage system.
Functionality may include portfolio analysis (including portfolio
importing functionality) which may aide diversification in
portfolio construction, management, and maintenance of portfolios.
Visualization technology may incorporate, extend, and visualize
risk analysis principles. Visualization may be more important
across large data sets, which are traditionally more difficult to
analyze and comprehend. Visualization technology may also provide
analysis and user interfaces to comprehend real time data. Some
embodiments may provide dynamic interaction with models in real
time and may incorporate multivariate interactivity. A user may be
able to change multiple inputs to query and to model effects on a
portfolio in real time.
[0152] An exemplary user interface produced by Data analysis and
visualization module 154 may include FIG. 26. FIG. 26 depicts a
user interface illustrating a tradeoff between risk correlated to a
market and returns in excess of the market. Another exemplary user
interface produced by Data analysis and visualization module 154
may include FIG. 27. FIG. 27 depicts a user interface illustrating
a scatterplot of financial instruments charted along a tradeoff
between risk correlated to a market and returns in excess of the
market. Yet another exemplary user interface produced by Data
analysis and visualization module 154 may include FIG. 28. FIG. 28
depicts a user interface illustrating a scatterplot of financial
instruments charted along a tradeoff between risk correlated to a
market and returns in excess of the market.
[0153] Further user options may enable a user to drag and select a
region of the Alpha-Beta scatterplot and have the scatterplot
dynamically `zoom` to that region and automatically re-size such
that that region becomes the entirety, or a different proportion,
of the scatterplot and such that the scatterplot dynamically
populates additional financial instruments at the higher level of
resolution. Further user options may enable the reverse process
(e.g., a user may remove a focus or zoom out to see a greater
number of financial instruments). A permutation of this embodiment
involves the interaction being a touch screen motion, including but
not limited to the touch screen motion being some sort of pinch
open and pinch close. A permutation of this embodiment involves the
interaction being a hand gesture via a device that translates the
hand-gesture into the exploration of a spatial representation of
the relation between Alpha and Beta on the scatterplot.
[0154] One or more of the above interface embodiments may utilize
hand gestures that translate into controls for exploration of a
spatial representation of a relation between risk and return on a
scatterplot.
[0155] A permutation of this embodiment may involve the
possibility/option of adding a Z axis to one or more of the above
described processes and/or options to create a three dimensions
spatial representation of the relation between Alpha and Beta in a
financial instrument, where the Z axis=some additional and/or
different metric of Alpha; some additional and/or different metric
of Beta, and/or some additional or different metric, including, but
not limited to: a metric of time, another metric of market risk,
another metric of market return, some other metric of correlation
(including a dynamic correlation) to one or more financial
instruments; a metric of volatility, a metric of volume, a metric
of market capitalization. An embodiment of a user interface
utilizing a Z axis to depict a metric of market Beta in relation to
risk and return is illustrated in FIG. 43.
[0156] Returning to FIG. 1, Data analysis and visualization module
154 may provide user options allowing a user to adjust a scale of
risk and return axis, and some embodiments may dynamically populate
a scatter plot with additional financial instruments as the scale
of risk and return changes. Additional user options may enable a
user to trigger tabular view of underlying data or provide other
visualization options. In a specific embodiment, a scatterplot of
Data analysis and visualization module 154 may depict metrics for
the risk and return of financial instruments as X and Y axis.
[0157] According to some embodiments, a user interface may be a
scatterplot depicting a user specified portfolio. For example, a
user portfolio may be imported and plotted along axis similar to
those depicted in exemplary FIGS. 26-28. A user portfolio may be
selected by a user from one or more menus or user controls (e.g.,
drop downs, picklists, search interfaces, etc.). A user portfolio
may also be imported (e.g., via a secure and/or authenticated
interface to a bank or other financial institution, via a data
file, or via another specified format). A user portfolio may be
compared against benchmarks, baselines, and/or comparative plots
(e.g., indices, commodities, sectors, and index components).
Changes over time may be illustrated on a user interface (e.g.,
change of a user portfolio over time versus one or more of indices,
commodities, sectors, and index components).
[0158] FIG. 2 depicts a block diagram of a computer system 200 in
accordance with an embodiment of the present disclosure. Computer
system 200 is suitable for implementing techniques in accordance
with the present disclosure. Computer system 200 may include a bus
212 which may interconnect major subsystems of computer system 210,
such as a central processor 214, a system memory 217 (e.g. RAM
(Random Access Memory), ROM (Read Only Memory), flash RAM, or the
like), an Input/Output (I/O) controller 218, an external audio
device, such as a speaker system 220 via an audio output interface
222, an external device, such as a display screen 224 via display
adapter 226, serial ports 228 and 230, a keyboard 232 (interfaced
via a keyboard controller 233), a storage interface 234, a floppy
disk drive 237 operative to receive a floppy disk 238, a host bus
adapter (HBA) interface card 235A operative to connect with a Fibre
Channel network 290, a host bus adapter (HBA) interface card 235B
operative to connect to a SCSI bus 239, and an optical disk drive
240 operative to receive an optical disk 242. Also included may be
a mouse 246 (or other point-and-click device, coupled to bus 212
via serial port 228), a modem 247 (coupled to bus 212 via serial
port 230), network interface 248 (coupled directly to bus 212),
power manager 250, and battery 252.
[0159] Bus 212 allows data communication between central processor
214 and system memory 217, which may include read-only memory (ROM)
or flash memory (neither shown), and random access memory (RAM)
(not shown), as previously noted. The RAM may be the main memory
into which the operating system and application programs may be
loaded. The ROM or flash memory can contain, among other code, the
Basic Input-Output system (BIOS) which controls basic hardware
operation such as the interaction with peripheral components.
Applications resident with computer system 210 may be stored on and
accessed via a computer readable medium, such as a hard disk drive
(e.g., fixed disk 244), an optical drive (e.g., optical drive 240),
a floppy disk unit 237, or other storage medium. For example, Data
Analysis and Visualization Module 154 may be resident in system
memory 217.
[0160] Storage interface 234, as with the other storage interfaces
of computer system 210, can connect to a standard computer readable
medium for storage and/or retrieval of information, such as a fixed
disk drive 244. Fixed disk drive 244 may be a part of computer
system 210 or may be separate and accessed through other interface
systems. Modem 247 may provide a direct connection to a remote
server via a telephone link or to the Internet via an internet
service provider (ISP). Network interface 248 may provide a direct
connection to a remote server via a direct network link to the
Internet via a POP (point of presence). Network interface 248 may
provide such connection using wireless techniques, including
digital cellular telephone connection, Cellular Digital Packet Data
(CDPD) connection, digital satellite data connection or the
like.
[0161] Many other devices or subsystems (not shown) may be
connected in a similar manner (e.g., document scanners, digital
cameras and so on). Conversely, all of the devices shown in FIG. 2
need not be present to practice the present disclosure. The devices
and subsystems can be interconnected in different ways from that
shown in FIG. 2. Code to implement the present disclosure may be
stored in computer-readable storage media such as one or more of
system memory 217, fixed disk 244, optical disk 242, or floppy disk
238. Code to implement the present disclosure may also be received
via one or more interfaces and stored in memory. The operating
system provided on computer system 210 may be MS-DOS.RTM.,
MS-WINDOWS.RTM., OS/2.RTM., OS X.RTM., UNIX.RTM., Linux.RTM.,
another known operating system, a custom operating system, or a
proprietary operating system.
[0162] Power manager 250 may monitor a power level of battery 252.
Power manager 250 may provide one or more APIs (Application
Programming Interfaces) to allow determination of a power level, of
a time window remaining prior to shutdown of computer system 200, a
power consumption rate, an indicator of whether computer system is
on mains (e.g., AC Power) or battery power, and other power related
information. According to some embodiments, APIs of power manager
250 may be accessible remotely (e.g., accessible to a remote backup
management module via a network connection). According to some
embodiments, battery 252 may be an Uninterruptable Power Supply
(UPS) located either local to or remote from computer system 200.
In such embodiments, power manager 250 may provide information
about a power level of an UPS.
[0163] Referring to FIG. 3, there is shown a Data analysis and
visualization module 154 in accordance with an embodiment of the
present disclosure. As illustrated, the financial instrument
attribute prediction and attribute visualization module 154 may
contain one or more components including baseline probability
generation module 312, market data gathering module 314, market
data correlation module 316, historical data matching module 318,
and visualization module 320.
[0164] The description below describes network elements, computers,
and/or components of a system and method for improving financial
instrument attribute prediction and attribute visualization that
may include one or more modules. As used herein, the term "module"
may be understood to refer to computing software, firmware,
hardware, and/or various combinations thereof. Modules, however,
are not to be interpreted as software which is not implemented on
hardware, firmware, or recorded on a processor readable recordable
storage medium (i.e., modules are not software per se). It is noted
that the modules are exemplary. The modules may be combined,
integrated, separated, and/or duplicated to support various
applications. Also, a function described herein as being performed
at a particular module may be performed at one or more other
modules and/or by one or more other devices instead of or in
addition to the function performed at the particular module.
Further, the modules may be implemented across multiple devices
and/or other components local or remote to one another.
Additionally, the modules may be moved from one device and added to
another device, and/or may be included in both devices.
[0165] Baseline probability generation module 312 may generate
baseline probabilities. For example, baseline probabilities may be
generated prior to the opening of a trading day for one or more
financial instruments. A baseline probability may be generated from
one or more factors including, for example, an average historical
performance for a current month of a year, an average historical
performance for a current calendar day, an average historical
performance for a numerical trading day of a week, a number of
positive closes for the financial instrument during previous
trading days, a number of positive closes of a financial market
associated with the financial instrument during previous trading
days, and an indication of volatility of a financial instrument
(e.g., a standard deviation of recent daily returns for the
financial instrument).
[0166] Market data gathering module 314 may receive market data
from one or more sources. According to some embodiments, market
data may be provided by external financial instrument market data
providers (e.g., Interactive Data Corporation, Image Master, or
another financial market data provider). Market data gathering
module 314 may provide one or more interfaces, filters, converters,
formatting modules, or other data processing components to format,
process, and/or analyze data. Data may be provided periodically
(e.g., daily, hourly, real time, or other increments), in batch or
bulk, in response to a query or request (e.g., initiated by a
server), or event driven (e.g., in response to market opening).
