Method and system for analysis, display and dissemination of financial information using resampled statistical methods
DC CAFCFirst Claim
1. A method for calculating, analyzing and displaying investment data comprising the steps of:
- (a) selecting a sample space, wherein the sample space includes at least one investment data sample;
(b) generating a distribution function using a re-sampled statistical method and a bias parameter, wherein the bias parameter determines a degree of randomness in a resampling process; and
, (c) generating a plot of the distribution function.
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Abstract
The present invention provides a method and system for the statistical analysis, display and dissemination of financial data over an information network such as the Internet and WWW. The present invention utilizes resampled statistical methods for the analysis of financial data. Resampled statistical analysis provides a meaningful and reasonable statistical description of financial information, which typically escapes modeling using parametric methods (i.e. assumptions of a Gaussian distribution). The present invention provides at least a GUI that provides functionality for user input of statistical queries, a statistical computation engine that performs statistical analysis of financial data and a graphical rendering engine that generates graphical display of statistical distributions generated by the statistical computation engine. According to one embodiment, the present invention employs a parallel processing architecture to speed generation of the resampled statistics.
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Citations
31 Claims
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1. A method for calculating, analyzing and displaying investment data comprising the steps of:
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(a) selecting a sample space, wherein the sample space includes at least one investment data sample;
(b) generating a distribution function using a re-sampled statistical method and a bias parameter, wherein the bias parameter determines a degree of randomness in a resampling process; and
,(c) generating a plot of the distribution function. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
(a) generating at least one bootstrap sample from the sample space; and
,(b) for each bootstrap sample, generating a corresponding bootstrap replication.
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4. The method according to claim 3, wherein the step of generating at least one bootstrap sample, further includes the steps of randomly selecting a set of Q data points from the sample space, wherein Q is a number of periods.
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5. The method according to claim 4, wherein the step of generating a bootstrap replication, further includes the step of taking a predetermined function of a bootstrap sample.
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6. The method according to claim 3, further including the steps of:
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(a) before step (b), calculating at least one of an autocorrelation function and a partial autocorrelation function of the sample, space for each of at least one lag parameter (a); and
,(b) determining a minimum lag parameter, N, wherein the minimum lag parameter N minimizes an autocorrelation function of the sample space.
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7. The method according to claim 6, wherein the step of generating at least one bootstrap sample, further includes the steps of:
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(a) randomly selecting a starting point in the sample space;
(b) selecting a set of N consecutive data points from the sample space; and
,(c) repeating steps (a)-(b) until at least Q data points have been selected, wherein Q is a number of periods.
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8. The method according to claim 1, wherein the re-sampled statistical method is a jackknife method.
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9. The method according to claim 1, where in the re-sampled statistical method is a cross-validation method.
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10. The method according to claim 5, wherein the predetermined function is one of a gross rate of return function, a maximum drawdown function and a monitor function.
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11. A method for providing statistical analysis of investment data over an information network, comprising the steps of:
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(a) storing investment data pertaining to at least one investment;
(b) receiving a statistical analysis request corresponding to a selected investment;
(c) receiving a bias parameter, wherein the bias parameter determines a degree of randomness in a resampling process; and
,(d based upon investment data pertaining to the selected investment, performing a resampled statistical analysis to generate a resampled distribution. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20, 21)
(a) selecting a sample space;
(b) generating at least one bootstrap sample from the sample space; and
,(c) for each bootstrap sample, generating a corresponding bootstrap replication.
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15. The method according to claim 14, wherein the step of generating at least one bootstrap sample, further includes the steps of randomly selecting a set of Q data points from the sample space, wherein Q is a number of periods.
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16. The method according to claim 14, wherein the step of generating a bootstrap replication, further includes the step of taking a predetermined function of the bootstrap sample.
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17. The method according to claim 14, further including the steps of:
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(a) before step (b), calculating at least one of an autocorrelation function and a partial autocorrelation function of the sample space for each of at least one lag parameter (a); and
(b) determining a minimum lag parameter, N, wherein the minimum lag parameter N minimizes an autocorrelation function of the sample space.
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18. The method according to claim 17, wherein the step of generating at least one bootstrap sample, further includes the steps of:
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(a) randomly selecting a starting point in the sample space;
(b) selecting a set of N consecutive data points from the sample space; and
,(c) repeating steps (a)-(b) until at least Q data points have been selected, wherein Q is a number of periods.
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19. The method according to claim 15, wherein the bias parameter is used to control a degree of randomness in selecting the set of Q data points.
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20. The method according to claim 11, wherein the information network is the Internet.
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21. The method according to claim 16, wherein the predetermined function is one of a gross rate of returns function, a maximum drawdown function and a monitor function.
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22. A system for providing statistical analysis of investment information over an information network comprising:
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a financial data database for storing investment data;
a client database;
a plurality of processors collectively arranged to perform a parallel processing computation, wherein the plurality of processors is adapted to;
receive a statistical analysis request corresponding to a selected investment;
based upon investment data pertaining to the selected investment, perform a resampled statistical analysis to generate a resampled distribution; and
,provide a report of the resampled distribution. - View Dependent Claims (23, 24, 25, 26, 27, 28)
(a) selects a sample space;
(b) generates at least one bootstrap sample from the sample space; and
,(c) for each bootstrap sample, generates a corresponding bootstrap replication.
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26. The system according to claim 22, further including an alert rules database, wherein the alert rules database stores at one alert rule record pertaining to a condition upon which a client desires to be notified.
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27. The system according to claim 26, wherein the processor, upon the violation of an alert rule based upon a resampled statistical analysis, notifies a client.
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28. The system according to claim 27, wherein the client is notified by electronic mail (“
- e-mail”
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- e-mail”
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29. A system for providing statistical analysis of investment information over an information network comprising:
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a financial data database for storing investment data;
a front end subsystem for receiving a statistical analysis request;
a parallel processor, wherein the parallel processor includes;
at least one processor for performing resampled statistical analysis. - View Dependent Claims (30, 31)
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Specification