Market value matrix

• US 20120290505A1
• Filed: 07/25/2012
• Published: 11/15/2012
• Est. Priority Date: 12/30/2003
• Status: Abandoned Application

• Current Assignee: Jeffrey Eder
• Original Assignee: Jeffrey Eder
• Inventors: Eder, Jeffrey Scott
• Application Number:
  US13/557,836
• Publication Number:
  US 20120290505A1
• Time in Patent Office: Days
• US Class Current: 705/36.R
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

An apparatus, computer program product and system for using artificial intelligence based cognitive learning methods to measure, manage and report value, risk and return for a portfolio on a continual basis. The elements of value, external factors and segments of value of the portfolio are analyzed and modeled by item using predictive models that are developed by learning from the data associated with said portfolio. Scenarios of both normal and extreme situations are also developed. The scenarios are then used to drive simulations of the predictive models. The output from these simulations are then used to calculate risks and a risk adjusted value for the elements of value, the items within each element of value, the external factors and the items within each external factor. The optimal mix of changes to the portfolio at the item level are also identified and presented to the user.

Claims

1. An intelligent system for portfolio management comprising:

a computer with at least one processor having circuitry to execute instructions;

a storage device available to each processor with sequences of instructions stored therein, which when executed cause the at least one processor to;

prepare a plurality of data representative of a portfolio for processing where said portfolio comprises a plurality of segments of value, where one or more elements of value and one or more external factors has a net contribution to or impact on a value of each of the segments of value and where each of the elements of value and each of the external factors consists of a plurality of items,

develop a predictive model for each of the segments of value that quantifies the impact by item of the elements of value and the external factors on the value the segment of value by learning from at least part of said data where a linearity of each predictive model is determined by learning from the data,

identify one or more scenarios,

simulate a value of the portfolio using said predictive models under each scenario in order to quantify a plurality of portfolio risks by item,

calculate a value for each item under each scenario by combining the risks of each item with the impacts of said item, and

output the impact by item, the risks by item and the value by item.

2. The system of claim 1, wherein developing the predictive model for each of the segments of value that quantifies the impact by item of the elements of value and the external factors on the value of the segments of value by learning from at least part of the data comprises:

using a plurality of predictive models and a plurality of causal models to analyze and select a portion of the data as one or more value drivers for use as an input when modeling an impact of each of the one or more elements of value;

using the plurality of predictive models and the plurality of causal models to analyze and select a portion of the data as one or more factor value drivers for use as an input when modeling an impact each of the one or more external factors;

learning which algorithm from a plurality of linear and nonlinear predictive model algorithms to include in the model for each of the segments of value in order to model a net contribution or impact of each of the one or more elements of value by item and each of each of the one or more external factors by item to a value of each of the segments of value;

learning which model from a plurality of causal models comprises a best fit for modeling the contribution of the elements of value and the external factors to the value of each of the segments of value when using the selected value driver data;

learning if a clustering of the input data improves an accuracy of the segment of value models;

learning a relative contribution of each of the elements of value to the value of each of the segments of value,

learning a relative contribution of each of the external factors to the value of each of the segments of value, and

learning a relative contribution of each of the external factors to the portfolio value

where the plurality of causal models are selected from the group consisting of Tetrad, LaGrange, Bayesian and path analysis and where the plurality of predictive models are selected from the group consisting of classification and regression tree;

projection pursuit regression;

generalized additive model (GAM), redundant regression network;

neural network, multivariate adaptive regression splines;

linear regression; and

stepwise regression.

3. The system of claim 1, wherein the processors identify and output one or more changes at the item level that will optimize one or more aspects of a portfolio financial performance selected from the group consisting of a total portfolio return, a total portfolio risk and a total portfolio value for each of the one or more scenarios.

4. The system of claim 1, wherein the one or more scenarios are selected from the group consisting of normal and extreme where the extreme scenario is developed by using a peak over threshold algorithm.

