Source: https://patents.google.com/patent/US7096197B2/en
Timestamp: 2020-08-04 07:04:30
Document Index: 306838664

Matched Legal Cases: ['Application No. 60', 'ART) 252', 'ART) 6', 'ART) 729', 'ART) 329', 'ART) 28', 'ART) 71']

US7096197B2 - Methods and apparatus for simulating competitive bidding yield - Google Patents
US7096197B2
US7096197B2 US09/737,038 US73703800A US7096197B2 US 7096197 B2 US7096197 B2 US 7096197B2 US 73703800 A US73703800 A US 73703800A US 7096197 B2 US7096197 B2 US 7096197B2
US09/737,038
US20010037278A1 (en
1999-12-30 Priority to US17394799P priority Critical
2000-12-14 Application filed by GE Corporate Financial Services Inc filed Critical GE Corporate Financial Services Inc
2000-12-14 Priority to US09/737,038 priority patent/US7096197B2/en
2001-05-07 Assigned to GENERAL ELECTRIC COMPANY reassignment GENERAL ELECTRIC COMPANY ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: MESSMER, RICHARD PAUL, EDGAR, MARC THOMAS, AKBAY, KUNTER SEREF, CIFARELLI, JAMES LOUIS, JOHNSON, CHRISTOPHER DONALD
2001-08-14 Priority claimed from TW90119909A external-priority patent/TW554276B/en
2001-11-01 Publication of US20010037278A1 publication Critical patent/US20010037278A1/en
2006-08-22 Publication of US7096197B2 publication Critical patent/US7096197B2/en
230000002860 competitive Effects 0.000 title description 7
This application claims the benefit of U.S. Provisional Application No. 60/173,947, filed Dec. 30, 1999, which is hereby incorporated by reference in its entirety.
Traditionally, bids and offers are established based upon an asset's inherent worth and a knowledge of the competitive context. Investment and/or divestiture committees of entities making bids come to consensus on minimum and maximum prices they will unilaterally make. As a result of this process, a bidder may significantly undervalue a tranche and submit a bid that is not competitive or bid higher than the underwritten value and assume unquantified risk. Of course, since the objective is to win each tranche at a price that enables a bidder to earn a return, losing a tranche due to significant undervaluation of the tranche represents a lost opportunity. It would be desirable to provide a system that facilitates accurate valuation of a large number of financial instruments in a short period of time and understand the associated probabilities of return for a given bid. It would be further desirable to determine a bid price for each tranche of a portfolio of financial instruments in a sealed bid auction.
As the process proceeds and more assets are underwritten, the number of assets in the first and second portions increase and the number of assets in the third portion decreases and the variance of the valuation of the assets in the third portion becomes more and more defined. More specifically, the assets in the third portion are evaluated by grouping the assets into clusters based on similarity to valuations of assets in the first and second portions. Hypothetical bids are generated using the valuations to determine an optimum bid within parameters determined by the bidder. The optimum bid is identified through an iterative bid generation process. A bid price for each tranch of a portfolio of financial instruments in a sealed bid auction is determined by simulating a bid opening process and determining an optimal bid that returns the highest probability of a successful bid.
Cluster Cluster Valuative Number Name High Exp Low Time Confidence High Exp Low Time 1 Lien .85 .62 .15 3 .3 (.3/1.65)(.85) (.3/1.65)(.62) (.3/1.65)(.15) (.3/1.65)(3) positions- recourse 2 Asset .45 .4 .31 3 .7 (.7/1.65)(.45) (.7/1.65)(.4) (.7/1.65)(.31) (.7/1.65)(3) classification- industry- age 3 Coordinates- .9 .5 .2 2 .65 (.65/1.65)(.9) (.65/1.65)(.5) (.65/1.54)(.2) (.65/1.65)(2) use- borrower n x 1.65 .6999 .4792 .2374 2.6059
