Systems and methods for scenario simulation

Patent metadata

Field	Value
Publication number	US-2019294633-A1
Application number	US-201815897010-A
Country	US
Kind code	A1
Filing date	Feb 14, 2018
Priority date	May 1, 2017
Publication date	Sep 26, 2019
Grant date	—

Abstract

Systems and methods for automatically generating scenarios and user interface elements representing valuations of instruments under the scenarios are described. The systems and methods use expert polling systems and machine learning rules to generate tree data storage structures representing different scenarios of macro factors for outcomes of events. Machine implemented interfaces for expert polling, presentment of scenarios, and interaction with scenarios are also provided.

First claim

What is claimed is:

1 . A method for dynamically generating data structures representing scenarios and user interface elements using artificial intelligence, polling and network theory, the method comprising:
processing a plurality of data feeds by applying a first set of rules to generate an event from a plurality of events defined by the first set of rules, the event linked to a set of outcomes; generating a set of macro factors by applying a second set of rules to the event; obtaining a third set of rules that define a plurality of poll questions; processing a subset of the set of macro factors by applying the third set of rules to generate a subset of poll questions, each poll question linked to a macro factor of the subset of macro factors and a range of input responses acceptable as data values for the macro factor; generating and displaying a user interface with visual elements for the poll questions linked to macro factors and the ranges of input responses acceptable as the data values for the macro factors; generating a graph data storage structure representing scenarios for the macro factors and the set of outcomes, each node in the graph data storage structure defining a descriptor and a data value, the graph data storage structure including an event node corresponding to a root node, outcome nodes connected to the root nodes, and macro factor nodes connected to the outcome nodes, each macro factor node including a data value; receiving, at the user interface, selected input responses to the poll questions; obtaining a fourth set of rules that compute the data values for the macro factor nodes and filter the input responses for bias based on sentiment factors; processing the filtered input responses by applying the fourth set of rules to generate the data values for the macro factor nodes; populating the graph data storage structure with the data values for the macro factor nodes to generate scenarios for the outcome nodes; and updating the user interface to produce further visual elements indicating a distribution of responses.

2 . The method of claim 1 , wherein generating the set of macro factors by applying the second set of rules to the event involves deep learning on historical data.

3 . The method of claim 1 , wherein generating the set of macro factors by applying the second set of rules to the event involves regression on historical data.

4 . The method of claim 1 , wherein the data values for the macro factor are computed based on the distribution of responses.

5 . The method of claim 1 , wherein the data values for the macro factor nodes include a range to an extreme.

6 . The method of claim 1 , wherein the data values for the macro factor nodes include a probability for increasing or decreasing in value.

7 . A device for generating scenarios and user interface elements representing valuations of instruments under the scenarios, the device comprising:
a data storage device; and a processor configured to: receive a plurality of data feeds and applying a first set of rules to generate an event, the event linked to a set of outcomes; generate a set of macro factors for the event; generate a subset of poll questions for a subset of the set of macro factors, each poll question linked to a macro factor of the subset of macro factors and a range of input responses acceptable as data values for the macro factor; generate a user interface with visual elements for the poll questions linked to macro factors and the ranges of input responses acceptable as the data values for the macro factors; generate a graph data storage structure representing scenarios for the macro factors and the set of outcomes, each node in the graph data storage structure defining a descriptor and a data value, the graph data storage structure including an event node corresponding to a root node, outcome nodes connected to the root nodes, and macro factor nodes connected to the outcome nodes, each macro factor node including a data value; receive, at the user interface, selected input responses to the poll questions; compute the data values for the macro factor nodes using the selected input responses filtered by sentiment factors to automatically detect bias; populate the graph data storage structure with the data values for the macro factor nodes to generate scenarios for the outcome nodes; and update the user interface to produce further visual elements indicating a distribution of responses or valuation of portfolio.

8 . The device of claim 7 , wherein the processor generates the set of macro factors using deep learning on historical data.

9 . The device of claim 7 , wherein the processor generates the set of macro factors using regression on historical data.

10 . The device of claim 7 , wherein the data values for the macro factor are computed based on the distribution of responses.

11 . The device of claim 7 , wherein the data values for the macro factor nodes include a range to an extreme.

12 . The device of claim 7 , wherein the data values for the macro factor nodes include a probability for increasing or decreasing in value.

13 . A method for generating scenarios and user interface elements representing valuations of instruments under the scenarios comprising:
obtaining a first set of rules that define a plurality of events; processing a plurality of data feeds by applying the first set of rules to generate an event from the plurality of events, the event linked to a set of outcomes; obtaining a second set of rules that define a plurality of macro factors; processing the event by applying the second set of rules to generate a subset of macro factors; obtaining a third set of rules that define a plurality of poll questions; processing the subset of macro factors by applying the third set of rules to generate a subset of poll questions, each poll question linked to a macro factor of the subset of macro factors and a range of input responses acceptable as data values for the macro factor; generating and displaying a user interface with visual elements for the poll questions linked to macro factors and the ranges of input responses acceptable as the data values for the macro factors; generating a graph data storage structure representing scenarios for the macro factors and the set of outcomes, each node in the graph data storage structure defining a descriptor and a data value, the graph data storage structure including an event node corresponding to a root node, outcome nodes corresponding to children of the root nodes, and macro factor nodes corresponding to further children of the outcome nodes, each macro factor node including a data value; receiving, at the user interface, selected input responses to the poll questions; obtaining a fourth set of rules that compute the data values for the macro factors nodes; processing the selected input responses by applying the fourth set of rules to generate the data values for the macro factors nodes and to filter the selected input responses for bias; populating the graph data storage structure with the data values for the macro factors nodes to generate scenarios for the outcome nodes; and updating the user interface to produce further visual elements indicating a distribution of the filtered input responses and the scenarios of the graph data storage structure.

14 . The method of claim 13 , wherein each outcome node of the graph data storage structure defines a subtree of 2 n paths of macro factor nodes, each path corresponding to a scenario, n being a number of macro factors in the subset of macro factors.

15 .

Assignees

• Goldman Sachs & Co Llc

Inventors

• Dembo Ron
• Pawar Atul
• Nahum Ezra
• Phillips Andrew

Classifications

• G06Q40/06 Primary

  Asset management; Financial planning or analysis · CPC title

• G06F40/242

  Dictionaries · CPC title

• G06F16/9024 Primary

  Graphs; Linked lists (G06F16/9027 takes precedence) · CPC title

• G06N5/027

  Frames · CPC title

• G06F3/04847

  Interaction techniques to control parameter settings, e.g. interaction with sliders or dials · CPC title