5. The system of claim 1, wherein the portfolio comprises an organization that physically exists and wherein the one or more elements of value physically exist and are selected from the group consisting of:

alliances, brands, channels, customers, employees, information technology, processes, vendors and combinations thereof.

6. The system of claim 1, wherein the segments of value are selected from the group consisting of derivatives, investments, real options, market sentiment and combinations thereof where developing a model of the market sentiment segment of value comprises a top down analysis of portfolio value and risk.

7. The system of claim 1, wherein the plurality of risks are selected from the group consisting of event risks, element variability, factor variability and volatility where each risk consists of an expected reduction in value and where an event risk with a known expected reduction in value comprises a contingent liability that is measured using a real option algorithm.

8. A non-transitory computer program product tangibly embodied on a computer readable medium and comprising a program code for directing at least one computer to perform an intelligent portfolio management method, comprising:

prepare a plurality of data representative of a portfolio for processing where said portfolio comprises a plurality of segments of value, where one or more elements of value and one or more external factors has a net contribution to or impact on a value of each of the segments of value and where each of the elements of value and each of the external factors consists of a plurality of items,

develop a predictive model for each of the segments of value that quantifies the impact by item of the elements of value and the external factors on the value the segment of value by learning from at least part of said data where a linearity of each predictive model is determined by learning from the data,

identify one or more scenarios,

simulate a value of the portfolio using said predictive models under each scenario in order to quantify a plurality of portfolio risks by item,

calculate a value for each item under each scenario by combining the risks of each item with the impacts of said item, and

output the impact by item, the risks by item and the value by item.

9. The computer program product of claim 8, wherein developing the predictive model for each of the segments of value that quantifies the impact by item of the elements of value and the external factors on the value of the segments of value by learning from at least part of the data comprises:

using a plurality of predictive models and a plurality of causal models to analyze and select a portion of the data as one or more value drivers for use as an input when modeling an impact of each of the one or more elements of value;

using the plurality of predictive models and the plurality of causal models to analyze and select a portion of the data as one or more factor value drivers for use as an input when modeling an impact each of the one or more external factors;

learning which algorithm from a plurality of linear and nonlinear predictive model algorithms to include in the model for each of the segments of value in order to model a net contribution or impact of each of the one or more elements of value by item and each of each of the one or more external factors by item to a value of each of the segments of value;

learning which model from a plurality of causal models comprises a best fit for modeling the contribution of the elements of value and the external factors to the value of each of the segments of value when using the selected value driver data;

learning if a clustering of the input data improves an accuracy of the segment of value models;

learning a relative contribution of each of the elements of value to the value of each of the segments of value,

learning a relative contribution of each of the external factors to the value of each of the segments of value, and

learning a relative contribution of each of the external factors to the portfolio value

where the plurality of causal models are selected from the group consisting of Tetrad, LaGrange, Bayesian and path analysis and where the plurality of predictive models are selected from the group consisting of classification and regression tree;

projection pursuit regression;

generalized additive model (GAM), redundant regression network;

neural network, multivariate adaptive regression splines;

linear regression; and

stepwise regression.

10. The computer program product of claim 8, wherein the method further comprises identifying and outputting one or more changes at the item level that will optimize one or more aspects of a portfolio financial performance selected from the group consisting of a total portfolio return, a total portfolio risk and a total portfolio value for each of the one or more scenarios.

11. The computer program product of claim 8, wherein the one or more scenarios are selected from the group consisting of normal and extreme where the extreme scenario is developed by using a peak over threshold algorithm.

12. The computer program product of claim 8, wherein the portfolio comprises an organization that physically exists and wherein the one or more elements of value physically exist and are selected from the group consisting of:

alliances, brands, channels, customers, employees, information technology, processes, vendors and combinations thereof.

13. The computer program product of claim 8, wherein the segments of value are selected from the group consisting of derivatives, investments, real options, market sentiment and combinations thereof where developing a model of the market sentiment segment of value comprises a top down analysis of portfolio value and risk.