[0167] Market data correlation module 316 may increase an amount of
historical market data available to analyze a financial instrument
by identifying additional historical market data based on a
correlation of the additional historical market data to the
financial instrument. According to some embodiments the correlation
may be based upon price behavior. According to some embodiments,
market data correlation module 316 may set a minimum level of
correlation required for identification of additional historical
market data. Market data correlation module 316 may set a minimum
level of correlation required statically. In one or more
embodiments, the minimum level of correlation required by market
data correlation module 316 may be dynamically set based at least
in part on an amount available historical data for the financial
instrument. For example, if a financial instrument has been in a
market for thirty years, it may have a large amount of historical
data available. For such a financial instrument additional
historical data from correlated financial instruments is less
important so a level of correlation required may be high (e.g., a
95% correlation). Market data correlation module 316 may weight
historical data based on a level of correlation. For example,
historical data of a second financial instrument with a 95%
correlation to an instrument being analyzed may be given more
weight than a second financial instrument with only an 85%
correlation.
[0168] Historical data matching module 318 may match one or more
current financial instrument attributes and one or more financial
instrument attributes of historical financial instrument data.
According to some embodiments, matching current market data to
historical market data may be performed using one or more portions
of market data including at least one of price, minutes left in a
trading day, volume, and volatility. Price may be represented in
different forms such as, for example, an overall market percentage
change for a financial instrument since the opening of the trading
day. In one or more embodiments, a strength of a match may be
weighted by Historical data matching module 318 based on a number
of market data portions matched. In some embodiments, market data
portions may be weighted individually and a strength of a match may
be based on which market data portions match.
[0169] Visualization module 320 may provide visualization and
interaction with financial data using scatterplot visualizations.
For example, data may be grouped according to two or more specified
dimensions and determining one or more hierarchical, relational,
spatial, relative, or temporal, relationships between the two or
more user-specified dimensions. A position of a financial
instrument intersecting an X and a Y axis may be depicted in a
first order based on the one or more metrics measuring the
relationships between return and risk associated with the financial
instrument. In an illustrative embodiment, the data includes
financial data. Visualization module 320 may automatically visually
highlight a featured financial instrument's placement along the
spatial relation between risk and return. A first user option may
enable a user to selectively visually query the identity of the
financial instrument in the scatterplot space, as well as the data
associated with its placement along the spatial relation between
risk and return. A second user option may enable a user to
selectively visually query the identity of comparative financial
instruments in the scatterplot space, as well as the data
associated with their placement along the spatial relation between
risk and return. Additional user options may enable a user to
select or input the time horizon and/or calculation method on the
basis of which return is measured. Further user options may enable
a user to select or input the time horizon and/or calculation
method on the basis of which risk is measured.
[0170] Visualization module 320 may provide user options allowing a
user to adjust a scale of risk and return axis, and some
embodiments may dynamically populate a scatter plot with additional
financial instruments as the scale of risk and return changes.
Additional user options may enable a user to trigger tabular view
of underlying data or provide other visualization options. In a
specific embodiment, a scatterplot of Visualization module 320 may
depict metrics for the risk and return of financial instruments as
X and Y axis.
[0171] Referring to FIG. 4A, there is shown a method for financial
instrument attribute prediction and attribute visualization, in
accordance with an embodiment of the present disclosure. At block
402, the method 400 may begin.
[0172] At block 404 a baseline probability for a financial
instrument may be established. For example, baseline probabilities
may be generated prior to the opening of a trading day for one or
more financial instruments. A baseline probability may be generated
from one or more factors including, for example, an average
historical performance for a current month of a year, an average
historical performance for a current calendar day, an average
historical performance for a numerical trading day of a week, a
number of positive closes for the financial instrument during
previous trading days, a number of positive closes of a financial
market associated with the financial instrument during previous
trading days, and an indication of volatility of a financial
instrument (e.g., a standard deviation of recent daily returns for
the financial instrument). At block 406, the baseline probability
may be displayed.
[0173] At block 408, the current marketplace data for the financial
instrument may be input. Current marketplace data may include, for
example, price, minutes left in a trading day, volume, and
volatility.
[0174] At block 410 current market place data may be matched to
historical data. One or more current financial instrument
attributes and one or more financial instrument attributes of
historical financial instrument data may be matched. According to
some embodiments, matching current market data to historical market
data may be performed using one or more portions of market data
including at least one of price, minutes left in a trading day,
volume, and volatility. Price may be represented in different forms
such as, for example, an overall market percentage change for a
financial instrument since the opening of the trading day. In one
or more embodiments, a strength of a match may be weighted based on
a number of market data portions matched. In some embodiments,
market data portions may be weighted individually and a strength of
a match may be based on which market data portions match.
[0175] At block 412 an average outcome of matched historical
conditions may be generated. At block 414 probabilities of future
financial instrument conditions may be generated based on the
averaged outcome of matched historical conditions. At block 416,
one or more generated probabilities for the financial instrument
may be output. At block 418, the method 400 may end.
[0176] FIG. 4B depicts a method for analyzing event data to predict
an impact on the performance of an asset, in accordance with an
embodiment of the disclosure. At block 422 the method 420 may
begin.
[0177] At block 424, received event data may be processed. Event
data may be from one or more sources. For example, event data may
be user entered event data to model an impact of a potential event
on a financial instrument, an actual event received from a data
feed, and an event generated by a system to model an impact of
upcoming potential events. Event data may include, for example,
geopolitical events, earnings events, weather events, product
events, and surprises relative to expectations for one or more
events.
[0178] At block 426, received event data may be correlated with a
large volume of historical data (e.g., decades of time series
financial data).
[0179] At block 426, a predicted impact may be identified based on
correlation of the event data with the historical data. The
predicted impact may be an impact on a financial instrument
performance.
[0180] At block 430 the predicted impact may be presented to a user
(e.g., via one or more of an alert, an email, a text message, a
blog post, a web based ticker, a web based animated banner, a
transmitted recorded audio message, and an electronic
notification). At block 432, the method 420 may end.
[0181] FIG. 5 depicts a user interface for pushing statistical
market content to a user, in accordance with an embodiment of the
disclosure.
[0182] In some embodiments, one or more automated processes may
mine historical data to produce statistical content to
automatically present to one or more users (e.g., financial data to
traders). Raw data (e.g., asset prices) may be derived, abstracted
and otherwise statistically analyzed to produce statistical data
(i.e., mined data). Data may be mined and presented as a real time
or near real time feed to users. Mined data may monitor events
based on one or more data feeds (e.g., economic data surprises,
weather anomalies, central bank statements and actions, product
releases, earnings surprises, mergers and acquisitions and IPOs,
corporate governance changes, regulatory approvals and denials, and
seasonality, etc.) and analyze data by mapping associations between
similar historical data and correlated results (e.g., historically
an event of type X impacted financial instrument Y by increasing
the relative performance of Y by 1.50% by the end of the trading
day with respect to a benchmark). Mined data may identify
significant impacts in relative and/or absolute performance of a
financial instrument. Large collections of historical data may be
mined in real time or near real time.
[0183] The predicted performance of various sectors and industries
may be ranked based on their performance in similar historical
events and/or market conditions. For example, if released jobless
numbers are a surprise (e.g., they deviate significantly from a
consensus figure on expected jobless numbers), the system may then
mine historical data and surface (identify) prior examples of
similar surprises of a similar magnitude to the one that just
happened. The system may define what the magnitude of the surprise
that just happened was by discovering the standard deviation of the
surprise (from the consensus) in the history of identified
surprises for that data point (e.g., jobless numbers). The system
may categorize the magnitude of the surprise that was just
announced, and then in so doing, may be able to find and match
other similar historical cases. Based on the matching, the system
may categorize and group the surprise of that day with other
historical surprises that the system has just established to be
similar (i.e., matching surprises on the independent variable side
may facilitate discovering a correct set of precedents to model out
the asset returns on the dependent variable side). The system may
then test the market impact of those previous surprises in the set
it just defined to be analogous to what just happened in the
market. Based on this the system may provide a probabilistic market
impact of what just happened (e.g., an event seconds ago such as
for example, an event determined by the system after receipt of the
event data to be a `1 standard deviation earnings surprise`
relative to all historical earnings results for that company, or an
event determined by the system after receipt of the event data to
be a 2 standard deviation jobs surprise relative to all historical
jobs surprises). Thus the system may be both able to characterize a
statistical frequency of occurrence of the independent variable
(e.g. earnings numbers or economic data surprises) by defining
dynamically the relevant set of historical precedents for modeling,
and also able to model asset price returns and asset pricing
anomalies in relation to that specific set of historical precedents
it just isolated and defined.
[0184] As depicted in user interface 502, notifications of
real-time events may be presented with summary information of an
impact of such events and a confidence level. The impact of such
events may be projected across different areas (e.g., different
market sectors, different benchmarks, different financial
instruments, etc.). Events may be categorized into one or more
categories (e.g., economic data surprises, weather anomalies,
central bank statements and actions, product releases, earnings
surprises, mergers and acquisitions and IPOs, corporate governance
changes, regulatory approvals and denials, seasonality, all events,
and custom focused feeds of events). Events may also be ranked,
sorted, or filtered. In some embodiments, a user may filter events
by market sector, portfolio holdings or other parameters in order
to filter events to those which affect or interest the user. As
depicted an exemplary economic data surprise may be a released
report indicating that non-farm payrolls rose more than expected. A
notification for the event may indicate a market impact of the
surprise, which may be calculated by statistically averaging the
returns of various financial instruments. An impact of a surprise
may be calculated quickly by using previously identified precedents
of the surprise. For example, a system may calculate one or more
sets of precedents for different types of events (e.g., jobs
surprises, non-farm payroll surprises, etc.) which may be
associated by one or more of a similarity based on orders of
magnitude of a surprise (e.g., a 1% standard deviation, a 2%
standard deviation, etc.), a similarity of market conditions, or
other factors. Using pre-calculated precedents of events, an impact
of an actual event on returns associated with an instrument may be
predicted using returns associated with the identified
precedents.
[0185] Within several minutes of the surprise being released, the
system automatically may send an alert with the statistics on the
market impact already calculated, tested, and charted. This may be
done programmatically, and automatically, in seconds--not requiring
human labor. Alternatively alerts may be created by human input and
displayed or otherwise communicated via the interface depicted in
user interface 502. As depicted, the impact of an unexpected
decrease in jobless claims from 339,000 to 319,000 may suggest
based on historical data that the industrial sector may rise by 60%
by the end of the day. Other indicators may also be displayed such
as, for example, the impact on a benchmark (e.g., S&P 500 to
rise by 61%), the rate of return for one or more sectors, the worst
performing sector historically and the projected impact, a
percentage of positive trades for one or more sectors. Although
depicted as web screen, the alert may be an alert, a text message,
an email, a banner or ticker, a blog post, an audio alert, a
generated phone message, or another electronic communication. The
language used in the alert may be machine-generated, using
algorithms taking as their input one or more of the return of the
assets being modeled, the frequency of positive returns, the rank
order of returns (best to worst), the number of prior observations,
and other inputs. The alert may carry a confidence indicator (by
means, for example of a `star rating` display or other means),
whose value is derived from inputs that may include one or more of:
the number of observations in the alert, the probability that the
returns of assets on the days in the model are statistically
anomalous compared to all other days during the same period of
time, the frequency distribution of returns, or other relevant
factors.