TABLE A Portfolio attributes Borrower Size (by Borrower Group UPB) Secured Syndicated (yes/no) Guaranteed Loan Type (Term, Revolving, etc.) % UPB from Liens in First Position Collection Score (0 = Bad, 1 = Good) 12-month collections % of UPB % of Last Payment for Principal # Borrower Loans Loan's portion of borrower UPB Single Family Residence Residential Retail Industrial Hospital Hospitality Multifamily Land Developed/Undeveloped/Other Office Stock/Margin Loans
Var ⁢ ( Y ^ R ) = n ⁡ [ 1 - n N ] × [ ∑ 1 N ⁢ x i ] 2 [ ∑ n 1 ⁢ x i ] 2 × ∑ N 1 ⁢ ( y i - Rx i ) 2 N - 1
R = ∑ 1 N ⁢ y i ∑ n 1 ⁢ x i = cluster ⁢ ⁢ expected ⁢ ⁢ recovery ⁢ ⁢ %
h 2 = k 2 × n ⁡ [ 1 - n N ] × [ ∑ 1 N ⁢ x i ] 2 [ ∑ n 1 ⁢ x i ] 2 × ∑ N 1 ⁢ ( y i - Rx i ) 2 N - 1 (Equation C)
Y = ∑ l N ⁢ ⁢ y i ⁢ ⁢ ⁢ with ⁢ ⁢ Y ^ R
Y ^ R = R ^ × ∑ i = 1 N ⁢ x i = ∑ i = 1 N ⁢ y i ∑ n i = 1 ⁢ x i × ∑ i = 1 N ⁢ x i = ∑ i = 1 n ⁢ ρ i ⁢ x i ∑ i = 1 n ⁢ x i × ∑ i = 1 N ⁢ x i (Equation D)
=constant in Tchebyshev's Formula:
 Y ^ R - μ Y ^ R  ≤ k ⁢ Var ⁡ ( Y ^ R ) ⁢ ⁢ with ⁢ ⁢ probability ≥ 1 - 1 k 2
TABLE B Sample Size Spreadsheet Wizard Sample Cume Exp. Size Exp. Rec. Exp. Rec. Cume UPB Rec. % Residual 1 779,131 779,131 2,936,279 26.5% — 2 716,951 1,496,082 5,447,631 27.5% 27,259 3 359,327 1,855,409 5,702,090 27.7% 12,042 4 481,798 2,337,206 8,538,875 27.4% (20,956) 5 606,774 2,943,980 10,706,452 27.5% 10,750 6 418,899 3,362,880 12,207,495 27.5% 5,397 7 822,516 3,985,396 14,609,180 27.3% (32,665) 8 594,799 4,580,195 16,911,278 27.1% (28,694) 9 713,922 5,294,117 19,440,132 27.2% 25,241 10 494,230 5,788,346 21,153,615 27.4% 29,363 11 738,334 6,523,680 24,031,814 27.1% (45,983) 12 683,155 7,206,835 26,387,193 27.3% 39,857 13 746,413 7,955,248 29,256,251 27.2% (31,730) 14 418,885 8,375,133 30,726,773 27.3% 19,068 15 757,050 9,132,183 33,682,971 27.1% (44,439) 16 553,674 9,685,857 35,690,262 27.1% 8,922 17 761,579 10,447,435 36,234,459 27.3% 66,386 18 677,811 11,125,246 40,756,944 27.3% (10,741) 19 563,811 11,689,057 42,688,952 27.4% 34,790 20 434,763 12,123,821 44,160,329 27.5% 30,810) N (cluster size) n (sample size) Expected Recovery % 20 6 27.5% Face Range ER % Range Face Value 2,000,000 5.0% 44,160,329 Min Face Min ER % Expected Recovery 1,000,000 25.0% 12,123,821 Confidence k Precision Precision % 75.0% 2.00 1,212,382 10.0%
The appropriate variance adjusted forecast is made for each asset and the valuation tables are constructed to include every asset in the portfolio. The recovery is valued with continuous probabilities at the unit of sale, which in one embodiment is a tranche. In the use of system 28, internal rate of return (“IRR”) and variance would then be assessed. Preferred tranches have lower variances for a given IRR. The probability of each tranche's net present value (“NPV”) to be above 0 is assesed using the project's discount rate. A discount rate is determined from the opportunity cost of capital, plus FX swap cost, plus risks in general uncertainties inherent in the variances of forecasted cash flow recovery. If it appears that there is more than a five-percent certainty that the project will have a negative NPV, no bid is made. Deal evaluation is by tranche with decision criteria being IRR, risk variance of the IRR in a tranche, estimated willingness and ability of the tranche to pay, time to profit (“TPP”) and the risk variance in the payback by tranche, and NPV of the expected cash flow by tranche discounted to risk free rate.
FIG. 9 is a high level overview 290 of the automated portion of the process employed by system 28. Automated procedures are used by underwriters to assist in frill underwriting based on procedure 34 (see also FIG. 3). Knowledge captured in procedure 34 is applied in inferred valuation procedure 40 to reduce cost and uncertainty in due diligence valuations of financial instruments and to reduce cost and variability between due diligence valuations. The valuations are subjected to a cash flow model which includes asset level valuation 146, deterministic cash flow bridge 148, stochastic cash flow bridge 152 and cash flow table 150. The resultant bid valuation 154 is subjected to gaming strategies 160 and management adjustments 162 to produce the final bid 164.