14. The computer program product of claim 8, wherein the plurality of risks are selected from the group consisting of event risks, element variability, factor variability and volatility where each risk consists of an expected reduction in value and where an event risk with a known expected reduction in value comprises a contingent liability that is measured using a real option algorithm.

15. An intelligent portfolio management apparatus, comprising:

means for data acquisition,

means for data storage,

means for data processing,

means for preparing a plurality of data representative of a portfolio for processing where said portfolio comprises a plurality of segments of value, where one or more elements of value and one or more external factors has a net contribution to or impact on a value of each of the segments of value and where each of the elements of value and each of the external factors consists of a plurality of items,

means for developing a predictive model for each of the segments of value that quantifies the impact by item of the elements of value and the external factors on the value the segment of value by learning from at least part of said data where a linearity of each predictive model is determined by learning from the data,

means for identifying one or more scenarios,

means for simulating a value of the portfolio using said predictive models under each scenario in order to quantify a plurality of portfolio risks by item,

means for calculating a value for each item under each scenario by combining the risks of each item with the impacts of said item, and

means for outputting the impact by item, the risks by item and the value by item.

16. The apparatus of claim 15, wherein the means for developing the predictive model for each of the segments of value that quantifies the impact by item of the elements of value and the external factors on the value of the segments of value by learning from at least part of the data comprises:

means for using a plurality of predictive models and a plurality of causal models to analyze and select a portion of the data as one or more value drivers for use as an input when modeling an impact of each of the one or more elements of value;

means for using the plurality of predictive models and the plurality of causal models to analyze and select a portion of the data as one or more factor value drivers for use as an input when modeling an impact each of the one or more external factors;

means for learning which algorithm from a plurality of linear and nonlinear predictive model algorithms to include in the model for each of the segments of value in order to model a net contribution or impact of each of the one or more elements of value by item and each of each of the one or more external factors by item to a value of each of the segments of value;

means for learning which model from a plurality of causal models comprises a best fit for modeling the contribution of the elements of value and the external factors to the value of each of the segments of value when using the selected value driver data;

means for learning if a clustering of the input data improves an accuracy of the segment of value models;

means for learning a relative contribution of each of the elements of value to the value of each of the segments of value,

means for learning a relative contribution of each of the external factors to the value of each of the segments of value, and

means for learning a relative contribution of each of the external factors to the portfolio value

where the plurality of causal models are selected from the group consisting of Tetrad, LaGrange, Bayesian and path analysis and where the plurality of predictive models are selected from the group consisting of classification and regression tree;

projection pursuit regression;

generalized additive model (GAM), redundant regression network;

neural network, multivariate adaptive regression splines;

linear regression; and

stepwise regression.

17. The apparatus of claim 15, that further comprises means for identifying and outputting one or more changes at the item level that will optimize one or more aspects of a portfolio financial performance selected from the group consisting of a total portfolio return, a total portfolio risk and a total portfolio value for each of the one or more scenarios.

18. The apparatus of claim 15, wherein the one or more scenarios are selected from the group consisting of normal and extreme where the extreme scenario is developed by using a peak over threshold algorithm.

19. The apparatus of claim 15, wherein the portfolio consists of an organization that physically exists and wherein the one or more elements of value physically exist and are selected from the group consisting of:

alliances, brands, channels, customers, employees, information technology, processes, vendors and combinations thereof.

20. The apparatus of claim 15, wherein the segments of value are selected from the group consisting of derivatives, investments, real options, market sentiment and combinations thereof where developing a model of the market sentiment segment of value comprises a top down analysis of portfolio value and risk and wherein the plurality of risks are selected from the group consisting of event risks, element variability, factor variability and volatility where each risk consists of an expected reduction in value and where an event risk with a known expected reduction in value comprises a contingent liability that is measured using a real option algorithm.