[0186] FIG. 6 depicts a user interface for pushing statistical
market content to a user, in accordance with an embodiment of the
disclosure.
[0187] Clicking on an alert, focusing on an alert, selecting an
alert or otherwise responding to an alert may provide further level
of detail as depicted in FIG. 6. As depicted in FIG. 6, selecting
an alert 610 may provide further summary text (e.g., "Jobless
Claims Misses>8,529 (-0.5 SD Miss") and may provide one or more
details on the impact on particular sectors. For example, a
correlation of a trade in a sector with a benchmark may be shown
(e.g., the S&P 500). A number of observations and a standard
deviation from an average trading day may also be presented for a
sector. Other data may be presented for one or more sectors
including, for example, an average excess return, a cumulative
return, and a Sharpe ratio.
[0188] FIG. 7 depicts a user interface for pushing statistical
market content to a user, in accordance with an embodiment of the
disclosure. FIG. 7 may represent an additional detail display
presented in response to further drilling down or selecting an
alert. This may be, for example, a full, in-depth statistical
report--of the type that would take a human research team days of
work to generate--all created programmatically within a short
period of time of the market event (e.g., seconds). One or more
graphs may be presented depicting an impact of an event such as,
for example, an impact of the event across sectors (e.g.,
industries, financials, energy, materials, healthcare, utilities,
IT, etc.) Other graphs may include an impact across industries, an
impact on benchmarks, etc. Graphs may include benchmarks and an
ability to drill down on one or more elements of a graph (e.g., a
sector, an industry, a benchmark, a ticker, etc.) A graph may
indicate one or more specific market elements (e.g., particular
financial instruments, companies, tickers, etc.) significantly
impacted by an event. Impact may be measured by a projected and/or
a relative rank order of return compared to other industries,
sectors, or financial instruments based on historical data, a
percentage of positive trades based on a correlation to historical
data, an average excess return (e.g., compared to a benchmark), or
by other measure of performance.
[0189] One or more graphs may present trading strategies based on
analysis from correlation of the event to historical data (e.g.,
back tested trades). Strategies may include suggested holding
periods and other data. Detailed report data may also include a
distribution of benchmark returns, a distribution of returns for a
sector, or other comparative financial data. A list of historical
events correlated to a current event being analyzed may be
presented. A listing of correlated historical events may be
provided chronologically, by order of correlation, by order of
impact to the market, or based on other sort parameters. A user may
be able to drill down and view details of historical events. In
some embodiments, a user may be able to exclude one or more events
and recalculate financial impact of a current event based on
historical data other than the excluded events.
[0190] FIG. 8 depicts a detailed report provided via a
notification, in accordance with an embodiment of the disclosure.
For example, in response to an event such as the Crimean Referendum
and Declaration of Independence, a detailed report on one or more
financial assets (e.g., the Ruble) may be produced. According to
some embodiments, the dynamically generated report may be produced
in near real time in response to the event being received (e.g.,
from a news feed, scraping a website or blog, etc.). FIG. 9 depicts
a detailed report chart provided via a notification, in accordance
with an embodiment of the disclosure. As depicted, a detailed bar
chart may be provided showing performance of assets analyzed in the
report of FIG. 8. The bar chart may provide one or more benchmarks,
an ability to drill down into a particular asset represented by a
bar of the chart, an ability to filter or add assets, and other
user interface controls.
[0191] FIG. 10 depicts a detailed report chart provided via a
notification, in accordance with an embodiment of the disclosure.
FIG. 10 may display historical performance of one or more assets
analyzed in the report of FIG. 8. In some embodiments, FIG. 10 may
be linked with another chart (e.g., a bar chart of FIG. 9) or a
report, such that when an asset is selected in one chart or report,
the historical performance is displayed in chart depicted of FIG.
10.
[0192] FIG. 11 shows a listing of study results associated with an
event notification, in accordance with an embodiment of the
disclosure. As depicted in FIG. 11, one or more study summaries
associated with an event may be displayed. A study summary may
provide further detail on an asset associated with an analyzed
event (e.g., Crimean Referendum and Declaration of
Independence).
[0193] FIG. 12 shows a trade history associated with an event
notification, in accordance with an embodiment of the disclosure.
As depicted in FIG. 12, a trade history of one or more assets
associated with an event may be displayed in comparison with a
benchmark trade for a similar period.
[0194] FIG. 13 depicts a listing of trading ranges of assets in a
study, in accordance with an embodiment of the disclosure. Assets
may include, for example, sectors, individual financial
instruments, and benchmarks. A trading range for one or more assets
including a color coded indicator, may be provided.
[0195] FIG. 14 depicts a menu for selecting events for analysis, in
accordance with an embodiment of the disclosure. User interface
controls may allow a user to select, add, delete, filter, sort,
and/or prioritize event types. Other conditions and parameters may
be specified (e.g., a specifying listing of tickers to monitor
whereby an event may be displayed based on potential or actual
impact to the listing of financial instruments represented by the
tickers). Thresholds may be set to filter or rank events (e.g.,
display events which have greater than a specified percentage
impact projected for a user's portfolio or specified instruments or
sectors).
[0196] FIG. 15 depicts a help menu on an event analysis user
interface, in accordance with an embodiment of the disclosure. The
event user interface may provide a large listing of events
available for study generation. Events may be categorized, sorted,
and filtered.
[0197] FIG. 16 depicts a help menu on an event analysis user
interface, in accordance with an embodiment of the disclosure. As
depicted in FIG. 16 help may be provided to allow a user to create
a study based on one or more events (e.g., the events listed in the
background of FIG. 15) or based on user provided events. Help may
also be provided for other study functionality such as, for
example, sharing studies, populating studies with a ticker or
portfolio, viewing and duplicating studies, and other analytical
functionality.
[0198] FIG. 17 depicts a help menu on an event analysis user
interface, in accordance with an embodiment of the disclosure. Help
may be provided for advanced functionality such as, for example,
advanced studies using multiple conditions or parameters, creating
baskets of assets, comparing baskets of assets, and other grouping
and comparison functionality.
[0199] FIGS. 18A and 18B show a user interface controls for pushing
statistical market content to a user, in accordance with an
embodiment of the disclosure. FIG. 18A may be a dashboard for
navigation among multiple interfaces or components of a system. For
example, icons, buttons, or other user interface controls may allow
navigation to user interface screens for featured studies, all
studies, study creation, a user dashboard, an event listing, an
alert or notification listing, settings, and help. FIG. 18B may
provide navigation among classifications or groupings of events.
Events may be grouped by a user specified or administrator
specified taxonomy.
[0200] FIG. 19 depicts an event analysis user interface, in
accordance with an embodiment of the disclosure. An event user
interface may provide a large listing of events available for study
generation. Events may be categorized, sorted, and filtered.
[0201] FIG. 20 depicts a method for establishing baseline
probabilities for financial instrument attributes, in accordance
with an embodiment of the present disclosure. As discussed above
with reference to block 404 of FIG. 4A, a baseline probability may
be generated from one or more factors including, for example, an
average historical performance for a current month of a year, an
average historical performance for a current calendar day, an
average historical performance for a numerical trading day of a
week, a number of positive closes for the financial instrument
during previous trading days, a number of positive closes of a
financial market associated with the financial instrument during
previous trading days, and an indication of volatility of a
financial instrument (e.g., a standard deviation of recent daily
returns for the financial instrument).
[0202] FIG. 21 shows a method for gathering financial marketplace
data, in accordance with an embodiment of the present disclosure.
The current marketplace data for the financial instrument may be
input. Current marketplace data may include, for example, price,
minutes left in a trading day, volume, and volatility.
[0203] FIG. 22 depicts a method for identifying relevant financial
marketplace data, in accordance with an embodiment of the present
disclosure. As illustrated in FIG. 7, real time current market
conditions for a financial instrument may be matched against
historical financial data. Current marketplace data may include a
ticker symbol, minutes left in trading day, % change since open,
volume since open, volatility, overall market % change since open.
Weighting of matched historical data may depend on one or more
factors. A perfect match along one dimension=higher weight to end
of day outcome of historical data record. A proximity match along
one dimension=some weight to end of day outcome of historical data
record. No match along one dimension=no weight to end of day
outcome of historical data record. The > the # of Perfect of
Proximity Matches Along Multiple Dimension of a Historical Data
Record the > the Weight Applied to End of Day Outcome of
Historical Data Record.
[0204] FIGS. 23A-23J depict a user interface for viewing predicted
financial instrument attributes, in accordance with an embodiment
of the present disclosure. As shown in FIG. 8A, a user interface
may depict real time odds of a price change of a financial
instrument, historical odds, average monthly percentage change of a
financial instrument, a financial instrument price quote and other
financial instrument analysis and data. User interfaces may provide
an ability to search on one or more financial instrument attributes
(e.g., a ticker symbol, a price range, a risk range, etc.).
[0205] Referring to FIG. 23A, in some embodiments a financial
instrument's probability of closing positive over a given trading
session or a given time period (such as calendar weeks and/or
months) may be provided. For example, a seasonality score may
provide a ranking indicating a likelihood of closing positive
and/or some metric of a financial instrument's typical gain or loss
over a given trading session or a given time period (such as
calendar weeks and/or months). This may be represented as a
graphical rating or ranking (e.g., a `5 star` rating scale or other
graphical indicators).
[0206] FIG. 24 depicts a process flow for a method of financial
instrument attribute prediction, in accordance with an embodiment
of the present disclosure. As illustrated, at step one metrics may
be gathered (e.g., average historical performances for a market
and/or financial instrument). At step two monitoring of one or more
financial instruments may be performed. At step three analysis of
real time market inputs may be performed. At step four historical
matching may be performed. Correlation may be used to expand a
sample size beyond a population of financial records for a specific
financial instrument to include other financial instruments whose
price historically correlates to the specific financial instrument.
Historical records may be weighted based on a similarity to current
real time market conditions (e.g., price of a financial instrument,
minutes left in a trading day, volume, and other factors).
