Cash generation from portfolio disposition using multi objective genetic algorithms

• US 7,680,747 B2
• Filed: 03/14/2002
• Issued: 03/16/2010
• Est. Priority Date: 03/14/2002
• Status: Active Grant

• Current Assignee: Intuit, Inc.
• Original Assignee: Intuit, Inc.
• Inventors: Hilton, Kenneth W.
• Primary Examiner(s): Vincent; David R
• Assistant Examiner(s): Wong; Lut
• Application Number:
  US10/099,043
• Publication Number:
  US 20040128219A1
• Time in Patent Office: 2,924 Days
• Field of Search: 706/13, 706/45
• US Class Current: 706/13
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

A plan for disposing of assets in a given asset portfolio is determined using a genetic algorithm, which operates to satisfy certain objectives, including the generation of a cash amount. A genome population including a number of vectors is generated. The genome population is modified using a genetic algorithm, until at least one vector represents a change in the percentage of each asset such that the disposition of each asset in accordance with the vector most nearly satisfies one or more objectives.

Claims

1. A computer-implemented method of determining an asset disposition plan for an asset portfolio comprising:

obtaining a genome population having a first set of vectors, each vector in the first set of vectors representing a change in a percentage of each asset in the asset portfolio containing a number of assets;

obtaining a plurality of objectives with respect to the asset disposition plan, the plurality of objectives including generation of a cash amount and each objective of the plurality of objectives associated with a weight indicating the importance of the objective;

computing a fitness score by applying a fitness function to each vector of a plurality of vectors in the genome population using an objective value, the objective value determined by applying an objective function to the vector, the objective value weighted by the corresponding weight,

wherein the fitness function comprises a function F=O_comp−

(O_average−

2·

O_σ

),

wherein F is the fitness score corresponding to each vector,

wherein O_comp is a composite objective value corresponding to each vector,

wherein O_average is an average of composite objective values corresponding to the first set of vectors in the genome population, and

wherein O_σ

is a standard deviation of the composite objective values corresponding to the first set of vectors in the genome population; and

introducing a new set of vectors in the genome population derived from the plurality of vectors including the first set of vectors, until a termination criteria is satisfied and at least one of the vectors of the plurality of vectors in the genome population represents the asset disposition plan, such that the disposition of each asset is in accordance with the asset disposition plan where the objectives are optimized, by;

selecting mating vectors from the first set of vectors based on the fitness score of each of the first set of vectors;

mating the selected mating vectors by uniform crossover to obtain a second set of vectors;

mutating the second set of vectors, by selecting a second plurality of vectors from the second set of vectors in a uniform distribution, and modifying the second plurality of vectors to obtain a third set of vectors;

replacing selected ones of vectors in the first set of vectors in the genome population with the third set of vectors, by;

adding the third set of vectors to the first set of vectors to obtain a modified first set of vectors;

determining the fitness score of each vector in the modified first set of vectors to obtain determined fitness scores;

removing a first predetermined number of the worst vectors from the modified first set of vectors based upon the determined fitness scores of the modified first set of vectors; and

adding a second predetermined number of best vectors in the first set of vectors to the modified first set of vectors based upon fitness scores of the first set of vectors to generate a result comprising a plurality of result vectors;

determining that the termination criteria is satisfied, wherein the termination criteria comprises a threshold amount for the cash amount generated and a threshold fitness score, wherein the termination criteria is satisfied when the cash amount generated exceeds the threshold amount, and wherein the termination criteria is satisfied when the fitness score of at least one of the plurality of result vectors is lower than the threshold fitness score.

2. The computer-implemented method of claim 1, wherein obtaining the genome population having the first set of vectors comprises generating the first set of vectors by random selection of numerical values representing the disposition of each asset in the asset portfolio.

3. The computer-implemented method of claim 1, wherein each vector in the first set of vectors comprises a plurality of alleles each including a plurality of bits, and obtaining the genome population having the first set of vectors comprises:

randomly choosing a bit density d from a uniform distribution, wherein 0<

=d<

=1; and

in each of the first set of vectors, randomly setting each bit of each allele to one with a probability of d.

