Computer method and apparatus for optimizing portfolios of multiple participants

• US 6,393,409 B2
• Filed: 10/31/1997
• Issued: 05/21/2002
• Est. Priority Date: 10/31/1997
• Status: Expired due to Term

• Current Assignee: Morgan Stanley Services Group Inc. (Morgan Stanley)
• Original Assignee: Morgan Stanley Dean Witter & Company (Morgan Stanley)
• Inventors: Reddy, Stephen David, Lee, Young-Sup, Young, Andrew R., Tick, Evan, Chang, Yoon, Tse, Joan Ka-Wai, Scowcroft, John, Campbell, Roy Edwin II, Towse, Robert C. Jr.
• Primary Examiner(s): Hafiz, Tariq R.
• Assistant Examiner(s): MEINECKE DIAZ, SUSANNA M
• Application Number:
  US08/963,605
• Publication Number:
  US 20010014875A1
• Time in Patent Office: 1,663 Days
• Field of Search: 705/35, 705/36, 705/37, 705/39
• US Class Current: 705/37
• 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

Abstract

Computer technology for substantially optimizing portfolios of multiple participants is disclosed. Preferably the portfolios of such multiple participants comprise fixed income instruments. The disclosed systems and methods include using at least one computer system for storing digital data representing portfolio holdings of multiple parties and, in particular, for each participant storing in the computer memory data representing constraints with respect to the desired portfolio. The method and system comprise optimizing using an optimization engine portfolio and constraint information of multiple participants so as to generate a set of trades that would substantially optimize participants portfolios with respect to a known objective.

Claims

1. A computer method for adjusting portfolios of fixed income instruments of multiple parties, comprising:

storing in memory of at least one computer digital data representing the portfolios of the multiple parties;

storing in the memory of at least one computer digital data representing constraints that define trading requirements of the parties;

converting, using at least one computer, the digital data representing the portfolios of the multiple parties and the digital data representing the constraints of the multiple parties to optimization digital data adapted for processing by an optimization engine;

supplying first pricing information for the fixed-income instruments in the portfolios of the multiple parties from a publically available source;

optimizing using at least one computer the optimization digital data and the first pricing information so as to generate a first set of trades among the parties that rebalance the parties'"'"' portfolios of fixed-income instruments in accordance with the constraints that define trading requirements of the parties such that the portfolios are substantially optimized with respect to at least one predetermined objective;

communicating the first set of trades to each of the multiple parties;

receiving approval of the first set of trades from each of the multiple parties;

supplying second pricing information for the fixed-income instruments in the portfolios of the multiple parties, said second pricing information comprising prices quoted by traders of an intermediary entity that facilitates trades among the parties that rebalance the parties'"'"' portfolios of fixed-income instruments; and

optimizing using at least one computer the optimization digital data and the second pricing information so as to generate a second set of trades among the parties that rebalance the parties'"'"' portfolios of fixed-income instruments in accordance with the constraints that define trading requirements of the parties such that the portfolios are substantially optimized with respect to at least one predetermined objective; and

executing the second set of trades at the prices quoted by the traders of the intermediary entity.

2. The method of claim 1 wherein the predetermined objective is programmed as an objective function.

3. The method of claim 1 wherein the step of optimizing comprises computer processing of a linear programming problem.

4. The method of claim 1 wherein the step of optimizing comprises computer processing of a mixed integer programming problem.

5. The method of claim 1 wherein the step of converting further comprises converting digital data stored in the memory representing portfolio and constraint data into a matrix digital data suitable for processing by the optimization engine.

6. The method of claim 1 wherein the digital data representing the constraints includes digital data representing user constraints defining relationships between portfolio instruments that should be satisfied in a resultant portfolio produced by the optimization engine during optimizing.

7. The method of claim 6 wherein the user constraints include digital data representing duration neutrality constraints.

8. The method of claim 6 wherein the user constraints include digital data representing convexity neutrality constraints.

9. The method of claim 6 wherein the user constraints including digital data representing par-value weighted attributes.

10. The method of claim 6 wherein the user constraints include digital data representing proceeds bounding within sectors.

11. The method of claim 6 wherein the step of converting includes parsing the user constraints and building a data structure stored in memory of at least one computer as a tree data structure.

12. The method of claim 1 wherein the digital data representing the constraints includes digital data representing system constraints stored in the memory.

13. The method of claim 12 wherein the system constraints include digital data representing bond conservation constraints.

14. The method of claim 12 wherein the system constraints include digital data representing proceeds neutrality constraints.

15. The method of claim 12 wherein the system constraints include digital data representing non-negativity and boundedness.

16. The method of claim 12 wherein the system constraints include mutual exclusion digital data for avoiding churning.

17. The method of claim 12 wherein the system constraints include mutual exclusion digital data for avoiding wash sales.

18. The method of claim 12 wherein the system constraints include digital data for avoiding trading between subsidiaries of the same parent.

19. The method of claim 1 further comprising storing digital data representing an objective function for optimization.

20. The method of claim 19 wherein the objective function substantially maximizes tax deductions generated by traded participants'"'"' portfolios.

21. The method of claim 19 wherein the objective function substantially maximizes a total book loss in participants'"'"' portfolios.

22. The method of claim 21 wherein the objective function includes data representing economic value of tax deferral.

23. The method of claim 1 wherein the digital data, stored in computer memory, representing the constraints of multiple participants is organized in accordance with a formal grammar.

24. The method of claim 23 wherein the formal grammar includes representation of logical relationships among sectors.

25. The method of claim 23 wherein the formal grammar includes specifying bounded linear constraints.

26. The method of claim 25 wherein the formal grammar comprises specifying base attributes and normalization attributes of the constraints.