Historical records for other financial instruments may also be
weighted based on a correlation to a specific financial instrument
being analyzed. At step 5 the matched historical records may be
assessed to identify the historic outcome of one or more financial
instruments. Historic outcomes may be averaged, weighted or
otherwise processed. A prediction of the specific financial
instrument being analyzed may be generated. The prediction may be
made in real time, periodically, in response to a user command or
event or at specified times. Such a prediction may be updated in
real time based on changing market conditions, news information, or
other factors. Predictions may be posted on a user interface (e.g.,
a web page), sent via an electronic message, or otherwise provided
to a user.
[0207] FIG. 72 depicts a platform for correlation of non-asset
metrics to asset prices and metrics, in accordance with an
embodiment of the disclosure. As depicted in FIG. 72, sources of
data for asset and/or non-asset information may include one or more
public sources of data such as, for example, blog 5704, wiki 5706,
and Feed 5708. For example, these sources of data may include
non-asset metrics available via the internet (e.g., economic data
surprises, weather anomalies, central bank statements and actions,
product releases, earnings surprises, mergers and acquisitions and
IPOs, corporate governance changes, regulatory approvals and
denials, seasonality, etc.) According to some embodiments, data
sources may be Internet based sources whose URLs are scraped.
Sources of data for asset and/or non-asset information may also
include licensed data 5710(1) . . . (N) which may include, for
example, licensed feeds of market asset prices, news feeds, and/or
other data. Data from public sources may undergo one or more
processing steps. For example, data may be cached at cache 5712.
Cached data may be provided to one or more processing management
nodes 5714 (1) . . . (N). Cache 5712 may maintain a data structure
(e.g., a list, a database, etc.) of public data sources to
harvest/scrape.
[0208] Processing management nodes 5714 may distribute a workload
of processing data among one or more processing nodes 5716 (e.g.,
load balancing processing among one or more processing nodes).
Processing nodes 5716 may use one or more methods to harvest,
scrape, and/or refine data. For example, processing nodes 5716 may
use regular expressions (RegEx), format specific scraping (e.g.,
wiki specific scraping), summarizers, sentiment analysis, natural
language processing, and other methods. Data may be stored as time
series data.
[0209] Processed data may be fed to one or more queues (e.g., queue
5718). As illustrated, data of a known format and/or quality may be
provided directed to a queue (e.g., licensed data 5710). Queued
data may go through one or more quality gates 5720, A quality gate
5720 may verify one or more things such as, for example, spell
checking, format consistency, existence, and numerical
plausibility. In some embodiments, data may cycle through one or
more quality gates a plurality of times (e.g., for a redundant
quality check).
[0210] After being processed at a quality gate, changes in data may
be recorded at log file 5722. Logged data may rank a data source
(e.g., for quality based on an amount of processing required or
errors found). After logging one or more attributes of time series
data, it may be transferred to an environment (e.g., a development
environment, a test environment, a staging environment, and/or a
production environment.) In some embodiments, a data may be
transferred to a first environment such as a development
environment after one or more iterations through processing and
quality gates. After subsequent iterations, data may be advanced to
another environment. This may provide an opportunity to further
evaluate data prior to advancement to a production environment. In
some embodiments, changes to data may be distributed to a plurality
of environments in a same iteration or at a same time (e.g., data
changes from a highly ranked source).
[0211] According to some embodiments, correlation between events
may be identified by a correlation between a first event and an
asset and a correlation between a second event and an asset.
Multiple studies may be linked to create associations between
events based on such a correlation. For example, if a first event
type (e.g., Middle East events) has a high correlation with an
asset (e.g., oil), and a second event type (e.g., U.N. sanctions)
has a correlation with the same asset there may be a correlation
between the two event types. A first study or analysis may have
been performed by a first user which may analyze a correlation
between the first event type and the asset. A second study may have
been performed by a second user studying a second event type and
the same asset. Users may anonymously share data and/or studies
with a financial analysis system and/or other users. In some
embodiments, studies may be shared anonymously within a group, a
company, or an organization. Data based on correlations between
studies may be provided to users with whom the studies are
shared.
[0212] A financial analysis system may analyze shared studies
looking for correlations between studies. Such correlations between
event types may be used to produce more detailed analysis and/or
more accurate analysis of an asset associated with both events.
[0213] FIGS. 25A-D depicts a user interface for financial
instrument visualization, in accordance with an embodiment of the
present disclosure. The user interfaces of FIGS. 25A-D depict the
risk that a user might buy the financial instrument at the wrong
time of year. The X axis shows the degree of variance in the
monthly returns of the ticker, where higher variance (tickers on
the right half of the figure) means greater chances of buying the
ticker in a month that results in a significant loss--even if the
ticker is generally positive over long periods of time. The top
left region of the figure is optimal: Tickers with high annual
returns and low month-to-month variance in returns. The bottom
right of the figure may be the worst region: Tickers with very high
month-to-month variation in returns and low overall annual returns.
The bottom left region and the top right region are areas that are
suitable for different investment strategies: If a user can be
satisfied with a lower overall return as the price of not having to
worry about buying in a bad month of the year and taking a
significant short-term loss, then the bottom left region is
appropriate for the user. If a user can weather the month-to-month
variations and not flinch at shorter term losses because the user
is willing to ride the stock to higher overall long term returns,
then the top right region is more suitable for the user. User
interfaces 25A-D may provide an ability to search on one or more
financial instrument attributes (e.g., a ticker symbol, a price
range, a risk range, etc.). User interfaces 25A-D may provide
functionality to generate reports for one or more financial
instruments and to set alerting and notification options for one or
more financial instruments (e.g., based on a floor parameter, a
ceiling parameter, or other metrics). According to some
embodiments, a user may specify criteria to monitor and such
criteria may change a focus or zoom of a user interface. For
example, a floor of a minimum amount of return may be specified and
a ceiling of a maximum amount of risk may be specified. A user
interface may depict a scatter plot and the scatter plot may depict
financial instruments that fall within the specified criteria at
the present time in the market. Such a user interface may update in
real time, periodically, or in response to a specified event or
user command. A dynamically updating interface may reflect
financial instruments that move into a range of specified criteria
and financial equities that fall outside of the specified criteria
may be removed from display. A user may be able to specify specific
financial instruments to exclude, specific financial instruments to
include, market indices to chart and other market data to track.
Financial instruments to include or exclude may also be identified
by specifying specific factors (e.g., minimum volume for a
financial instrument, maximum volatility for an instrument, a
market sector, etc.) A user interface may be capable of displaying
trend lines for one or more financial instruments during a market
day or over a longer historic period.
[0214] FIG. 29 depicts a user interface for evaluating the
performance of a plurality of financial instruments, in accordance
with an embodiment of the present disclosure. According to some
embodiments, a plurality of financial instruments may be listed
alongside an average rate of return for a month for each of the
plurality and a percentage of time each of the plurality closed
positive, as well as the number of observations or the length of
the observation period (e.g., 29 years), as well as other summary
statistics, such as Max/Min values or other liminal values. The
timeframe may be a current month, a past month, a current quarter,
a past quarter, a current week, a past week, a current or past
year, or another specified period. The plurality of financial
instruments may be selected (e.g., displayed based on specified
search criteria), ordered by rate of return, ordered by percentage
of time positive, ordered by the number of observations, and
filtered (e.g., to exclude financial instruments below a floor,
above a ceiling, or meeting a specified threshold). Other financial
instrument ratings may be displayed (e.g., risk, current market
price, etc.)
[0215] FIG. 30 shows a user interface for evaluating the
performance of a financial instrument, in accordance with an
embodiment of the present disclosure. In some embodiments, market
news and triggers may be displayed (e.g., political events,
earnings events, holidays, elections, industry events, sector
events, economic indicator events, etc.). A plurality of financial
instruments may be selected (e.g., displayed based on specified
search criteria), ordered by rate of return following an event or
series of events, ordered by percentage of time positive following
an event or series of events, and filtered (e.g., to exclude
financial instruments below a floor, above a ceiling, or meeting a
specified threshold, or filtered to exclude market news events or
other event triggers categorized as below a floor, above a ceiling,
or meeting a specified threshold, e.g., `Employment Reports that
were positive surprises,` where a positive surprise is defined as
more than 25K jobs above the consensus estimate, or `Earnings
Reports (for a given company) that were positive surprises,` where
a positive surprise is defined as more than $0.50 a share above the
consensus estimate, or some similar metric used during earnings
reports). Furthermore, the timeframe of the universe of event
triggers sampled (e.g., Employment Reports or Earnings Reports) may
be constrained by the user to only include a current month, a past
month, a current quarter, a past quarter, a current week, a past
week, a current or past year, or another specified period, and the
user may constrain the timeframe of the universe of event triggers
sampled via user interfaces such as a slider or a dropdown menu.
Furthermore, the timeframe of the rate of return following an event
or series of events sampled may be constrained by the user to only
include a number of seconds or minutes following the occurrences of
the event, only the first trading days on or following the
occurrences of the event, only the first two trading days on or
following the occurrences of the event, or only some specific
number of trading days, weeks, or months, trading days on or
following the occurrences of the event, and the user may constrain
the timeframe of the rate of return following an event or series of
events sampled via user interfaces such as a slider or a dropdown
menu.
[0216] In some embodiments, a scoring request may be received. A
scoring request may be a set of identifiers that map to a set of
varying time series, as well as filters through which time series
data is passed. These filter functions may process time series data
and produce a second time series. For example, a filter function
using a financial instrument ticker (e.g., "AAPL") and compare it
to a closing price (e.g., "AAPL>500"). This filter function may
return a list of dates (time series of events) which correspond to
days where AAPL closed above 500. A time series may be associated
with multiple filter functions. Each combination of time series
data and a filter function may be sent to a compute node based on a
routing algorithm. Routing may be handled by a mixer node (e.g.,
mapping). The new time series data computed from the original time
series and the filter function (e.g., the reduced data) may be
gathered from each compute node. Multiple sets of generated time
series data may be collected and merged on or more nodes to form
final result.
[0217] FIG. 73 depicts a platform for dynamic resharding of data
based on demand, in accordance with an embodiment of the
disclosure. In some embodiments, based on day-to-day demand for
time series data (stocks, metrics, events, etc.), the distribution
of such data may be rebalanced across compute nodes (CNs). For
example a mixer node 5806 may receive a scoring requests 5804 from
users/automatic queries, etc. Scoring requests 5804 may include a
set of identifiers that map to a set of varying time series, as
well as filters through which time series data is passed. These
filter functions take in a time series, and produce a second time
series. Scoring requests may be logged (e.g., scoring request log
5810) to gather statistics on the scoring requests.
[0218] Mixer node 5806 may create time series function pairs.
Compute nodes 5808 may score the results and send the results to a
map reduce node 5812. The merged results may be sent from map
reduce node 5812 to a requester (e.g., an automated process or a
user).
