Granted Patent B2

US 7,539,636 · App. 10/674,432 · Granted May 26, 2009

Abstract

Claims (117)

1. A computer-implemented method for creating a database, said method comprising:

at one or more computers, collecting security transaction data for a preselected period of time, for a plurality of institutional investors, said transaction data including identity of securities being traded, transaction order sizes, execution prices and execution times;

grouping said transaction data into groups of orders, wherein each group of orders consists of a plurality of orders each associated with a common category from a plurality of common categories;

calculating a plurality of cost benchmarks for each group of orders;

estimating transaction costs for each institutional investor from said transaction data relative to each of said calculated cost benchmarks for each category of said plurality of common categories; and

storing said data for said calculated benchmarks and said estimated transaction costs;

wherein the grouping of transaction data into groups of orders includes combining discrete transaction data which form an order into each order.

2. The method as recited in claim 1 , wherein said estimating step includes a step of regressing said transaction costs onto a plurality of percentiles.

3. The method as recited in claim 2 , wherein said regressing step utilizes a formula:

X i =α i +β i f ( S )+γ i g ( M )+ε i ,

for percentiles i=25, 40, 50, 60 or 75, and each percentile i is assumed to depend linearly on functions f and g of size (S) and momentum (M) respectively, and (α i , β i , γ i ) are regression parameters.

4. The method as recited in claim 3 , wherein the regression parameters (α i , β i , γ i ) are estimated using (a) ordinary least squares (OLS), (b) weighted least squares (WLS) with respect to OLS residuals (WLS 1 ), and (c) WLS with respect to observations in each subdivision (WLS 2 ).

5. The method as recited in claim 3 , wherein functions f and g are set to be linear functions.

6. The method as recited in claim 1 , wherein said plurality of cost benchmarks include:

a closing price C T−1 of the security on a day prior to the day of the execution of the corresponding order;

a volume-weighted average price VWAP across all trades for the security during the day of execution of the corresponding order;

a closing price C T+1 of the security on the first day after the day of execution of the corresponding order;

a closing price C T+20 of the security on the 20th day after the day of execution of the corresponding order;

an open price O T of the security on the day of execution of the corresponding order; and

a prevailing midquote M T of the security prior to the execution time of the corresponding order; and

wherein each of said plurality of benchmarks are calculated for each security for each order.

7. The method recited in claim 1 , wherein said plurality of common categories are associated with at least cost factors size and momentum.

8. The method recited in claim 6 , wherein said plurality of common categories are associated with at least cost factors size and momentum.

9. The method as recited in claim 2 , wherein said regressing step utilizes the formula:

X i =α i +β i f ( S )+γ i g ( M )+ε i ,

for percentiles i=25, 40, 50, 60 or 75, and each percentile i is assumed to depend linearly on functions f and g of size (S) and momentum (M) respectively, and (α i , β i , γ i ) are regression parameters; and

wherein transaction costs are regressed for each of at least one cost factor.

10. The method as recited in claim 9 , wherein the regression parameters (α i , β i , γ i ) are estimated using (a) ordinary least squares (OLS), (b) weighted least squares (WLS) with respect to OLS residuals (WLS 1 ), and (c) WLS with respect to observations in each subdivision (WLS 2 ).

11. The method as recited in claim 9 , wherein functions f and g are set to be linear functions.

12. The method as recited in claim 1 , wherein said cost benchmarks are calculated in real-time as transactions are executed, and are stored in a database.

13. The method as recited in claim 1 , wherein said estimating step is performed periodically for all transactions that occurred during a predetermined time frame.

14. A computer-implemented method for ranking security transaction cost performance relative to transaction costs of other institutional investors, said method comprising steps of:

at one or more computers, collecting security transaction data for a preselected period of time, for a plurality of investment institutions, said transaction data including identity of securities being traded, transaction order sizes, execution prices, momentum and execution times;

grouping said transaction data into a plurality of orders, wherein each group of orders consists of a plurality of orders associated with a common category from a plurality of common categories;

calculating a plurality of cost benchmarks for each group of orders;

estimating transaction costs for each investment institution relative to each of said calculated cost benchmarks for each category of said plurality of common categories; and

ranking a first investment institution of said plurality of institutional investors against said plurality of investment institutions based on said estimated transaction costs for said plurality of institutions for at least one of said common categories;

wherein the grouping of transaction data into groups of orders includes combining discrete transaction data which form an order into each order.

15. The method as recited in claim 14 , wherein said estimating step includes a step of regressing said transaction costs onto a plurality of percentiles.

16. The method as recited in claim 15 , wherein said regressing step utilizes a formula:

X i =α i +β i f ( S )+γ i g ( M )+ε i ,

for percentiles i=25, 40, 50, 60 or 75, and each percentile i is assumed to depend linearly on functions f and g of size (S) and momentum (M) respectively, and (α i , β i , γ i ) are regression parameters.