4. The computer-implemented method of claim 1, wherein each vector of the plurality of vectors in the genome population includes a plurality of alleles, each allele corresponding to an asset in the asset portfolio and indicating a percentage of the asset that should be sold as part of the asset disposition plan.

5. The computer-implemented method of claim 1, wherein each vector of the plurality of vectors in the genome population includes a plurality of alleles, each allele corresponding to an asset in the asset portfolio and indicating a percentage of the asset that should be kept as part of the asset disposition plan.

6. The computer-implemented method of claim 1, wherein the mating vectors are selected by roulette wheel selection.

7. The computer-implemented method of claim 1, wherein the mating vectors are selected by tournament selection.

8. The computer-implemented method of claim 1, wherein computing the fitness score for each vector of the plurality of vectors comprises:

obtaining objective values O_G corresponding to associated objectives, each objective value representing a degree to which an associated objective is optimized when each asset is disposed in accordance with the asset disposition plan represented by each vector;

normalizing and standardizing each of the objective values O_G to obtain normalized, standardized objective values O_i corresponding to each of the objectives;

obtaining the composite objective value O_comp corresponding to the vector by the following equation;

$O_{\mathrm{comp}}=\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br><br><br>w_i<br><br>O_i$

wherein O_comp is the composite objective value corresponding to each vector,

wherein O_i is the normalized, standardized objective value corresponding to each of the objectives,

wherein w_i is the weight corresponding to each objective, and

wherein n is the number of objectives; and

applying the fitness function to the composite objective value O_comp to obtain the fitness score corresponding to the vector.

9. The computer-implemented method of claim 8, wherein the weight is a user-specified value.

10. The computer-implemented method of claim 8, wherein the weights add up to one.

11. The computer-implemented method of claim 8, wherein the normalized, standardized objective value O_i corresponding to each objective is obtained by dividing a difference between the objective value O_G and an optimum objective value with a normalizing factor.

12. The computer-implemented method of claim 11, wherein the normalizing factor is a difference between the optimum objective value and a worst objective value.

13. The computer-implemented method of claim 8, wherein the associated objectives comprise optimizing the amount of cash generated, and the normalized, standardized objective value O_i for the objective of optimizing the amount of cash generated is obtained by:

O_i=|O_A−

O_G|/|O_A|, when O_A>

=O_Z/2; and

O_i=|O_A−

O_G|/|O_A−

O_Z|, when O_A<

O_Z/2,

where O_A is a desired amount of cash to be generated from the asset portfolio;

O_Z is a maximum amount of cash that can be generated from the asset portfolio; and

O_G is total cash generated when each asset in the asset portfolio is disposed of in accordance with the asset disposition plan represented by the vector.

14. The computer-implemented method of claim 8, wherein the associated objectives comprise maximizing capital gain, and the normalized, standardized objective value O_i for the objective of maximizing capital gain is obtained by:

O_i=|O_A−

O_G|/|O_A−

O_Z|,

where O_A is a maximum capital gain that can be generated from the asset portfolio;

O_Z is a maximum capital loss that can be generated from the asset portfolio; and

O_G is total capital gain generated when each asset in the asset portfolio is disposed of in accordance with the asset disposition plan represented by the vector.

15. The computer-implemented method of claim 8, wherein the associated objectives comprise minimizing tax, and

the normalized, standardized objective value O_i for the objective of minimizing tax is obtained by:

O_i=|O_A−

O_G|/|O_A−

O_Z|,

where O_A is a loss limit permitted by tax law;

O_Z is a maximum tax incurred when all assets showing a capital gain are sold from the asset portfolio; and

O_G is total tax incurred when each asset in the asset portfolio is disposed of in accordance with the asset disposition plan represented by the vector.

16. The computer-implemented method of claim 8, wherein the associated objectives comprise minimizing costs for transactions, and the normalized, standardized objective value O_i for the objective of minimizing costs for transactions is obtained by:

O_i=|O_A−

O_G|/|O_A−

O_Z|,

where O_A is 0;

O_Z is total costs incurred by disposing of all assets in the asset portfolio; and

O_G is costs incurred when each asset in the asset portfolio is disposed of in accordance with the asset disposition plan represented by the vector.