[0219] In some embodiments, desired rebalancing can be calculated
by taking into account one or more factors. Factors may include,
for example: [0220] A. Historical demand (e.g., on average, most
people ask for X 40 times as often as the canonical time series);
[0221] B. Short term information (e.g., Sudden bursts of demand,
e.g. GOOG split causes increased interest in Google's stock data);
and [0222] C. Anticipated demand (e.g. Google will be splitting
tomorrow, so we should plan for increased demand. Fed announcement
tomorrow, which typically implies X1 and X2 time series having
higher demand).
[0223] Actual rebalancing may consist of peer to peer sharing of
data across compute nodes. For example, a mixer node may a message
to one or more compute nodes telling the node the data sets it
should add or remove, and each compute node can advertise (e.g., in
a peer to peer file sharing protocol), for the datasets it needs.
These datasets may be downloaded from multiple sources to ensure
fast rebalancing.
[0224] FIG. 31 depicts a user interface for evaluating the
performance of a financial instrument, in accordance with an
embodiment of the present disclosure. In some embodiments, a user
may have the ability to select a financial instrument data point on
a visualization via some user-input interaction, such as a `hover
over,` and the financial instrument data point might animate in
some way, such as become larger, in order to more clearly visualize
its location and/or relative position on the visualization. Other
interactive animations may include extending lines horizontally and
vertically from its position on the visualization to the spots on
the X and/or Y axis that it intersects (e.g., where the X and Y
axis are metrics that instrument risk and return, and/or financial
Alpha and Financial Beta, and/or some combination of the above), in
order to more clearly visualize a location and/or relative position
on the visualization of a financial instrument. In a further
embodiment, an interactive animation might also result in the
visualization of key data or attributes associated with the
financial instrument data point, such as its name, its `value`
along the X axis, its `value` along the Y axis, (e.g., where the X
and Y axis are metrics that instrument risk and return, and/or
financial Alpha and Financial Beta, and/or some combination of the
above), the sector to which it belongs, its market capitalization,
as well as other attributes of the financial instrument.
[0225] FIG. 32 depicts a user interface for evaluating the
performance of a financial instrument, in accordance with an
embodiment of the present disclosure. According to some
embodiments, FIG. 32 may represent a zoomed in or focused view of a
scatterplot diagram. A user and/or a system may change a scale of X
and/or Y axis (a "zoom in/zoom out function"), where the X and Y
axis may be metrics that instrument risk and return, and/or
financial Alpha and Financial Beta, and/or some combination of the
above. In some embodiments, as the user and/or system to changes
the scale, (e.g., `zooms in` or `zooms out`, of the X and/or Y
axis) the system may dynamically populate the visualization with
more or fewer instruments (e.g. interactive and/or non-interactive
data points) at these different levels or `resolution` or `zoom`.
In another embodiment, a user may have the ability to select (for
example through a click, or a click and drag, or a tap, or a pinch
motion, or some other hand-gesture, or a speech command) a region
to zoom in and out of, with the resulting above-described
consequences, functionalities, and features. A visualization
interface may be repopulated in response to a user or system
command to change focus. A visualization interface may also be
repopulated in real time based on changed in market data, news, and
other conditions. A user may specify inputs for a visualization
interface (e.g., display top 100 data points within a specified
risk and return range ordered by trading volume, current market
price, or other criteria). Zooming in may cause more data points to
meet a threshold (e.g., make a top 100 list) and to become
visible.
[0226] FIG. 33 depicts a user interface for evaluating the
performance of a financial instrument, in accordance with an
embodiment of the present disclosure.
[0227] FIG. 34 depicts a user interface for evaluating the
performance of a financial instrument, in accordance with an
embodiment of the present disclosure. A user may be able to select
or deselect one or more financial instruments by name to layer onto
or off the above visualization. A user may also be able to view a
visualization and deselect and select financial instruments (e.g.,
by clicking on a financial instrument and specifying delete or
filter to remove it from display). A user may be provided a drop
down, a query box, a list or other user interface control to add
financial instruments to a display. A user may also be able to view
a ranking of financial instruments based on specified criteria and
then may be able to customize a ranking so that certain instruments
are added to or removed from a visualization.
[0228] In some embodiments, a user or system may be able to select
or deselect one or more types/categories/classes/attributes of
financial instruments to layer onto or off the visualization. For
example, types/categories/classes/attributes of financial
instruments might include, but are not limited to, sector, market
capitalization (such as the distinction between large market
capitalization and small market capitalization financial
instruments) beta (such as the distinction between high beta and
low beta financial instruments); volatility (such as the
distinction between high volatility and low volatility financial
instruments); volume (such as the distinction between high volume
and low volume financial instruments); absolute price (such as the
distinction between high absolute price and low absolute price
financial instruments); book-to-market ratio (such as the
distinction between high book-to-market and low book-to-market
financial instruments); `growth` versus `value` (such as the
distinction between `growth stocks` and `value stocks`). In one or
more of the above, `high` and `low` and `large` and `small` can be
defined by outside external definition or source and/or
distinctions such as quintiles and quartiles relative to the
financial instrument's class, dynamically calculated by the system
and/or imported from an outside external definition or source;
and/or some threshold inputted by the user into the system and/or
some other analysis carried out by the system itself.
[0229] According to some embodiments, visualizations might use
coloring or shading to label/classify/identify financial instrument
data points by types/categories/classes/attributes of financial
instruments. Types/categories/classes/attributes of financial
instruments might include, but are not limited to, asset class,
instrument type, geography, market capitalization, beta, volume,
volatility, absolute price, and Book-to-Market Ratio. A
visualization system might use slices of multiple colors on a
financial instrument data point to indicate that the data point
belongs to more than one set of
types/categories/classes/attributes.
[0230] FIG. 35 depicts a user interface for evaluating the
performance of a financial instrument, in accordance with an
embodiment of the present disclosure. According to some embodiments
a user or a system may be able to select or deselect one or more
financial instruments by name or by
types/categories/classes/attributes of the financial instruments to
layer onto or off the visualization. For example, a user interface
control may be provided via a drop down menu, radio buttons,
spinners, combination boxes, or other user input controls.
Financial instrument data points may populate and/or de-populate in
response to a selection.
[0231] FIG. 36 depicts a user interface for evaluating the
performance of a financial instrument, in accordance with an
embodiment of the present disclosure. According to some embodiments
a user or a system may be able to select or deselect one or more
financial instruments by name or by
types/categories/classes/attributes of the financial instruments to
layer onto or off the visualization. For example, asset classes may
include equities, commodities, bonds, currencies or other classes.
A user may select one or more classes to add to a visualization.
Instrument types may include futures, mutual funds, ETFs, stocks,
and CDs. Index components may also be added to or removed from a
visualization (e.g., Dow Jones, S&P 500, Nasdaq-100, Russell
2000, etc.). Other classes or attributes may be used to add or
remove data from a visualization. Financial instrument data points
may populate and/or de-populate in response to a selection.
[0232] FIG. 37 depicts a user interface for evaluating the
performance of a financial instrument, in accordance with an
embodiment of the present disclosure. According to some embodiments
a user or a system may be able to select or deselect one or more
financial instruments by name or by
types/categories/classes/attributes of the financial instruments to
layer onto or off the visualization. Types, categories, classes,
attributes and other selection criteria may be color coded, shaded,
shaped, contain patterns or otherwise provide indicators of a
selection criteria. The indicators of a selection criteria may be
displayed on a visualization (e.g., financial instruments of a
first type may be one color or pattern and financial instruments of
a second type may be another color or pattern). Financial
instrument data points may populate and/or de-populate in response
to a selection.
[0233] FIG. 38 depicts a user interface for evaluating the
performance of a financial instrument, in accordance with an
embodiment of the present disclosure. According to some embodiments
a user or a system may be able to select or deselect one or more
financial instruments by types/categories/classes/attributes of the
financial instruments to layer onto or off the visualization
results in distribution of instruments with those attributes along
Return/Alpha versus Risk/Beta space, with the use of coloration or
other visual indicators to distinguish classes. Financial
instrument data points may populate and/or de-populate in response
to a selection. Hovering over a plotted data point may identify the
financial instrument it represents and one or more attributes of
the financial instrument. Clicking on a data point may provide a
second functionality (e.g., displaying real time odds of closing
positive such as in FIGS. 23A-23I.) Right mouse clicking on a data
point may bring up a menu with one or more options (e.g., order,
quote, remove from display, add to favorites, track, etc.)
[0234] FIG. 39 depicts a user interface for evaluating the
performance of a financial instrument, in accordance with an
embodiment of the present disclosure. According to some embodiments
a user or a system may be able to select or deselect one or more
financial instruments by types/categories/classes/attributes of the
financial instruments to layer onto or off the visualization
results in distribution of instruments with those attributes along
Return/Alpha versus Risk/Beta space, with the use of coloration or
other visual indicators to distinguish classes. Financial
instrument data points may populate and/or de-populate in response
to a selection. Hovering over a plotted data point may identify the
financial instrument it represents and one or more attributes of
the financial instrument. As depicted in FIG. 39, a financial
instrument for Apple, Inc. is selected.
[0235] FIG. 40 depicts a user interface for evaluating the
performance of a financial instrument, in accordance with an
embodiment of the present disclosure. According to some embodiments
a user or a system may be able to query or enter (for example, via
a search function) a proper name or ticker of one or more
instruments and have the system automatically populate the query
result as an (interactive) layer on the above visualization, as
well as the ability to select from a list of results following such
a query and having the system populate a user selection from within
the results of the query as an (interactive) layer on the
visualization. A user may be able to specify floors values that a
financial instrument must meet to be displayed, ceiling values that
a financial instrument must fall beneath to be displayed or other
criteria. A user may set a limit on a maximum number of returned
results or displayed results or may receive a warning if results
exceed a specified value. A user may specify a sort order to select
a top or bottom number of instruments to be displayed (e.g., top
100 by trading volume within a specified risk and return
ranges).
[0236] FIG. 41 depicts a user interface for evaluating the
performance of a financial instrument, in accordance with an
embodiment of the present disclosure. According to some embodiments
a user or a system may be able to query or input (for example, via
a search function) the name of one or more of the above described
types/categories/classes/attributes of financial instruments and
have the system automatically populate the query result as an
(interactive) layer on the visualization, as well as the ability to
select from a list of results following such a query and having the
system populate a user selection from within the results of the
query as an (interactive) layer on the visualization.
