Methods and systems for analytical-based multifactor multiobjective portfolio risk optimization

• US 20040199448A1
• Filed: 03/19/2003
• Published: 10/07/2004
• Est. Priority Date: 03/19/2003
• Status: Active Grant

• Current Assignee: GE Financial Assurance Holdings Incorporated (General Electric Company), General Electric Company
• Original Assignee: General Electric Company
• Inventors: Messmer, Richard Paul, Clark, Michael Craig, Chalermkraivuth, Kete Charles, Chakraborty, Anindya
• Granted Patent: US 7,640,201 B2
• Time in Patent Office: Days
• US Class Current: 705/36.00R
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

The invention provides systems and methods for determining the allocation of securities in a portfolio. The method includes providing a collection of securities in a portfolio, each security being associated with associated attributes; providing risk factor data related to the portfolio; pooling the securities into a plurality of security clusters based on the attributes associated with each security and the risk factor data, each security being assigned to an security cluster, the pooling being performed using multivariate decision tree processing; processing the security clusters using a nonlinear programming optimizer to generate optimization results; and presenting the optimization results in a risk-return space for determination of a security allocation.

Claims

1. A method for determining the allocation of securities in a portfolio, the method comprising:

providing a collection of securities in a portfolio, each security being associated with associated attributes;

providing risk factor data related to the portfolio;

pooling the securities into a plurality of security clusters based on the attributes associated with each security and the risk factor data, each security being assigned to an security cluster, the pooling being performed using multivariate decision tree processing;

processing the security clusters using a nonlinear programming optimizer to generate optimization results; and

presenting the optimization results in a risk-return space for determination of a security allocation.

2. The method of claim 1, wherein security attributes and the risk factor data provide a risk measure and a return measure for each security, the risk measure of each security and the return measure of each security being used as target variables in the multivariate decision tree processing.

3. The method of claim 2, wherein a first target is volatility and a second target is security return.

4. The method of claim 2, wherein the method further includes determining pooled measures for each cluster.

5. The method of claim 4, wherein the pooled measures serve as proxies to each security in the security portfolio, the pooled measures serving as inputs to the multivariate decision tree processing.

6. The method of claim 2, wherein the processing the security clusters using a nonlinear programming optimizer to generate optimization results includes:

inputting inputs to the nonlinear programming optimizer, the inputs based on variables of the security attributes.

7. The method of claim 6, further including determining pooled measures for each cluster on each variable that serves as an input to the nonlinear programming optimizer.

8. The method of claim 7, wherein the pooled measures serve as constraints in the nonlinear programming optimizer.

9. The method of claim 2, further including decomposing the securities, before pooling the securities into a plurality of security clusters, into respective subsets of securities, each subset then being subject to multivariate decision tree processing.

10. The method of claim 1, wherein the risk factor data is associated with risk measures, the method further including performing a risk measure simplification process through matrix manipulation comprising:

defining the change in risk factors;

defining Delta and Gamma;

restating the change in risk factors in Delta-Gamma formulation, the Delta-Gamma formulation having the factors Δ

F'"'"'s;

defining the covariance matrix of Δ

F;

taking the Cholesky decomposition of the covariance matrix to generate a P transformation matrix;

applying the P transformation matrix to Gamma to define a matrix Q_k;

determining the Eigenvalue decomposition of Q_k to obtain a matrix of Eigenvectors N; and

applying the matrix of Eigenvectors N and the P transformation matrix to evaluate the risk measures.

11. The method of claim 1, the portfolio is a collection of securities, which includes at least one of assets and liabilities.

12. A system for determining the allocation of securities in a portfolio, the system comprising:

a security attribute portion, being at least one of an asset data portion and a liability data portion, containing security attributes that provides a collection of securities in a portfolio, each security being associated with associated attributes;

a risk factor data portion that provides risk factor data related to the portfolio;

a pooling portion that pools the securities into a plurality of security clusters based on the attributes associated with each security and the risk factor data, each security being assigned to an security cluster, the pooling being performed using multivariate decision tree processing;

an optimization portion that processes the security clusters using a nonlinear programming optimizer to generate optimization results; and

a presentation portion that presents the optimization results in a risk-return space for determination of a security allocation.

13. The system of claim 12, wherein security attributes and the risk factor data provide a risk measure and a return measure for each security, the risk measure of each security and the return measure of each security being used as target variables in the multivariate decision tree processing.

14. The system of claim 13, wherein a first target is volatility and a second target is security return.

15. The system of claim 13, wherein the pooling portion determines pooled measures for each cluster.

16. The system of claim 15, wherein the pooled measures serve as proxies to each security in the security portfolio, the pooled measures serving as inputs to the multivariate decision tree processing.

17. The system of claim 13, wherein the pooling portion decomposes the securities, before pooling the securities into a plurality of security clusters, into respective subsets of securities, each subset then being subject to multivariate decision tree processing.

18. A computer readable medium for determining the allocation of securities in a portfolio, the computer readable medium comprising:

a first portion that provides a collection of securities in a portfolio, each security being associated with associated attributes;

a second portion that provides risk factor data related to the portfolio;

a third portion that pools the securities into a plurality of security clusters based on the attributes associated with each security and the risk factor data, each security being assigned to a security cluster, the pooling being performed using multivariate decision tree processing;

a fifth portion that processes the security clusters using a nonlinear programming optimizer to generate optimization results; and

a sixth portion that presents the optimization results in a risk-return space for determination of a security allocation.

19. A method for determining the allocation of securities in a portfolio, the method comprising:

providing a collection of securities in a portfolio, each security being associated with associated attributes;

providing risk factor data related to the portfolio;

pooling the securities into a plurality of security clusters based on the attributes associated with each security and the risk factor data, each security being assigned to an security cluster, the pooling being performed using multivariate decision tree processing;

processing the security clusters using a nonlinear programming optimizer to generate optimization results;

presenting the optimization results in a risk-return space for determination of a security allocation; and

wherein security attributes and the risk factor data provide a risk measure and a return measure for each security, the risk measure of each security and the return measure of each security being used as target variables in the multivariate decision tree processing;

wherein the method further includes determining pooled measures for each cluster; and

wherein the pooled measures serve as proxies to each security in the security portfolio, the pooled measures serving as inputs to the multivariate decision tree processing.