Predicting economic conditions

• US 9,639,897 B2
• Filed: 05/04/2016
• Issued: 05/02/2017
• Est. Priority Date: 11/12/2013
• Status: Active Grant

• Current Assignee: Bank of America Corp.
• Original Assignee: Bank of America Corp.
• Inventors: Bendel, Timothy J., Krein, Mark V., Joffe, David N., Setiawan, Sandi, Nobili, William Anthony, Joa, David
• Primary Examiner(s): Norman, Samica L
• Assistant Examiner(s): Jacob, William J
• Application Number:
  US15/146,345
• Publication Number:
  US 20160247234A1
• Time in Patent Office: 363 Days
• Field of Search: 706 21
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

Computer-implemented methods for identifying or assessing any type of risk and/or opportunity that may arise can include either, alone or in combination, band pass filtering, principal component analysis, random matrix theory analysis, synchronization analysis, and early-warning detection. Each technique can also be viewed as a process that takes a set of inputs and converts it to a set of outputs. These outputs can be used as inputs for a subsequent process or the outputs may be directly actionable for formulating certain economic predictions to make certain decisions.

Claims

1. An apparatus comprising:

an early warning system;

a processor; and

memory storing computer readable instructions that, when executed by the processor, cause the apparatus to;

receive economic data of multiple time series for an economic system;

determine correlation coefficient values using the data of the multiple time series;

create a matrix using the correlation coefficient values;

determine the nearest neighbor spacing distribution;

determine lowest values of correlation coefficients and set the lowest values of correlation coefficients iteratively to zero;

determine a threshold level of a control parameter, wherein the threshold level is detected once a phase transition occurs and wherein a modeled distribution for the economic data changes from a first distribution to a second distribution;

output an economic instability prediction based on the threshold level;

generate, based on the economic instability prediction, an early warning signal about the economic data, wherein the early warning signal is indicative of whether the threshold level is approaching and wherein the early warning signal comprises an early warning notification and detailed information about a potential instability of the economic data;

determine a set of registered computing devices associated with the economic system, wherein the set includes a first device and a second device;

send the early warning notification about the economic system to the first and second devices;

initiate launching of an early warning application at the first device; and

send the detailed information about the potential instability to the first device via the early warning application.

2. The apparatus of claim 1 wherein the nearest neighbor spacing distribution is determined by taking the differences between nearest neighbor eigen-values derived from a correlation coefficient matrix.

3. The apparatus of claim 2, wherein the memory storing the computer readable instructions that, when executed by the processor, further cause the apparatus to:

diagonalize the correlation coefficient matrix until the phase transition occurs.

4. The apparatus of claim 1 wherein the threshold level is determined by using a chi-square test.

5. The apparatus of claim 4 wherein the threshold level is determined additionally by detecting a sharp phase transition from a Gaussian Orthogonal Ensemble distribution to a Poisson distribution.

6. The apparatus of claim 1 wherein standard Pearson correlation coefficient values are determined using the data of multiple time series.

7. The apparatus of claim 1, wherein the memory stores computer readable instructions that, when executed by the electronic processing device, further cause the apparatus to:

send an invite message to another device, wherein the invite message invites the other device to communicate with the first device.

8. The apparatus of claim 1, wherein the memory stores computer readable instructions that, when executed by the electronic processing device, further cause the apparatus to:

when the acknowledgment to the warning notification is received from the first device, identify a device type of the first device;

determine a first display characteristic of the first device from the device type; and

send a subset of information components to the first device based on the first display characteristic, wherein the detailed information comprises a plurality of information components.

9. The apparatus of claim 1 further comprises:

a data structure, wherein the data structure maps decision-making roles of individuals with associated device identities, wherein the device identities identify a plurality of devices; and

wherein the memory device storing computer readable instructions that, when executed by the electronic processing device, further causes the apparatus to establish a first communication connection with the plurality of devices based on the data structure.

10. The apparatus of claim 9 wherein the memory stores computer readable instructions that, when executed by the electronic processing device, further cause the apparatus to:

establish a second communication connection with a subset of the plurality of devices based on the data structure; and

prevent content of the second communication connection from being conveyed to the first communication connection.

11. The apparatus of claim 1 wherein the memory stores computer readable instructions that, when executed by the electronic processing device, further cause the apparatus to:

send an alert indication to a designated recipient through an access-id application, wherein the recipient is informed to check at least one of the set of registered computing devices.