TABLE C Rank Error Ratios and R2 value per asset Rank Error R-Squared pe Ratio for Loan for C Tranche CO Data B C Grand Total C loans loans CO 01 Sum of a Curr UPB THB 645,959,109 82,692,009 728,651,119 Count of Loan No 66 10 76 Sum of SST 599,969 990,091,044 72,331,126,127,460 672 301,116,218,504 Sum of SSE(CART) 252,088 256,587 362 26,877,527,094,865 278,965,783 682,227 Sum of SSE(Simple) 440,700,263,795,025 36,637,006,656,009 477,337,270,451,034 0.733617 0.18% CO 02 Sum of a Curr UPB THB 58 779,400 379 765,147 438,544,547 Count of Loan No 9 118 127 Sum of SST 32,332,549,696,133 1,039,401,135,208,180 1,071,733,685,904,320 Sum of SSE(CART) 6,139,933,273,655 83,849,226,818,428 89 989,160 092,084 Sum of SSE(Simple) 7 037,799,486,368 136,366,441,963,041 143,404,241,449,409 0.614882 0.06% CO 03 Sum of a Curr UPB THB 798,969,257 276,915,573 1,075,884,830 Count of Loan No 98 99 197 Sum of SST 2,869,807,879,172,670 1,017,087,163,438,760 3,886,895,042,611,430 Sum of SSE(CART) 729,304,505,050 836 65 902,258,632,574 795 206,763,683,411 Sum of SSE(Simple) 929,822,648,064,552 41,730,444,375,417 971,553,092 439,969 1.579237 0.46% CO 04 Sum of a Curr UPB THB 916,281,888 184,828,399 1,101,110,287 Count of Loan No 116 28 144 Sum of SST 927,232,177,539,735 223,991,862,418,471 1 151,224,039,958,210 Sum of SSE(CART) 329,869,566,636,764 92,347,778,018,417 422,217,344,655,182 Sum of SSE(Simple) 688,543,329,448,792 62,722,788,782,158 751,266,118,230,950 1.472316 0.11% CO 05 Sum of a Curr UPB THB 221,769,281 41,505,412 263 274,692 Count of Loan No 36 19 55 Sum of SST 270,033,444,922,605 164,601,058,694,453 434,634,503,617,058 Sum of SSE(CART) 28,547,982,198,098 10 191,006,095,769 38,738,988,293,867 Sum of SSE(Simple) 28,897 015,065,918 8,519,509,247,449 37 416,524 313,367 1.196196 0.14% Total Sum of a Curr 2,641,758,934 965,706,540 3,607,465,475 UPB THB Total Count of 325 274 599 Loan No Total Sum of SST 4,699,376,041,422,190 2,517,412,345,887,330 7,216,788,387,309,520 Total Sum of 1,345,950,243,746,720 279,167,796,660,054 1,625,118,040,406,770 SSE(CART) Total Sum of 2,095,001,055,860,660 285,976,191,024,073 2,380,977,246,884,730 0.976192 0.22% SSE(Simple) R-Squared (CART) 71 4% 88 9% 77 5% R-Squared (Simple) 55 4% 88 6% 67 0%
TABLE D Portfolio Segment Ranking Tranche CO C R-Squared Rank Error Ratio Rank R-squared CO 01 0.73 0.18% 2 2 CO 02 0.61 0.06% 1 5 CO 03 1.58 0.46% 5 1 CO 04 1.47 0.11% 4 4 CO 05 1.20 0.14% 3 3
y _ ^ l = ∑ i , j , k ⁢ I lk ⁢ f ijk ⁢ y ^ lk ∑ i , j , k ⁢ I lk ⁢ f ijk (Equation E]
J = ∑ k = 1 n ⁢ ∑ i = 1 c ⁢ μ ik m ⁢  X k - V i  2 (Equation F)
where n is the number of data points; c is the number of clusters, Xk is the kth data point; Vi is the ith cluster centroid; μik is the degree of membership of the kth data in the ith cluster; m is a constant greater than 1 (typically m=2). Note that μik is a real number and bounded in [0, 1]. μik=1 means that ith data is definitely in kth cluster, while μik=0 means that ith data is definitely not in kth cluster. If μik=0.5, then it means that ith data is partially in kth cluster to the degree 0.5. Intuitively, the cost function would be minimized if each data point belongs exactly to a specific cluster and there is no partial degree of membership to any other clusters. That is, there is no ambiguity in assigning each data point to the cluster to which it belongs.