17. The method as recited in claim 16 , wherein the regression parameters (α i , β i , β i ) are estimated using (a) ordinary least squares (OLS), (b) weighted least squares (WLS) with respect to OLS residuals (WLS 1 ), and (c) WLS with respect to observations in each subdivision (WLS 2 ).

18. The method as recited in claim 16 , wherein functions f and g are set to be linear functions.

19. The method as recited in claim 14 , wherein said plurality of cost benchmarks include:

a closing price C T−1 of the security on a day prior to the day of the execution of the corresponding order;

a volume-weighted average price VWAP across all trades for the security during the day of execution of the corresponding order;

a closing price C T+1 of the security on the first day after the day of execution of the corresponding order;

a closing price C T+20 of the security on the 20th day after the day of execution of the corresponding order;

an open price O T of the security on the day of execution of the corresponding order; and

a prevailing midquote M T of the security prior to the execution time of the corresponding order; and

wherein each of said plurality of benchmarks are calculated for each security for each order.

20. The method recited in claim 14 , wherein said plurality of common categories are associated with at least cost factors size and momentum.

21. The method recited in claim 19 , wherein said plurality of common categories are associated with at least cost factors size and momentum.

22. The method as recited in claim 15 , wherein said regressing step utilizes a formula:

X i =α i +β i f ( S )+γ i g ( M )+ε i ,

for percentiles i=25, 40, 50, 60 or 75, and each percentile i is assumed to depend linearly on functions f and g of size (S) and momentum (M) respectively, and (α i , β i , γ i ) are regression parameters; and

wherein transaction costs are regressed for each of at least one cost factor.

23. The method as recited in claim 22 , wherein the regression parameters (α i , β i , γ i ) are estimated using (a) ordinary least squares (OLS), (b) weighted least squares (WLS) with respect to OLS residuals (WLS 1 ), and (c) WLS with respect to observations in each subdivision (WLS 2 ).

24. The method as recited in claim 22 , wherein functions f and g are set to be linear functions.

25. The method as recited in claim 14 , wherein said cost benchmarks are calculated in real-time as transactions are executed, and are stored in a database.

26. The method as recited in claim 14 , wherein said estimating step is performed periodically for all transactions that occurred during a predetermined time frame.

27. A system for ranking security transaction cost performance relative to transaction costs for a plurality of institutional investors, said system comprising:

processing means for collecting security transaction data for a preselected period of time, for a plurality of institutional investment investors, said transaction data including identity of securities being traded, transaction order sizes, execution prices, momentum and execution times, grouping said transaction data into groups of orders, wherein each group of orders consist of a plurality of orders associated with a common category from a plurality of common categories; calculating a plurality of cost benchmarks for each group of orders; estimating transaction costs for each institutional investor from said transaction data relative to each of said calculated cost benchmarks for each category of said plurality of common categories; and ranking a first investment institution of said plurality of investment institutions based on said estimated transaction cost against said plurality of investment institutions for at least one of said common categories; and

storing means for receiving data from said processing means, storing said data, and making data available to said processing means;

wherein grouping of transaction data into groups of orders includes combination discrete transaction data which form an order into each order.

28. The system according to claim 27 , wherein said processing means estimates the transaction costs by regressing said transaction costs onto a plurality of percentiles.

29. The system according to claim 28 , wherein said processing means performs the regression by a formula:

X i =α i +β i f ( S )+γ i g ( M )+ε i ,

for percentiles i=25, 40, 50, 60 or 75, and each percentile i is assumed to depend linearly on functions f and g of size (S) and momentum (M) respectively, and (α i , β i , γ i ) are regression parameters.

30. The system according to claim 29 , wherein the regression parameters (α i , β i , γ i ) are estimated using (a) ordinary least squares (OLS), (b) weighted least squares (WLS) with respect to OLS residuals (WLS 1 ), and (c) WLS with respect to observations in each subdivision (WLS 2 ).

31. The system according to claim 29 , wherein functions f and g are set to be linear functions.

32. The system according to claim 27 , wherein said plurality of cost benchmarks include:

a closing price C T−1 of the security on a day prior to the day of the execution of the corresponding order;

a volume-weighted average price VWAP across all trades for the security during the day of execution of the corresponding order;

a closing price C T+1 of the security on the first day after the day of execution of the corresponding order;

a closing price C T+20 of the security on the 20th day after the day of execution of the corresponding order;

an open price O T of the security on the day of execution of the corresponding order; and

a prevailing midquote M T of the security prior to the execution time of the corresponding order; and

wherein each of said plurality of benchmarks are calculated for each security for each order.

33. The system according to claim 27 , wherein said plurality of common categories are associated with at least cost factors size and momentum.

34. The system according to claim 32 , wherein said plurality of common categories are associated with at least cost factors size and momentum.

35. The system according to claim 28 , wherein said processing means performs the regression by a formula:

X i =α i +β i f ( S )+γ i g ( M )+ε i ,

for percentiles i=25, 40, 50, 60 or 75, and each percentile i is assumed to depend linearly on functions f and g of size (S) and momentum (M) respectively, and (α i , β i , γ i ) are regression parameters; and

wherein transaction costs are regressed for each of at least one cost factor.