[0237] One or more of the foregoing visualizations may provide a
user the opportunity to click financial instrument data point to
present a correspond interface (e.g., via a hyperlink). A
corresponding interface for a financial instrument data point may
be a drill down interface including a `page` or interface for that
financial instrument that may include a vastly expanded set of data
about that financial instrument. This may not be included in the
Risk/Return visualization and may present further financial
instrument data including, but not limited to, price quotes, price
charts, volume quotes, volume charts, other forms of charts and
graphical representations, "fundamental data" (such as price to
earnings ratios), categorization data (such as sector and
sub-sector membership, e.g., `Energy Sector; Oil and Gas);
statistical data (such as historical and/or statistical price
movement probabilities), news about the financial instrument,
including news dynamically scraped from internet and/or
non-internet sources; social `conversations` surrounding the
financial instrument, such as those that take place on a social
network, graphical or other representations of the identity or
institutions and/or parties that hold the financial instrument
and/or the proportion of the total outstanding shares or volume of
the financial instrument which they hold. Functionality may be
provided for a user to buy the financial instrument, sell the
financial instrument, track the financial instrument, receive
alerts for the financial instrument, and/or receive a call back or
other contact from an advisor regarding the financial
instrument.
[0238] A user interface may be provided to import and or export
portfolios. In some embodiments, one or more of the above
visualizations may display only financial instruments of a
specified portfolio. In some embodiments, a specified portfolio may
contain a specific visual indicator (e.g., shading, blinking,
color, shape, etc.) and other financial instruments may be
displayed along with the portfolio.
[0239] FIG. 42 depicts a user interface for embedding within or
associating with another user interface, in accordance with an
embodiment of the present disclosure. According to some
embodiments, FIG. 42 may represent a `trading calendar` `widget`
than may be displayed on other sites, networks, and platforms, or
as a widget within a user's own site. A widget may display a top
financial instrument as ranked by one or more factors (e.g., a user
preference, a likelihood of closing positive, a rate of return, a
risk, a trading volume, and an event affecting the financial
instrument). A widget may also update based on one or more factors
(e.g., real time data and analysis, a news event, a market event,
and a user specified parameter being met). A widget may alternate
display between a plurality of financial instruments based on one
or more factors (e.g., a user's portfolio, a specified watch list,
user preferences, volume, risk, rate of return, market events, news
events, real time odds or statistics associated with the financial
instrument closing positive, and a recommended financial instrument
for a user portfolio based on specified criteria such as risk and
return ranges). A widget may be customizable by a user for a
certain footprint, layout, positioning on a screen, and content. A
widget may contain one or more links to drill down, refer to
another site, and/or provide more information about a financial
instrument. In some embodiments, a widget may be customized based
on a site or page that a widget is incorporated into. In some
embodiments, FIG. 42 may represent a banner ad. In one or more
embodiments, a banner ad may contain information about a financial
instrument (e.g., real time odds or statistics associated with the
financial instrument closing positive). A banner ad may expand or
contract based on hovering, clicking, or other user interactions. A
banner ad may contain one or more links to drill down, refer to
another site, and/or provide more information about a financial
instrument. In some embodiments, FIG. 42 may represent a browser
add-on (e.g., a tool bar) which may contain information about a
financial instrument (e.g., real time odds or statistics associated
with the financial instrument closing positive).
[0240] FIG. 44 depicts a user interface 2900 for navigating studies
of financial instruments, in accordance with an embodiment of the
present disclosure. As depicted in FIG. 29, a user interface 2900
may provide an ability to scroll or otherwise navigate among a
listing of studies. The listing of studies may include study
details including name, creation date, author, description and
other metadata. The listing of studies may also provide one or more
metrics associated with the study such as, for example, a
cumulative percent return, an average percent return, a geometric
mean percent return, a best percent return, a worst percent return,
a number of trades, a percentage of trades having a positive
return, and a Sharpe ratio.
[0241] A user interface 2900 for navigating financial studies may
also provide user interface controls to access further
functionality. For example, a create new study user interface
control 2902 (e.g., a button, a link, a drop down, etc.) may
provide access to functionality for creating a new study. Studies
of financial instruments may also be grouped or classified and user
interface controls 2904 may be provided to access different
groupings of financial instrument studies (e.g., featured studies,
Kensho studies, studies grouped by author, studies classified by a
currently logged in user, etc.) Clicking on a study may allow a
user to drill down into or navigate to a study. Drilling down into
a study may provide study details and functionality related to a
study. Access to details of a study or functionality associated
with a study may be determined by a user's permissions, roles, and
access control list, group permissions, or other security
mechanisms. Right clicking on a study in a listing may provide
other user interface controls (e.g., publish a study, share a
study, add to favorites, delete a study, etc.). In some
embodiments, hovering over or mousing over a study in a listing may
also provide additional functionality or further details.
[0242] FIG. 45 depicts a user interface 2900 for navigating studies
of financial instruments, in accordance with an embodiment of the
present disclosure. FIG. 45 provides a listing of further exemplary
studies similar to those discussed above in reference to FIG.
44.
[0243] FIG. 46 depicts a user interface 3100 for viewing details of
a study of financial instruments, in accordance with an embodiment
of the present disclosure. According to some embodiments, study
details may include a description of the study, a title, an author,
and access to study results and trade history. Additional
functionality may be provided, such as, for example an ability to
delete a study or modify a study (e.g., via user interface controls
3102). A study may be a group of financial instruments modeled to
illustrate the effects of one or more market events or conditions.
For example, FIG. 31 may depict a study of the Russell 3000
following the last dispute between President Obama and Republicans
over raising the debt ceiling, which took place between July and
August of 2011. During this period the credit-rating agency
Standard & Poor's downgraded (on August 5th) the credit rating
of US government bond for the first time in the country's history.
Markets in the US then experienced their most volatile week since
the 2008 financial crisis, with the Dow Jones Industrial Average
plunging for 635 points (5.6%) in one day. An exemplary study in
FIG. 31 may examine which equities across the entire Russell 3000
survived best under the extreme volatility and market stress that
occurred during the debt ceiling sell-off of July 22-Aug. 19,
2011.
[0244] FIG. 47 depicts a user interface 3200 for a financial
instrument visualization, in accordance with an embodiment of the
present disclosure. According to some embodiments, FIG. 47 may
depict the study results for the study described above with respect
to FIG. 31. As illustrated in FIG. 47, one or more metrics
associated with the study may be displayed above a fractal
visualization 3202. Study metadata 3204 may also be displayed
(e.g., a study period of Jul. 22, 2011 to Aug. 19, 2011). Metrics
3206 associated with the study may include, for example, a
cumulative percent return, an average percent return, a geometric
mean percent return, a best percent return, a worst percent return,
a number of trades, a percentage of trades having a positive
return, and a Sharpe ratio.
[0245] A visualization 3202 of the study results may be a bar chart
that may be interactive. According to some embodiments, the
interactivity may be turned on or off via a user interface control
3208 (e.g., a link, a button, a drop down, etc.). Via an
interactive user interface 3200, a user may navigate study results
by zooming in or out of a bar chart. Zooming in may allow a user to
via a specific segment of study results. For example, FIG. 32 may
depict the returns of stocks of the Russell 3000 stock index. Due
to the large number of equities displayed (e.g., 3000 stocks), when
the chart is zoomed out to view the full range or returns (e.g.,
the entire chart), the individual components may not be visible
separately. According to some embodiments, one or more bench marks
may be displayed. For example, a benchmark (e.g., the S&P 500)
may be illustrated using a different colored bar. Further
functionality is described with reference to FIGS. 48-53 below.
[0246] FIG. 48 depicts a user interface 3300 for a financial
instrument visualization, in accordance with an embodiment of the
present disclosure. FIG. 48 may depict the study results of FIG. 47
with a bench mark highlighted. Moving a cursor over components of a
study or benchmarks included in a study may display metrics 3302
associated with the individual components. For example, moving a
cursor over a bar representing the S&P 500 benchmark for an
exemplary study of the Russell 3000 may provide metrics including a
cumulative return of -16.39% during the study period.
[0247] FIG. 49 depicts a user interface 3400 for viewing component
information of a financial instrument visualization, in accordance
with an embodiment of the present disclosure. FIG. 34 may depict
the study results of FIG. 49 with a lowest performing component of
a study highlighted.
[0248] FIG. 50 depicts a user interface 3500 for viewing component
information of a financial instrument visualization, in accordance
with an embodiment of the present disclosure. FIG. 35 may depict
the study results of FIG. 47 with a highest performing component of
a study highlighted.
[0249] FIG. 51 depicts a user interface 3600 for focusing a
financial instrument visualization, in accordance with an
embodiment of the present disclosure. FIG. 51 may depict the study
results of FIG. 47 focused or zoomed in to show a subset of study
results. A user may zoom in or out of study results using one or
more methods (e.g., a track pad, a mouse wheel, an arrow key, an
assigned function or letter key, etc.). According to some
embodiments, when study results a zoomed in or focused such that an
entire range of results may not be displayed on a user screen, a
user may navigate among the results. For example, if a user drills
down to focus on a subset of study components outperforming a
benchmark (e.g., to the right of the S&P 500 indicator in a bar
chart showing returns from lowest to highest), a user may navigate
to underperforming components by clicking and dragging to the left
of the benchmark indicator. Other forms of navigation may be
possible (e.g., arrow keys, a track pad, etc.)
[0250] FIG. 52 depicts a user interface 3700 for focusing a
financial instrument visualization, in accordance with an
embodiment of the present disclosure. FIG. 52 may depict the study
results of FIG. 47 focused or zoomed in to show a subset of study
results. As depicted in FIG. 52 when a zoom or focus level is
sufficient to provide display space, component metadata and metrics
3702 may be provided for one or more components (e.g., financial
instruments) of a study. For example, if study results are focused
enough a stock symbol, a return rate, a name, or other performance
metric may be provided. FIG. 52 may depict higher performing
components of the Russell 3000 during a period of the study.
[0251] FIG. 53 depicts a user interface 3800 for focusing a
financial instrument visualization, in accordance with an
embodiment of the present disclosure. FIG. 53 may depict the study
results of FIG. 47 focused or zoomed in to show a subset of study
results. FIG. 53 may depict lower performing components of the
Russell 3000 during a period of the study.
[0252] Clicking on an individual component of a study may provide
information about the component (e.g., a particular equity).
Additional functionality may be provided (e.g., an ability to buy
or sell the particular equity, an ability to view an impact of a
particular equity to one or more portfolios, an ability to add a
particular equity to a model portfolio, an ability to remove a
particular equity from a model portfolio, etc.). If an individual
component is an index or a benchmark, a user may drill down
further. For example, if a user clicks on the S&P 500 they may
drill down to view sector performance and then even further to view
the performance of individual components of a sector.