μ ik = 1 ∑ j = 1 c ⁢ (  X k - V i  2  X k - V j  2 ) 1 m - 1 (Equation G)
V i = ∑ k = 1 n ⁢ ( μ ik ) m ⁢ X k ∑ k = 1 n ⁢ ( μ ik ) m (Equation H)
A E W = ∑ ( Act ) ∑ ( Wtd ⁢ ⁢ exp ) 0.72858 (Equation I)
NPV = ∑ P - ∑ E - ( ∑ C ) × A E W (Equation J)
1. A computer-implemented method for determining for a user a winning bid, at an optimal bid price, for a sealed bid auction, the method implemented using a computer, said method comprising the steps of:
using the computer to determine a statistical distribution of possible bid values from competing bidders in the sealed bid auction for at least one tranche included within a portfolio of assets;
selecting by the user a bid value for the at least one tranche for comparing against a random sample of competing bid values;
randomly sampling the statistical distribution of possible competing bid values to generate one possible auction scenario;
determining a probability that the user selected bid value is greater than the randomly sampled competing bid values included in the auction scenario; and
determining a winning bid value for the sealed bid auction based on an outcome of the auction scenario.
2. A method according to claim 1 wherein said step of randomly sampling the statistical distribution further comprises the step of using an iterative sampling technique to produce a distribution of auction outcomes.
3. A method according to claim 2 wherein said step of using an iterative sampling technique further comprises the step of using a Monte Carlo analysis to produce the distribution of auction outcomes.
selecting by the user a plurality of bid values for the at least one tranche included within the portfolio of assets;
randomly sampling the statistical distribution of possible competing bid values to generate possible auction scenarios;
applying each user selected bid value to the auction scenarios; and
determining for each user selected bid value a probability of winning the auction scenarios.
5. A method according to claim 4 wherein said step of randomly sampling the statistical distribution further comprises the step of using an iterative sampling technique to produce a distribution of auction outcomes.
6. A method according to claim 5 wherein said step of using an iterative sampling technique further comprises the step of using a Monte Carlo analysis to produce the distribution of auction outcomes.
7. A method according to claim 1 wherein said step of using the computer to determine a statistical distribution of possible bid values further comprises the step of determining financial capabilities for at least one of the possible competing bidders.
8. A method according to claim 1 wherein said step of using the computer to determine a statistical distribution of possible bid values further comprises the step of codifying market rules and contracts into computerized business rules suitable for a simulation.
9. A method according to claim 1 wherein said step of using the computer to determine a statistical distribution of possible bid values further comprises the step of codifying at least one of potential competition, market forces, forecasted budgets, priorities, risk and return tradeoffs into a preference matrix.
10. A method according to claim 1 wherein said step of using the computer to determine a statistical distribution of possible bid values further comprises the step of codifying past bidding history of competing bidders based upon knowledge of tranche types preferred by competing bidders.
at least one client system connected to said server through a network and configured to access said database via said server,
determine a statistical distribution of possible bid values from competing bidders in the sealed bid auction for at least one tranche included within a portfolio of assets,
select a bid value for the at least one tranche for comparing against a random sample of competing bid values,
randomly sample the statistical distribution of possible competing bid values to generate one possible auction scenario,
determine a probability that the selected bid value is greater than the randomly sampled competing bid values included in the auction scenario; and
determine a winning bid value for the sealed bid auction based on an outcome of the auction scenario.
12. A system according to claim 11 wherein said server is configured to use an iterative sampling technique to produce a distribution of auction outcomes.
13. A system according to claim 12 wherein said server is configured to use a Monte Carlo analysis as an iterative sampling technique.
14. A system according to claim 11 wherein said server is configured to:
select a plurality of bid values for the at least one tranche included within the portfolio of assets;
randomly sample the statistical distribution of possible competing bid values to generate possible auction scenarios; and
apply each selected bid value to the auction scenarios; and
determine for each selected bid value a probability of winning the auction scenarios.
15. A system according to claim 14 wherein said server is configured to use an iterative sampling technique to produce a distribution of auction outcomes.
16. A system according to claim 15 wherein said server is configured to use a Monte Carlo analysis as an iterative sampling technique.
17. A system according to claim 11 wherein said server is configured to determine financial capabilities for at least one of the possible competing bidders.
18. A system according to claim 11 wherein said server is configured to codify market rules and contracts into computerized business rules.
19. A system according to claim 11 wherein said server is configured to codify at least one of potential competition, market forces, forecasted budgets, priorities, risk and return tradeoffs into a preference matrix.
