Stochastic control system and method for multi-period consumption

• US 7,516,095 B1
• Filed: 02/27/2008
• Issued: 04/07/2009
• Est. Priority Date: 10/12/2007
• Status: Active Grant

• Current Assignee: Refinitiv US Organization, LLC (Refinitiv US Holdings, Inc.)
• Original Assignee: Advisor Software, Inc. (Refinitiv US Holdings, Inc.)
• Inventors: Torre, Nicolo G., Rudd, Andrew T.
• Primary Examiner(s): Weisberger; Richard C
• Application Number:
  US12/038,712
• Publication Number:
  US 20090099975A1
• Time in Patent Office: 405 Days
• Field of Search: 705 35- 45
• US Class Current: 705/35
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

The present invention relates to dynamic optimization of system control over time. The need for dynamic optimization arises in many settings, as diverse as solar car power consumption during a multi-day race and retirement portfolio management. We disclose a reformulation of the control problem that overcomes the so-called “curse of dimensionality” and allows formulation of optimal control policies multiple period planning horizons. One optimal control policy is for power consumption by a solar car during a race, which involves many course segments, as course conditions vary through a day. Another is for risk in and consumption from a portfolio intended to support retirement. Both multi-period control policies take into account future uncertainty. Particular aspects of the present invention are described in the claims, specification and drawings.

Claims

1. A computer-implemented method for generating a recommended a control action policy in a multi-year planning setting, the method including:

retrieving mean return, variance and covariance data for multiple of classes of prospective investments;

retrieving pay and likelihood of unemployment estimates for human capital and covariance estimates between the human capital and the prospective investments;

retrieving house price return and variance data for housing resources and covariance estimates between the housing resources and the prospective investments;

electronically calculating, from the retrieved data and estimates, returns on alternative diversified portfolios that include the prospective investments, the human capital and the housing resources;

filtering among the alternative diversified portfolios so that the human capital and the housing resource components of the diversified portfolios conform to resources of an investor and goals set by the investor for retirement age and for holding or selling the housing resources;

receiving data into computer memory and processing the data to generate a search grid spanning and subdividing at least

a time horizon,

potential resource values for the investor'"'"'s portfolio,

a range of spending levels that satisfy essential goals of the investor and are then applied to satisfying the investor'"'"'s non-essential goals, and

the range of risk taking levels; and

working backwards through the time horizon,

using a computer to evaluate potential control actions for particular times and resource values within the search grid,

selecting recommended control actions for the particular times and resource values, the control actions setting spending and risk taking levels for the particular times, and

combining the control actions into a control action policy that includes the recommended spending and risk taking levels for combinations of the particular times and resource values;

wherein using the computer to evaluate the potential control actions for the particular times and resource values includes calculating a utility of potential spending and risk taking levels,

such that the utility at a specific time and resource value for the potential spending and risk taking levels combines at least

a utility in the specific time of the spending and

a future expected utility through the end of the time horizon of future spending and risk taking levels; and

such that the utility calculation recognizes a greater utility of satisfying the essential goals than of satisfying the non-essential goals; and

outputting at least part of the control action policy for the time horizon and the potential resource values, including recommendations for the spending and the risk taking levels.

2. The method of claim 1, further including:

choosing for the goals whether to express an amount that satisfies a particular goal in constant dollars or current purchasing dollars; and

including in the search grid a range of inflation factors.

3. The method of claim 1, further including:

summarizing the alternative diversified portfolios with efficient frontiers; and

expressing the risk levels in terms of value-at-risk as a percentage of resources, given a selected probability or confidence level and a holding period.

4. The method of claim 1, wherein the alternative diversified portfolios account for include at least a home, an employment income and a social security entitlement.

5. The method of claim 1, further including retrieving data regarding embedded gain for tax purposes of at least one investor resources, wherein:

the search grid further includes a range of embedded gain; and

the evaluating includes realizing tax consequences of selling the resource with the embedded gain to satisfy the goals.

6. The method of claim 5, wherein the investor'"'"'s resource with the embedded gain is a home and sale of the home is paired with purchase of a replacement home.

7. The method of claim 1, further including outputting a control policy datacube, the control policy datacube spanning the time horizon and potential resource values of the search grid.

8. The method of claim 7, further including:

beginning with the current resources and following the control policy included in the control policy datacube, conducting at least 50 probabilistic simulation runs over the time horizon to generate a distribution of outcomes; and

reporting the distribution of outcomes.

9. The method of claim 1, further including:

prior to the evaluating the control actions, constructing a resources and claims analysis and qualifying the goals as likely to be satisfied by the resources available.

10. The method of claim 9, further including:

constructing a cash flow analysis and qualifying timing of the goals as feasible, given timing of the resources available.

11. A planning server for selecting at least immediate control actions in a multi-year planning setting, the planning server including:

a first processor programmed to

access mean return, variance and covariance data for multiple of classes of prospective investments, pay and likelihood of unemployment estimates for human capital and covariance estimates between the human capital and the prospective investments, and house price return and variance data for housing resources and covariance estimates between the housing resources and the prospective investments, and

determine returns on alternative diversified portfolios that include the prospective investments, the human capital and the housing resources, and

filters among the alternative diversified portfolios so that the human capital and the housing resource components of the diversified portfolios conform to resources of an investor and goals set by the investor for retirement age and for holding or selling the housing resources,

store the returns on the alternative diversified portfolios

a second processor programmed to generate a search grid spanning and subdividing at least

a time horizon,

potential resource values for the investor'"'"'s portfolio,

a range of spending levels that satisfy essential goals of the investor and are then applied to satisfying the investor'"'"'s non-essential goals, and

the range of risk taking levels; and

a third processor programmed to access the search grid and the stored returns on the alternative diversified portfolios and works backwards through the time horizon,

to evaluate potential control actions for particular times and resource values within the search grid,

to select recommended control actions for the particular times and resource values, the control actions setting spending and risk taking levels for the particular times,

to combine the control actions into a control action policy that includes the recommended spending and risk taking levels for combinations of the particular times and resource values, and

to output the control action policy that includes the consumption and the risk levels;

wherein the third processor calculates a utility of potential spending and risk taking levels, such that the utility at a specific time and resource value for the potential spending and risk taking levels combines at least

a utility in the specific time of the spending that recognizes a greater utility of satisfying the essential goals than of satisfying the non-essential goals, and

a future expected utility through the end of the time horizon of future spending and risk taking levels.

12. A control strategy evaluation system including the planning server of claim 11, further including:

a lifetime control policy simulation server that

accesses the control action policy output by the control action recommendation module and conducts at least 50 probabilistic simulation runs over the time horizon, which simulation runs apply the control action policy to generate a distribution of outcomes, and

reports at least the distribution of the outcomes.

13. The planning server of claim 11, wherein the first processor, the second processor and the third processor share processing resources.