[0253] FIG. 54 depicts a user interface 3900 for viewing financial
instrument visualization component details, in accordance with an
embodiment of the present disclosure. According to some
embodiments, a chart 3902 providing component metrics for a study
may include for one or more components, for example, a stock
symbol, a cumulative percent return, an average percent return, a
geometric mean percent return, a best percent return, a worst
percent return, a number of trades, a percentage of trades having a
positive return, and a Sharpe ratio. Study result data may be
presented in rows and may be sortable by one or more of the columns
(e.g., alphabetically by stock symbol, lowest to highest by a
particular metric, highest to lowest by a particular metric, etc.).
A subset of results or all results may be selectable, exportable,
printed, emailed, or shared electronically (e.g., emailed, posted,
etc.). A study may also include a listing 3904 of trades associated
with a study components. Trade information may include one or more
of the following for components of a study including: a buy date
for a component, a sell date for a component, a percentage return
for a component, a buy price for a component, a sell price for a
component, and a symbol for a component.
[0254] FIG. 55 depicts a user interface 4000 for creating a study
of financial instruments, in accordance with an embodiment of the
present disclosure. As illustrated in FIG. 55, a user interface
control 4002 such as, for example, a drop down may be provided for
creating one or more studies. Studies may include, for example, a
conditional analysis, a cyclical analysis, an event analysis, a
relative analysis, a relative analysis with multiple date ranges, a
relative analysis from a starting date to present date, a relative
analysis for a current year to date, or other studies. Further
detail on creating studies is discussed below with respect to FIGS.
57-65.
[0255] FIG. 56 depicts a user interface 4100 for account access, in
accordance with an embodiment of the present disclosure. As
depicted in FIG. 56, user interface functionality may be provided
for accessing an account (e.g., user interface control 4102), for
password hints or resets (e.g., user interface control 4104), for
account creation (e.g., user interface control 4106), for account
information (e.g., user interface control 4108), and for additional
functionality. Accounts may be required to access studies, to
create studies, to edit studies, to delete studies, and/or to
publish or share studies. Different levels of accounts may be
provided that may have different functionality and/or access.
Accounts may require a fee, a subscription, may be free, or may be
provided on another basis. Different levels of access and
functionality may require different subscriptions or fees.
[0256] FIG. 57 depicts a user interface 4200 for creating a study
of financial instruments, in accordance with an embodiment of the
present disclosure. FIG. 57 may depict a user interface for
creation of a conditional analysis study which may accept one or
more user inputs 4202 to generate a study. For example, user inputs
4202 may include: a study title, a study description, a trigger
symbol (e.g., a stock symbol or benchmark used for conditional
analysis), a threshold or above/below parameter, a buy price, a
second above/below threshold parameter, a sell price, and a date
range for a study (e.g., a start date and an ending date).
Components of a study may be populated using tickers or financial
instrument symbols, a user list or portfolio of holdings, an index
(e.g., the Russell 3000, S&P 500, Sector components, etc.).
Other functionality may be provided (e.g., share a study, publish a
study, etc.) Generation of a study may allow a user to view results
as described above with reference to FIGS. 46-54.
[0257] FIG. 58 depicts a user interface 4300 for creating a study
of financial instruments, in accordance with an embodiment of the
present disclosure. FIG. 58 may depict a user interface for
creation of a cyclical analysis study which may accept one or more
user inputs 4302 to generate a study. For example, user inputs 4302
may include: a study title, a study description, a number of years
to look back, a starting month, a starting day, an ending month,
and an ending day. Components of a study may be populated using
tickers or financial instrument symbols, a user list or portfolio
of holdings, an index (e.g., the Russell 3000, S&P 500, Sector
components, etc.) Other functionality may be provided (e.g., share
a study, publish a study, etc.) Generation of a study may allow a
user to view results as described above with reference to FIGS.
46-54.
[0258] FIG. 59 depicts a user interface 4400 for creating a study
of financial instruments, in accordance with an embodiment of the
present disclosure. FIG. 59 may depict a user interface for
creation of an event analysis study which may accept one or more
user inputs 4402 to generate a study. For example, user inputs 4402
may include: a study title, a study description, an event type, an
event date, a relative start day, and a relative end day.
Components of a study may be populated using tickers or financial
instrument symbols, a user list or portfolio of holdings, an index
(e.g., the Russell 3000, S&P 500, Sector components, etc.)
Other functionality may be provided (e.g., share a study, publish a
study, etc.) Generation of a study may allow a user to view results
as described above with reference to FIGS. 46-54. Events are not
limited an may include market based announcements, government
reports, political events, natural disasters, press releases,
surveys, etc.
[0259] FIG. 60 depicts a user interface 4500 for entering
parameters for creating a study of financial instruments, in
accordance with an embodiment of the present disclosure. FIG. 60
illustrates a user interface control with a partial listing of
events available for an event analysis.
[0260] FIG. 61 depicts a user interface 4500 for entering
parameters for creating a study of financial instruments, in
accordance with an embodiment of the present disclosure. FIG. 61
illustrates a user interface control with a partial listing of
additional events available for an event analysis.
[0261] FIG. 62 depicts a user interface 4700 for creating a study
of financial instruments, in accordance with an embodiment of the
present disclosure. FIG. 62 may depict a user interface for
creation of a relative analysis study which may accept one or more
user inputs 4702 to generate a study. For example, user inputs 4702
may include: a study title, a study description, a start day, and
an end day. Components of a study may be populated using tickers or
financial instrument symbols, a user list or portfolio of holdings,
an index (e.g., the Russell 3000, S&P 500, Sector components,
etc.) Other functionality may be provided (e.g., share a study,
publish a study, etc.) Generation of a study may allow a user to
view results as described above with reference to FIGS. 46-54.
[0262] FIG. 63 depicts a user interface 4800 for creating a study
of financial instruments, in accordance with an embodiment of the
present disclosure. FIG. 63 may depict a user interface 4800 for
creation of a relative analysis study with multiple date ranges.
User inputs may be accepted via user input controls 4802.
[0263] FIG. 64 depicts a user interface 4900 for creating a study
of financial instruments, in accordance with an embodiment of the
present disclosure. FIG. 64 may depict a user interface 4900 for
creation of a relative analysis study from a specified start date
to a present date. User inputs may be accepted via user input
controls 4902.
[0264] FIG. 65 depicts a user interface 5000 for creating a study
of financial instruments, in accordance with an embodiment of the
present disclosure. FIG. 65 may depict a user interface for
creation of a year-to-date relative analysis study. User inputs may
be accepted via user input controls 5002.
[0265] FIG. 66 depicts a user interface 5100 for a financial
instrument visualization, in accordance with an embodiment of the
present disclosure. According to some embodiments, FIG. 66 may
depict study results associated with a study of best performing
energy companies in summer months. As illustrated in FIG. 66, one
or more metrics associated with the study may be displayed above a
fractal visualization 5102. Study metadata may also be displayed
(e.g., a study period of June first to September first over the
last 20 years). Metrics associated with the study may include, for
example, a cumulative percent return, an average percent return, a
geometric mean percent return, a best percent return, a worst
percent return, a number of trades, a percentage of trades having a
positive return, and a Sharpe ratio. As described above with
respect to FIGS. 47-53, a visualization of the study results may be
a bar chart that may be interactive. According to some embodiments,
the interactivity may be turned on or off via a user interface
control 5104 (e.g., a link, a button, a drop down, etc.). Via an
interactive user interface, a user may navigate study results by
zooming in or out of a bar chart. Zooming in may allow a user to
via a specific segment of study results.
[0266] FIG. 67 depicts a user interface 5200 for a financial
instrument visualization, in accordance with an embodiment of the
present disclosure. FIG. 67 may be a line graph corresponding to
the study results of FIG. 66. According to some embodiments, FIG.
67 may be interpreted as a line graph wherein vertical or angled
lines (either up or down) indicate that the given asset is being
held during this time period, because a condition in a study
defined by a user was active during that time period. Perfectly
horizontal lines indicate that the given asset is not being held by
the simulated study or strategy during this time period, because
the necessary conditions defined by the user in the study were not
all active during that time period. Therefore in the horizontal
sections of the line, price changes during that period are not
contributing to the total cumulative return or loss of the
strategy, and are not counted.
[0267] According to some embodiments, the line graph shows the
performance of the strategy asset-by-asset over time. This may be
useful because it speaks to the consistency of the study or
strategy both through time as well as across the assets in the
basket. Typically, a user would want to see consistency across both
dimensions. A good study or strategy may be one where (1) a given
asset moves up on most of the event days/condition periods over
time, and (2) on a given event day/condition period most assets in
the study move up. Such a strategy or study has good risk-adjusted
returns cross-sectionally and in the time-series is a win-win.
[0268] If the focus of the study is to see if a given event or
condition period has an effect on assets, a user may look for
assets to consistently move either up or down when the given event
or condition period is active. If a user sees effects across some
assets but not others, a user may remove the latter from the
strategy and try finding others that more consistently move either
up or down when the given event or period is active.
[0269] FIG. 68 depicts a user interface 5300 for a financial
instrument visualization, in accordance with an embodiment of the
present disclosure. According to some embodiments, FIG. 68 may
depict study results associated with a study of U.S. equity
performance during a last government shutdown of 1995-1996. The
United States federal government shutdown of 1995 and 1996 was the
result of conflicts between Democratic President Bill Clinton and
the Republican Congress over funding for Medicare, education, the
environment, and public health in the 1996 federal budget. The
government shut down after Clinton vetoed the spending bill the
Republican Party-controlled Congress sent him. The federal
government of the United States put non-essential government
workers on furlough and suspended non-essential services from Nov.
14 through Nov. 19, 1995 and from Dec. 16, 1995 to Jan. 6, 1996,
for a total of 28 days. The study of FIG. 53 may identify the U.S.
equities that led and lagged over these two periods. As illustrated
in FIG. 53, one or more metrics associated with the study may be
displayed above a fractal visualization. Study metadata may also be
displayed (e.g., a study period of Nov. 14, 1995-Nov. 19, 1995 and
Dec. 16, 1995-Jan. 6, 1996). Metrics associated with the study may
include, for example, a cumulative percent return, an average
percent return, a geometric mean percent return, a best percent
return, a worst percent return, a number of trades, a percentage of
trades having a positive return, and a Sharpe ratio. As described
above with respect to FIGS. 47-53, a visualization of the study
results may be a bar chart that may be interactive. According to
some embodiments, the interactivity may be turned on or off via a
user interface control (e.g., a link, a button, a drop down, etc.).