20. A system according to claim 11 wherein said server is configured to codify past bidding history of competing bidders based upon knowledge of tranche types preferred by competing bidders.
determine a statistical distribution of possible bid values from competing bidders in a sealed bid auction for at least one tranche included within a portfolio of assets;
select a bid value for the at least one tranche for comparing against a random sample of competing bid values;
randomly sample the statistical distribution of possible competing bid values to generate one possible auction scenario;
22. A computer according to claim 21 programmed to use an iterative sampling technique to produce a distribution of auction outcomes.
23. A computer according to claim 22 programmed to use a Monte Carlo analysis as an iterative sampling technique.
24. A computer according to claim 21 programmed to:
randomly sample the statistical distribution of possible competing bid values to generate possible auction scenarios;
25. A computer according to claim 24 programmed to use an iterative sampling technique to produce a distribution of auction outcomes.
26. A computer according to claim 25 programmed to use a Monte Carlo analysis as an iterative sampling technique.
27. A computer according to claim 21 programmed to determine financial capabilities for at least one of the possible competing bidders.
28. A computer according to claim 21 programmed to codify market rules and contracts into business rules.
29. A computer according to claim 21 programmed to codify at least one of potential competition, market forces, forecasted budgets, priorities, risk and return tradeoffs into a preference matrix.
30. A computer according to claim 21 programmed to codify past bidding history of competing bidders based upon knowledge of tranche types preferred by competing bidders.
31. A method in accordance with claim 1 wherein said selecting a bid value for the at least one tranche further comprises:
fully underwriting each asset included within a first portion of the asset portfolio including underwriting in a full cash manner to generate a full value table, and underwriting in a partial cash manner to generate a partial value table;
grouping and underwriting a sample of assets included within a second portion of the asset portfolio;
using the computer to statistically infer a value for each asset included within a third portion of the asset portfolio;
and using the full value table and the partial value table, the underwriting of the sample of assets within the second portion of the asset portfolio, and the statistically inferred values of assets included in the third portion of the asset portfolio to select a bid value for said at least one tranche.
US09/737,038 1999-12-30 2000-12-14 Methods and apparatus for simulating competitive bidding yield Active 2023-05-15 US7096197B2 (en)
US17394799P true 1999-12-30 1999-12-30
US09/737,038 US7096197B2 (en) 1999-12-30 2000-12-14 Methods and apparatus for simulating competitive bidding yield
KR1020027008587A KR20030004316A (en) 1999-12-30 2000-12-19 Methods and apparatus for simulating competitive bidding yield
JP2001550666A JP2004500646A (en) 1999-12-30 2000-12-19 Competitive bidding revenue simulation method and system
AU25846/01A AU2584601A (en) 1999-12-30 2000-12-19 Methods and apparatus for simulating competitive bidding yield
BR0017059A BR0017059A (en) 1999-12-30 2000-12-19 Methods and apparatus to simulate competitive bidding yield
PCT/US2000/034599 WO2001050379A2 (en) 1999-12-30 2000-12-19 Methods and apparatus for simulating competitive bidding yield
EP00989333A EP1259917A1 (en) 1999-12-30 2000-12-19 Methods and apparatus for simulating competitive bidding yield
PL00356538A PL356538A1 (en) 1999-12-30 2000-12-19 Methods and apparatus for simulating competitive bidding yield
US20010037278A1 US20010037278A1 (en) 2001-11-01
US7096197B2 true US7096197B2 (en) 2006-08-22
US09/737,038 Active 2023-05-15 US7096197B2 (en) 1999-12-30 2000-12-14 Methods and apparatus for simulating competitive bidding yield
AU (1) AU2584601A (en)
BR (1) BR0017059A (en)
MX (1) MXPA02006545A (en)
PL (1) PL356538A1 (en)
WO (1) WO2001050379A2 (en)
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2000-12-14 US US09/737,038 patent/US7096197B2/en active Active
2000-12-19 AU AU25846/01A patent/AU2584601A/en not_active Abandoned
2000-12-19 KR KR1020027008587A patent/KR20030004316A/en not_active Application Discontinuation
2000-12-19 EP EP00989333A patent/EP1259917A1/en not_active Withdrawn
2000-12-19 BR BR0017059A patent/BR0017059A/en not_active Application Discontinuation
2000-12-19 WO PCT/US2000/034599 patent/WO2001050379A2/en not_active Application Discontinuation
2000-12-19 JP JP2001550666A patent/JP2004500646A/en active Pending
2000-12-19 PL PL00356538A patent/PL356538A1/en unknown
2000-12-19 CN CN 00817246 patent/CN1411582A/en not_active Application Discontinuation
2000-12-19 MX MXPA02006545A patent/MXPA02006545A/en not_active Application Discontinuation
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BR0017059A (en) 2002-10-22
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