36. The system according to claim 35 , wherein the regression parameters (α i , β i , γ i ) are estimated using (a) ordinary least squares (OLS), (b) weighted least squares (WLS) with respect to OLS residuals (WLS 1 ), and (c) WLS with respect to observations in each subdivision (WLS 2 ).

37. The system according to claim 35 , wherein functions f and g are set to be linear functions.

38. The system according to claim 27 , wherein said cost benchmarks are calculated in real-time as transactions are executed, and are stored in a database.

39. The system according to claim 27 , wherein said processing means performs periodically for all transactions that occurred during a predetermined time frame.

40. A system for ranking security transaction cost performance relative to transaction costs for a plurality of institutional investors, said system comprising:

a processing unit coupled with a network and configured to collect security transaction data for a pre-selected period of time, for a plurality of investment institutional investors, said transaction data including identity of securities being traded, transaction order sizes, execution prices, momentum and execution times, to group said transaction data into groups of orders, wherein each group of orders consists of a plurality of orders associated with a common category from a plurality of common categories, to calculate a plurality of cost benchmarks for each group of orders, to estimate transaction costs for each order from said transaction data relative to each of said calculated cost benchmarks for each category of said plurality of common categories, and to store said data for said calculated benchmarks and said estimated transaction costs in a database; and

a database unit coupled with said processing unit and configured to communicate with said processing unit, store data, and make data available to said processing unit;

wherein grouping transaction data into groups of orders includes combining discrete transaction data which form an order into each order.

41. The system according to claim 40 , wherein said processing unit is further configured to estimate the transaction costs by regressing said transaction costs onto a plurality of percentiles.

42. The system according to claim 41 , wherein said processing unit is further configured to perform the regression by a formula:

X i =α i +β i f ( S )+γ i g ( M )+ε i ,

for percentiles i=25, 40, 50, 60 or 75, and each percentile i is assumed to depend linearly on functions f and g of size (S) and momentum (M) respectively, and (α i , β i , γ i ) are regression parameters.

43. The system according to claim 42 , wherein the regression parameters (α i , β i , γ i ) are estimated using (a) ordinary least squares (OLS), (b) weighted least squares (WLS) with respect to OLS residuals (WLS 1 ), and (c) WLS with respect to observations in each subdivision (WLS 2 ).

44. The system according to claim 43 , wherein functions f and g are set to be linear functions.

45. The system according to claim 44 , wherein said plurality of cost benchmarks include:

a closing price C T−1 of the security on a day prior to the day of the execution of the corresponding order;

a volume-weighted average price VWAP across all trades for the security during the day of execution of the corresponding order;

a closing price C T+1 of the security on the first day after the day of execution of the corresponding order;

a closing price C T+20 of the security on the 20th day after the day of execution of the corresponding order;

an open price O T of the security on the day of execution of the corresponding order; and

a prevailing midquote M T of the security prior to the execution time of the corresponding order; and

wherein each of said plurality of benchmarks are calculated for each security for each order.

46. The system according to claim 45 , wherein said plurality of common categories are associated with at least one cost factors size and momentum.

47. The system according to claim 45 , wherein said cost benchmarks are calculated in real-time as transactions are executed, and are stored in a database.

48. The system according to claim 45 , wherein said processing unit performs estimates periodically for all transactions that occurred during a predetermined time frame.

49. The system according to claim 40 , further comprising at least one client interface coupled with said database unit, said client interface configured to display a ranking for a selected institutional investor based on said data stored in said database unit.

50. The system according to claim 49 , wherein said client interface is configured to graphically display said ranking as bar graphs, said ranking shown as a percentage of a total range for a plurality of factors.

51. The system according to claim 49 , wherein said client interface is configured to graphically display said ranking as bar graphs, said ranking shown as a percentage of a total range for each said cost benchmark.

52. The method of claim 1 further comprising a step of preparing a graphical representation for display on a client interface said estimated transaction costs for a selected benchmark for a selected institutional investor for one or more selected common categories relative to one or more measures of central tendency or extrema of the estimated transaction costs of the plurality of institutional investors for said selected bench mark for said selected one or more common categories.

53. The method of claim 27 wherein the processing means further comprises processing means for preparing a graphical representation for a display on a client interface said estimated transaction costs for a selected benchmark for a selected institutional investor for one or more selected common categories relative to one or more measures of central tendency or extrema of the estimated transaction costs of the plurality of institutional investors for said selected bench mark for said selected one or more common categories.

54. The method of claim 40 wherein the processing unit is further configured to prepare a graphical representation for display on a client interface said estimated transaction costs for a selected benchmark for a selected institutional investor for one or more selected common categories relative to one or more measures of central tendency or extrema of the estimated transaction costs of the plurality of institutional investors for said selected bench mark for said selected one or more common categories.

Assignments (6)