Via an interactive user interface, a user may navigate study
results by zooming in or out of a bar chart. Zooming in may allow a
user to via a specific segment of study results.
[0270] FIG. 69 depicts a user interface 5400 for a financial
instrument visualization, in accordance with an embodiment of the
present disclosure. FIG. 69 may be a line graph corresponding to
the study results of FIG. 68. According to some embodiments, FIG.
69 may be interpreted as a line graph wherein vertical or angled
lines (either up or down) indicate that the given asset is being
held during this time period, because a condition in a study
defined by a user was active during that time period. Perfectly
horizontal lines indicate that the given asset is not being held by
the simulated study or strategy during this time period, because
the necessary conditions defined by the user in the study were not
all active during that time period. Therefore in the horizontal
sections of the line, price changes during that period are not
contributing to the total cumulative return or loss of the
strategy, and are not counted. As illustrated in FIG. 69, an
individual component or line of a graph may be highlighted and
corresponding metadata for that component may be displayed. For
example, metrics such as a rate of return for a highest performing
component may be displayed (e.g., Chesapeake Energy).
[0271] According to some embodiments, a shade or color of a line
may vary depending on performance. For example, a line may be a
bright green for a high positive return percentage for the
corresponding financial instrument during a period of the study. A
line may be bright red for a high negative return during a period
of a study. Other colors or indicators may be used. A line may
change colors, shades, or indicators as the performance of a
corresponding financial instrument changes. A user may determine
color schemes or other indicators. In some embodiments, a user may
indicate holdings of a specified portfolio with a specified
indicator.
[0272] FIG. 70 depicts a user interface 5500 for a financial
instrument visualization, in accordance with an embodiment of the
present disclosure. FIG. 70 is another view of the line graph of
FIG. 69, but with a lowest performing component highlighted (e.g.,
Kla-Tencor Corp.).
[0273] According to some embodiments, line graphs, such as those
depicted in FIGS. 69 and 70, may provide an ability for a user to
zoom in or otherwise navigate view individual component or sector
performance. Line graphs may also contain one or more benchmarks
(e.g., S&P 500) that may be provided in a different color, a
different line pattern, or with another distinctive indicator.
[0274] FIG. 71 depicts a platform 5600 for financial instrument
visualization and modeling, in accordance with an embodiment of the
present disclosure. Element 5602 may represent a user interface
layer for developing and generating studies using templates, custom
algorithms, or a code interface for custom algorithm design.
Element 5604 may represent custom execution engines for processing
large volumes of financial and modeling data. Processing for models
may be distributed across multiple engines for better performance.
Element 5606 may represent high speed data availability clusters.
Element 5608 may represent cloud based infrastructure such as, for
example, a financial cloud service provided by one or more
exchanges. Element 5610 may represent large volumes of data (e.g.,
petabytes). Infrastructure such as that depicted in FIG. 71 may
provide an ability for complex computation in near real time. It
may also allow for the provision of software as a service SaaS.
Clients may be browser based clients including PCs, laptops, mobile
devices, etc. Platforms such as that depicted in FIG. 71 may allow
for data preparation including, but not limited to, scrubbing of
data, cleaning of data, standardizing of data (across multiple
asset types and/or multiple markets). Platforms such as that
depicted in FIG. 71 may also allow for high speed searching of
large scale financial data, large scale financial data management,
real-time probability analysis, predictive analytics, and financial
visualization.
[0275] According to some embodiments, such platforms may allow for
construction and modeling of synthetic assets (e.g., a set of
financial instruments selected to closely track the performance of
one or more other financial instruments, such as equities of a
supply chain for a manufacturing based equity wherein the supply
chain equities closely track the performance of the manufacturing
equity).
[0276] According to some embodiments, platforms such as that
depicted in FIG. 71 may provide machine learning. For example,
historical data may be analyzed to predict how long to hold a
position for a financial instrument.
[0277] FIG. 74 depicts a user interface for pushing statistical
market content to a user, in accordance with an embodiment of the
disclosure. FIG. 75 depicts a user interface for pushing
statistical market content to a user which provides further
statistical content of an event selected from an interface in FIG.
74, in accordance with an embodiment of the disclosure.
[0278] As depicted in FIGS. 74 and 75, notifications or alerts may
be sent in advance of events (e.g., economic data releases,
earnings releases, elections, other events scheduled or known in
advance). The notifications may contain statistical content
modeling the market impact of different scenarios based on surfaced
(statistically identified in historical data) past results for each
scenario. This may allow a user to position a trade or hedge in
advance of a surprise. A user may thus hedge against previously
unknown major market implications of certain scenarios (based on
past reactions to similar cases and based on historical market
data) statistically identified. For example, FIGS. 74 and 75 may
model the impact of a projected housing starts report on the return
of one or more sectors or financial instruments in advance of the
release of any report. A user may specify a projected report result
and model an impact on the return of multiple sectors and financial
instruments.
[0279] FIG. 76 depicts a user interface for modeling the impact of
breaking political events, in accordance with an embodiment. FIG.
76 depicts a chart illustrating an impact of the Indian general
election. As illustrated, if the BJP wins the upcoming Indian
General Election, the Rupee statistically will decline over the
following week, temporarily reversing its secular rise since 2008,
based on the five prior occasions when the BJP won state-level
elections.
[0280] FIG. 77 depicts a user interface for modeling the impact of
breaking political events, in accordance with an embodiment.
Whereas "Breaking" alerts covers geopolitical events that have just
happened, "To Watch" alerts covers geopolitical events that are
known in advance (e.g., an impact based on a modeled outcome in
advance).
[0281] FIG. 78 depicts a user interface for modeling the impact of
breaking political events, in accordance with an embodiment.
[0282] FIG. 79 depicts a user interface for modeling the impact of
breaking political events, in accordance with an embodiment.
[0283] FIG. 80 depicts a user interface for modeling the impact of
breaking political events, in accordance with an embodiment.
[0284] FIG. 81 depicts a notification modeling a market impact of a
potential event, in accordance with an embodiment.
[0285] FIG. 82 depicts a notification modeling a market impact of a
potential event, in accordance with an embodiment.
[0286] FIG. 83 depicts a notification modeling a market impact of a
potential event, in accordance with an embodiment.
[0287] FIG. 84 depicts a notification modeling a market impact of a
potential event, in accordance with an embodiment.
[0288] FIG. 85 depicts a notification modeling a market impact of a
potential event, in accordance with an embodiment.
[0289] FIG. 86 depicts a notification modeling a market impact of a
potential event, in accordance with an embodiment.
[0290] FIG. 87 illustrates a user interface for modeling consensus
and surprise analysis, in accordance with an embodiment. Provides a
UI whereby a user can model how a basket of assets reacted to an
arbitrary surprise or disappointment (meaning the difference
between average consensus and actual number) for any major economic
data release (such as Unemployment, CPI, PPI etc). The user can
choose the economic metric, and select any range of surprise or
disappointment, expressed in the units of the metric, or in units
of the standard deviations of prior surprises (e.g. a 1.SD
difference). The user can also choose the buy and sell days
relative to the economic data release, and the assets modeled.
[0291] FIG. 88 depicts a user interface for economic regime
analysis in accordance with an embodiment. A user can select a
combination of macroeconomic factors (in this embodiment, US GDP
growth, CPI, US Unemployment rates, US Federal Funds rate, and
Volatility), and model how asset prices moved during periods when
economic conditions reflected that precise combination of factors.
The user is shown the range of those metrics (record high to record
low) and can select, by means of sliders or other visual cues, the
exact values within which the assets should be modeled. The system
provides instant feedback to the user about the number of days
since 1990 existed on which that combination of factors was
true--this alone is a unique capability of the system and
represents an enormous labor saving over current practice. The user
can model any combination of assets during the periods when the
selected factors had the values chosen.
[0292] FIG. 89 depicts illustrates a user interface for modeling
consensus and surprise analysis, in accordance with an embodiment.
Provides a UI whereby a user can model how a basket of assets
reacted to an arbitrary surprise or disappointment (meaning the
difference between average consensus and actual number) for any
major economic data release (such as Unemployment, CPI, PPI etc).
The user can choose the economic metric, and select any range of
surprise or disappointment, expressed in the units of the metric,
or in units of the standard deviations of prior surprises (e.g. a
1.SD difference). The user can also choose the buy and sell days
relative to the economic data release, and the assets modeled.
[0293] A user can study what happens when economic data releases or
earnings releases exceed or miss expectations, by entering
different thresholds for either the absolute or relative value of
the delta from consensus, (including specifying certain standard
deviations from normal), by constraining the dates of the
observations) and you can model the impact on different assets by
entering them.
[0294] FIG. 90 depicts a user interface for economic regime
analysis in accordance with an embodiment.
[0295] A user can select a combination of macroeconomic factors (in
this embodiment, US GDP growth, CPI, US Unemployment rates, US
Federal Funds rate, and Volatility), and model how asset prices
moved during periods when economic conditions reflected that
precise combination of factors. The user is shown the range of
those metrics (record high to record low) and can select, by means
of sliders or other visual cues, the exact values within which the
assets should be modeled. The system provides instant feedback to
the user about the number of days since 1990 existed on which that
combination of factors was true--this alone is a unique capability
of the system and represents an enormous labor saving over current
practice. The user can model any combination of assets during the
periods when the selected factors had the values chosen.
[0296] Other embodiments are within the scope and spirit of the
invention. For example, the functionality described above can be
implemented using software, hardware, firmware, hardwiring, or
combinations of any of these. One or more computer processors
operating in accordance with instructions may implement the
functions associated with generating and/or delivering electronic
education in accordance with the present disclosure as described
above. If such is the case, it is within the scope of the present
disclosure that such instructions may be stored on one or more
non-transitory processor readable storage media (e.g., a magnetic
disk or other storage medium). Additionally, modules implementing
functions may also be physically located at various positions,
including being distributed such that portions of functions are
implemented at different physical locations.
[0297] The present disclosure is not to be limited in scope by the
specific embodiments described herein. Indeed, other various
embodiments of and modifications to the present disclosure, in
addition to those described herein, will be apparent to those of
ordinary skill in the art from the foregoing description and
accompanying drawings. Thus, such other embodiments and
modifications are intended to fall within the scope of the present
disclosure. Further, although the present disclosure has been
described herein in the context of a particular implementation in a
particular environment for a particular purpose, those of ordinary
skill in the art will recognize that its usefulness is not limited
thereto and that the present disclosure may be beneficially
implemented in any number of environments for any number of
purposes. Accordingly, the claims set forth below should be
construed in view of the full breadth and spirit of the present
disclosure as described herein.
* * * * *