12. A method comprising:

receiving, by a processing system, economic data of multiple time series;

determining, by the processing system, correlation coefficient values using the data of the multiple time series;

creating, by the processing system, a matrix using the correlation coefficient values;

determining, by the processing system, the nearest neighbor spacing distribution;

determining, by the processing system, lowest values of correlation coefficients and setting the lowest values of correlation coefficients iteratively to zero;

determining, by the processing system, a threshold level of a control parameter, wherein the threshold level is detected once a phase transition occurs and wherein a modeled distribution for the economic data changes from a first distribution to a second distribution;

outputting, by the processing system, an economic instability prediction based on the threshold level;

generating, by the processing system, an early warning signal based on the economic instability prediction for an economic system, wherein the early warning signal is indicative of whether the threshold level is approaching and wherein the early warning signal comprises an early warning notification and detailed information about a potential instability of the economic system;

sending the early warning notification about the economic system to a primary device;

when an acknowledgement is received from the primary device, sending the detailed information about the instability to the primary device; and

when the acknowledgement is not received from the primary device,

determining a set of registered computing devices associated with the economic system, wherein the set includes a first device and a second device;

sending the early warning notification about the economic system to the first and second devices;

initiating a launching of an early warning application at the primary device;

remotely connecting the primary device to the first device; and

sending the detailed information to the first device via the primary device.

13. The method of claim 12 wherein the nearest neighbor spacing distribution is determined by taking the differences between nearest neighbor eigen-values derived from a correlation coefficient matrix.

14. The method of claim 12 wherein the threshold level is determined by using a chi-square test.

15. The method of claim 14 wherein the threshold level is determined by detecting a sharp phase transition from a Gaussian Orthogonal Ensemble distribution to a Poisson distribution.

16. The method of claim 12 wherein standard Pearson correlation coefficient values are determined using the data of multiple time series.

17. One or more non-transitory computer-readable media having instructions stored thereon that, when executed, cause at least one computing device to:

receive economic data of multiple time series for an economic system;

determine correlation coefficient values using the data of the multiple time series;

create a matrix using the correlation coefficient values;

determine the nearest neighbor spacing distribution;

determine lowest values of correlation coefficients and set the lowest values of correlation coefficients iteratively to zero;

determine a threshold level of a control parameter by using a chi-square test, wherein the threshold occurs at an observed phase transition and wherein a modeled distribution for the economic data changes from a first distribution to a second distribution;

output an economic instability prediction based on the threshold level;

generate an early warning signal from the economic instability prediction for the economic system, wherein the early warning signal is indicative of whether the threshold level is approaching and wherein the early warning signal comprises and early warning notification and detailed information about a potential instability of the economic system;

determine a set of registered computing devices associated with the economic system, wherein the set comprises a first device and a second device;

send the early warning notification about the economic system to the first and second devices;

initiate of an early warning application at the first device; and

send the detailed information about the potential instability to the first device via the early warning application.

18. The one or more non-transitory computer-readable media of claim 17 wherein the nearest neighbor spacing distribution is determined by taking the differences between nearest neighbor eigen-values derived from a correlation coefficient matrix.

19. The one or more non-transitory computer-readable media of claim 17 wherein the threshold level is determined by detecting a sharp phase transition from a Gaussian Orthogonal Ensemble distribution to a Poisson distribution.

20. The one or more non-transitory computer-readable media of claim 17 wherein standard Pearson correlation coefficient values are determined using the data of multiple time series.

21. An apparatus comprising:

a processor; and

memory storing computer readable instructions that, when executed by the processor,

cause the apparatus to;

obtain economic data of multiple time series of interest about an economic system;

determine correlation coefficient values using the data;

form a matrix of the correlation values and determine eigen-values of the correlation matrix;

form a random matrix counterpart with a known eigen-value density distribution;

compare statistics of the eigen-values of the data correlation matrix to values of the random matrix counterpart of the known eigen-value distribution;

determine a difference between a quantity of the eigen-values of the data correlation matrix and the random matrix counterpart;

output a retained number of principal components to be retained based on the difference between the quantity of the eigen-values of the data correlation matrix and the random matrix counterpart; and

determine, from the retained number, a driver of trends determined from the economic data;

include an indicator about the driver in an early warning signal, wherein the early warning signal comprises an early warning notification and detailed information about a potential instability of the economic system;

determine a set of registered computing devices associated with the economic system, wherein the set includes a first device and a second device;

initiate a launching of an early warning application at the first device; and

send the detailed information about the potential instability to the first device via the early warning application.

22. The apparatus of claim 21 wherein standard Pearson correlation coefficients are obtained using the data.

23. The apparatus of claim 21, wherein the memory storing the computer readable instructions that, when executed by the processor, further cause the apparatus to:

determine maximum values of observed eigen-values;

determine an observed quantity of observed eigen-values greater than the maximum values; and

approximate the retained number of principle components by the observed quantity.

24. The apparatus of claim 21, wherein the memory storing the computer readable instructions that, when executed by the processor, further cause the apparatus to:

analyze micro level data after the driver has been determined.