17. The computer-implemented method of claim 8, wherein the associated objectives comprise minimizing the total number of transactions, and the normalized, standardized objective value O_i for the objective of minimizing the total number of transactions is obtained by:

O_i=|O_A−

O_G|/|O_A−

O_Z|,

where O_A is 0;

O_Z is the total number of assets in the asset portfolio; and

O_G is the number of transactions required by the asset disposition plan represented by the vector.

18. The computer-implemented method of claim 8, wherein the associated objectives comprise favoring sale by whole asset lots, and the normalized, standardized objective value O_i for the objective of favoring sale by whole asset lots is obtained by:

O_i=|O_A−

O_G|/|O_A−

O_Z|,

where O_A is 1;

O_Z is 0; and

O_G is the average percentage of the asset lots being disposed of in accordance with the asset disposition plan represented by the vector.

19. The computer-implemented method of claim 8, wherein the associated objectives comprise minimizing regret value, and the normalized, standardized objective value O_i for the objective of minimizing regret value is obtained by:

O_i=|O_A−

O_G|/|O_A−

O_Z|,

where O_A is 0;

O_Z is 1; and

O_G is the average regret value when each asset in the asset portfolio is disposed of in accordance with the asset disposition plan represented by the vector.

20. The computer-implemented method of claim 8, wherein the associated objectives comprise obtaining a target capital gain, and the normalized, standardized objective value O_i for the objective of obtaining the target capital gain is obtained by:

O_i=|O_A−

O_G|/|O_MIN−

O_MAX|,

where O_MIN is the sum of capital loss from all assets in the asset portfolio;

O_MAX is the sum of capital gain from all assets in the asset portfolio;

O_A is the target capital gain; and

O_G is total capital gain generated when each asset in the asset portfolio is disposed of in accordance with the asset disposition plan represented by the vector.

21. The computer-implemented method of claim 8, wherein the associated objectives comprise maximizing return on investment, and the normalized, standardized objective value O_i for the objective of maximizing return on investment is obtained by:

O_i=|O_A−

O_G|/|O_A−

O_Z|,

where O_A is the sum of the return on investment of all assets of which the return on investment is greater than or equal to 0;

O_Z is the sum of the return on investment of all assets of which the return on investment is less than 0; and

O_G is the return on investment when each asset in the asset portfolio is disposed of in accordance with the asset disposition plan represented by the vector.

22. The computer-implemented method of claim 1, wherein the weight corresponding to an objective of generation of the cash amount is 0, wherein the objective is one of the plurality of objectives.

23. A computer readable storage medium storing instructions to determine an asset disposition plan for an asset portfolio, the instructions executable on a processor and comprising functionality to:

obtain a genome population having a first set of vectors, each vector in the first set of vectors representing a change in a percentage of each asset in the asset portfolio containing a number of assets;

obtain a plurality of objectives with respect to the asset disposition plan, the plurality of objectives including generation of a cash amount and each objective of the plurality of objectives associated with a weight indicating the importance of the objective;

compute a fitness score by applying a fitness function to each vector of a plurality of vectors in the genome population using an objective value, the objective value determined by applying an objective function to the vector and weighted by the corresponding weights,

wherein the fitness function comprises a function F=O_comp−

(O_average−

2·

O₉₄ ),

wherein F is the fitness score corresponding to each vector,

wherein O_comp is a composite objective value corresponding to each vector,

wherein O_average is an average of composite objective values corresponding to the first set of vectors in the genome population, and

wherein O_σ

is a standard deviation of the composite objective values corresponding to the first set of vectors in the genome population;

introduce a new set of vectors in the genome population derived from the plurality of vectors including the first set of vectors, until a termination criteria is satisfied and at least one of the vectors of the plurality of vectors in the genome population represents the asset disposition plan, such that the disposition of each asset is in accordance with the asset disposition plan where the objectives are optimized, by;

selecting mating vectors from the first set of vectors based on the fitness score of each of the first set of vectors;

mating the selected mating vectors by uniform crossover to obtain a second set of vectors;

mutating the second set of vectors, by selecting a second plurality of vectors from the second set of vectors in a uniform distribution and modifying the second plurality of vectors to obtain a third set of vectors; and

replacing selected ones of vectors in the first set of vectors in the genome population with the third set of vectors, by;

adding the third set of vectors to the first set of vectors to obtain a modified first set of vectors;

