OUTLIER TRADE DETECTION FOR FINANCIAL ASSET TRANSACTIONS
Tools are provided for identifying outliers or variations in trade data derived from financial asset transactions, such as securities lending transactions, foreign exchange transactions, over the counter and exchange traded derivative transactions, and equity and fixed income transactions. Such outliers may provide an indication that a given trade is suspicious or potentially inappropriate from a customer relationship point of view, a regulatory perspective, or a legal standpoint. Trades identified as outliers can be utilized in regression analyses to analyze specific trades, trader-broker relationships, or other trading activity.
1 . A computer-implemented method for processing a trade data set associated with a financial asset, the method comprising:
using a processor to:
apply a first trade detection methodology to the trade data set for identifying outliers in groups of trades in the trade data set, wherein identifying outliers with the first trade detection methodology includes analyzing each identified group of trades to determine whether a variance spread between the trades is outside at least one predetermined variation threshold range;
apply a second trade detection methodology to the trade data set for identifying groups of trades in a pre-period or a post-period around a trade date for a financial asset, wherein the second detection methodology includes:
(i) calculating an extrapolated expected value in response to at least one value selected from the group consisting of a weighted average rebate rate for trades associated with the financial asset in the pre-period and a weighted average rebate rate for trades associated with the financial asset in the post-period, and
(ii) analyzing the financial asset trade in response to the extrapolated expected value and a variation threshold range around the extrapolated expected value to identify whether an outlier trade exists.
2 . The method of claim 1 , further comprising applying multiple variation threshold ranges in connection with executing the first trade detection methodology.
3 . The method of claim 1 , further comprising applying multiple variation threshold ranges in connection with executing the second trade detection methodology.
4 . The method of claim 1 , further comprising applying a consolidated methodology to combine results of the first and second trade detection methodologies to establish a consolidated outlier trade data set.
5 . The method of claim 4 , further comprising:
processing the consolidated outlier trade data set through a regression analysis; and,
determining the probability of creating an outlier on a trade-specific basis in connection with the regression analysis.
6 . The method of claim 4 , further comprising:
processing the consolidated outlier trade data set through a regression analysis; and,
determining the probability of creating an outlier on a trader-broker relationship in connection with the regression analysis.
7 . The method of claim 1 , wherein the trade data set is derived from a trade transaction involving at least one securities lending transaction.
8 . The method of claim 1 , wherein the trade data set is derived from a trade transaction involving at least one foreign exchange transaction.
9 . The method of claim 1 , wherein the trade data set is derived from a trade transaction involving at least one over the counter transaction.
10 . The method of claim 1 , wherein the trade data set is derived from a trade transaction involving at least one exchange traded derivative transaction.
11 . The method of claim 1 , wherein the trade data set is derived from a trade transaction involving at least one equity transaction.
12 . The method of claim 1 , wherein the trade data set is derived from a trade transaction involving at least one fixed income transaction.
13 . A computer-implemented trade data processing system for processing a trade data set in association with a financial asset, the system comprising:
a module for applying a first trade detection methodology to the trade data set for identifying outliers in groups of trades in the trade data set, wherein identifying outliers with the first trade detection methodology includes analyzing each identified group of trades to determine whether a variance spread between the trades is outside at least one predetermined variation threshold range;
a module for applying a second trade detection methodology to the trade data set for identifying groups of trades in a pre-period or a post-period around a trade date for a financial asset, wherein the second detection methodology includes:
(i) calculating an extrapolated expected value in response to at least one value selected from the group consisting of a weighted average rebate rate for trades associated with the financial asset in the pre-period and a weighted average rebate rate for trades associated with the financial asset in the post-period, and
(ii) analyzing the financial asset trade in response to the extrapolated expected value and a variation threshold range around the extrapolated expected value to identify whether an outlier trade exists; and,
a processor for executing the functions of the modules.
14 . The system of claim 13 , further comprising a module for applying multiple variation threshold ranges in connection with executing the first trade detection methodology.
15 . The system of claim 13 , further comprising a module for applying multiple variation threshold ranges in connection with executing the second trade detection methodology.
16 . The system of claim 13 , further comprising a module for applying a consolidated methodology to combine results of the first and second trade detection methodologies to establish a consolidated outlier trade data set.
17 . The system of claim 16 , further comprising:
a module for processing the consolidated outlier trade data set through a regression analysis; and,
a module for determining the probability of creating an outlier on a trade-specific basis in connection with the regression analysis.
18 . The system of claim 16 , further comprising:
a module for processing the consolidated outlier trade data set through a regression analysis; and,
a module for determining the probability of creating an outlier on a trader-broker relationship in connection with the regression analysis.