System and Method for Estimating Transaction Costs Related to Trading a Security

Abstract

A method for creating a peer group database includes a step of collecting security transaction data for a preselected period of time, for a plurality of investment institutions. The transaction data includes identity of securities being traded, transaction order sizes, execution prices and execution times. The transaction data is grouped into a plurality of orders. A plurality of cost benchmarks are calculated for each of the orders. Transaction costs are estimated for each investment institution relative to the cost benchmarks. The data is stored.

1 . A method for creating a peer group database, said method comprising:
 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 and execution times;   grouping said transaction data into a plurality of orders;   calculating a plurality of cost benchmarks for each of said plurality of orders;   estimating transaction costs for each investment institution relative to said cost benchmarks; and   storing said data.   
   
   
       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 the formula:
     X   i =α i +β i ƒ( 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 ƒ 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 (WLS1), and (c) WLS with respect to observations in each subdivision (WLS2). 
   
   
       5 . The method as recited in  claim 3 , wherein functions ƒ 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 estimating step takes into consideration a number of cost factors per order. 
   
   
       8 . The method recited in  claim 6 , wherein said estimating step takes into consideration a number of cost factors per order. 
   
   
       9 . The method as recited in  claim 8 , wherein said regressing step utilizes the formula:
     X   i =α i +β i ƒ( 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 ƒ 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 cost factors.   
   
   
       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 (WLS1), and (c) WLS with respect to observations in each subdivision (WLS2). 
   
   
       11 . The method as recited in  claim 9 , wherein functions ƒ 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 method for ranking a first institutional investor's security transaction cost performance relative to transaction costs of other institutional investors, said method comprising steps of:
 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;   calculating a plurality of cost benchmarks for each of said plurality of orders;   estimating transaction costs for each investment institution relative to said cost benchmarks; and   ranking said first institutional investor against said plurality of investment institutions for at least one of a number of factors.   
   
   
       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 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.   
   
   
       17 . A system for ranking a first institutional investor's security transaction cost performance relative to transaction costs of other institutional investors, said system comprising:
 processing means for 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; calculating a plurality of cost benchmarks for each of said plurality of orders;   estimating transaction costs for each investment institution relative to said cost benchmarks; and ranking said first institutional investor against said plurality of investment institutions for at least one of a number of factors; and   storing means for receiving data from said processing means, storing said data, and making data available to said processing means.   
   
   
       18 . The system according to  claim 17 , wherein said factors include size and momentum. 
   
   
       19 . The system according to  claim 17 , wherein said cost benchmarks are calculated in real-time as transactions are executed, and are stored in a database. 
   
   
       20 . The system according to  claim 17 , wherein said processing means performs periodically for all transactions that occurred during a predetermined time frame.