determining the fitness score of each vector in the modified first set of vectors to obtain determined fitness scores;

removing a first predetermined number of the worst vectors from the modified first set of vectors based upon the determined fitness scores of the modified first set of vectors; and

adding a second predetermined number of best vectors in the first set of vectors to the modified first set of vectors based upon fitness scores of the first set of vectors to generate a result comprising a plurality of result vectors;

determine that the termination criteria is satisfied, wherein the termination criteria comprises a threshold amount for the cash amount generated and a threshold fitness score, wherein the termination criteria is satisfied when the cash amount generated exceeds the threshold amount, and wherein the termination criteria is satisfied when the fitness score of at least one of the plurality of result vectors is lower than the threshold fitness score.

24. The computer readable storage medium of claim 23, wherein computing the fitness score for each vector comprises:

obtaining objective values O_G corresponding to associated objectives, each objective value representing a degree to which the associated objective is optimized when each asset is disposed in accordance with an asset disposition plan represented by the vector;

normalizing and standardizing each of the objective values O_G to obtain normalized, standardized objective values O_i corresponding to each of the objectives;

obtaining the composite objective value O_comp corresponding to the vector by the following equation;

$O_{\mathrm{comp}}=\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br><br><br>w_i<br><br>O_i$

wherein O_comp is the composite objective value corresponding to the vector,

wherein O_i is the normalized, standardized objective value corresponding to each of the objectives,

wherein w_i is the weight corresponding to each objective, and

wherein n is the number of objectives; and

applying the fitness function to the composite objective value O_comp to obtain the fitness score corresponding to the vector.

25. A computer system for determining an asset disposition plan for an asset portfolio comprising:

an initialization module configured to;

obtain a genome population having a first set of vectors, each vector in the first set of vectors representing a change in a percentage of each asset in the asset portfolio containing a number of assets;

a scoring module configured to;

compute a fitness score by applying a fitness function to each vector of a plurality of vectors in the genome population using an objective value, the objective value associated with a plurality of objectives and weighted by a corresponding weight indicating the importance of the objective,

wherein the fitness function comprises a function F=O_comp−

(O_average−

2·

O₉₄ ),

wherein F is the fitness score corresponding to each vector,

wherein O_comp is a composite objective value corresponding to each vector,

wherein O_average is an average of composite objective values corresponding to the first set of vectors in the genome population, and

wherein O_σ

is a standard deviation of the composite objective values corresponding to the first set of vectors in the genome population;

a selection module configured to;

select mating vectors from the first set of vectors based on the fitness score of each of the first set of vectors;

a mating module configured to;

mate the selected mating vectors by uniform crossover to obtain a second set of vectors;

a mutation module configured to;

mutate the second set of vectors, by selecting a plurality of vectors from the second set of vectors in a uniform distribution and modifying the plurality of vectors to obtain a third set of vectors;

a replacement module configured to;

replace selected ones of vectors in the first set of vectors in the genome population with the third set of vectors, by;

adding the third set of vectors to the first set of vectors to obtain a modified first set of vectors;

determining the fitness score of each vector in the modified first set of vectors to obtain determined fitness scores;

removing a first predetermined number of the worst vectors from the modified first set of vectors based upon the determined fitness scores of the modified first set of vectors; and

adding a second predetermined number of best vectors in the first set of vectors to the modified first set of vectors based upon fitness scores of the first set of vectors to generate a result comprising a plurality of result vectors; and

a genetic algorithm module configured to;

introduce a new set of vectors in the genome population derived from the plurality of vectors including the first set of vectors, until a termination criteria is satisfied and at least one of the vectors of the plurality of vectors in the genome population represents the asset disposition plan, such that the disposition of each asset is in accordance with the asset disposition plan where the objectives are optimized;

determine that the termination criteria is satisfied, wherein the termination criteria comprises a threshold amount for a cash amount generated and a threshold fitness score, wherein the termination criteria is satisfied when the cash amount generated exceeds the threshold amount, and wherein the termination criteria is satisfied when the fitness score of at least one of the plurality of result vectors is lower than the threshold fitness score.

26. The computer system of claim 25, wherein the weight corresponding to an objective of generation of the cash amount is 0, wherein the objective is one of the plurality of objectives.