Abstract: A method and system for forecasting the transaction cost of a portfolio trade execution that may be applied to any given trade strategy or an optimal trade strategy that minimizes transaction costs. In preferred embodiments, a server comprises one or more computers that act as an automated forecaster whereby it accepts user-defined input variables from customers and generates a transaction cost estimation report based on those variables. The server is programmed with specific transaction cost estimation and optimization algorithms that model the transaction costs of a specific trade execution based on the user's trading profile and market variables.

Abstract: A method and system for forecasting the transaction cost of a portfolio trade execution that may be applied to any given trade strategy or an optimal trade strategy that minimizes transaction costs. In preferred embodiments, a server comprises one or more computers that act as an automated forecaster whereby it accepts user-defined input variables from customers and generates a transaction cost estimation report based on those variables. The server is programmed with specific transaction cost estimation and optimization algorithms that model the transaction costs of a specific trade execution based on the user's trading profile and market variables.

Abstract: A method and system for forecasting the transaction cost of a portfolio trade execution that may be applied to any given trade strategy or an optimal trade strategy that minimizes transaction costs. In preferred embodiments, a server comprises one or more computers that act as an automated forecaster whereby it accepts user-defined input variables from customers and generates a transaction cost estimation report based on those variables. The server is programmed with specific transaction cost estimation and optimization algorithms that model the transaction costs of a specific trade execution based on the user's trading profile and market variables.

Abstract: A computerized method for generating risk forecasts is provided. A set of securities is selected. A set of risk factors is selected. The risk factor returns a determined. A risk factor covariance matrix and an idiosyncratic variance matrix are constructed. For each risk factor, a factor loading coefficient is determined for each selected security. The risk factor covariance matrix is projected into a future forecast. The idiosyncratic variance matrix is projected into a future forecast. The factor loading coefficients, the future forecast of the risk factor covariance matrix, and the future forecast of the idiosyncratic variance matrix can be used to determine a forecast of the variance-covariance matrix for the selected securities.

Abstract: A method and system for forecasting the transaction cost of a portfolio trade execution that may be applied to any given trade strategy or an optimal trade strategy that minimizes transaction costs. In preferred embodiments, a server comprises one or more computers that act as an automated forecaster whereby it accepts user-defined input variables from customers and generates a transaction cost estimation report based on those variables. The server is programmed with specific transaction cost estimation and optimization algorithms that model the transaction costs of a specific trade execution based on the user's trading profile and market variables.

Abstract: A computerized method for generating risk forecasts is provided. A set of securities is selected. A set of risk factors is selected. The risk factor returns a determined. A risk factor covariance matrix and an idiosyncratic variance matrix are constructed. For each risk factor, a factor loading coefficient is determined for each selected security. The risk factor covariance matrix is projected into a future forecast. The idiosyncratic variance matrix is projected into a future forecast. The factor loading coefficients, the future forecast of the risk factor covariance matrix, and the future forecast of the idiosyncratic variance matrix can be used to determine a forecast of the variance-covariance matrix for the selected securities.

Abstract: A system and method for comparing investment transaction costs of institution peers includes database and a processor coupled to a network. The processor may be configured receive, via the network, security transaction data of investment institutions, which included data for traded securities, transaction order sizes, execution prices, peer identities and timestamps. The processor is further capable of grouping transaction data into groups of orders, calculating order costs and environmental factors for each order, and calculating a peer's average order cost within each group. The data are stored in the database so that it may be retrieved and displayed.

Abstract: A method and system for forecasting the transaction cost of a portfolio trade execution that may be applied to any given trade strategy or an optimal trade strategy that minimizes transaction costs. In preferred embodiments, a server comprises one or more computers that act as an automated forecaster whereby it accepts user-defined input variables from customers and generates a transaction cost estimation report based on those variables. The server is programmed with specific transaction cost estimation and optimization algorithms that model the transaction costs of a specific trade execution based on the user's trading profile and market variables.

Abstract: A computerized method for generating risk forecasts is provided. A set of securities is selected. A set of risk factors is selected. The risk factor returns a determined. A risk factor covariance matrix and an idiosyncratic variance matrix are constructed. For each risk factor, a factor loading coefficient is determined for each selected security. The risk factor covariance matrix is projected into a future forecast. The idiosyncratic variance matrix is projected into a future forecast. The factor loading coefficients, the future forecast of the risk factor covariance matrix, and the future forecast of the idiosyncratic variance matrix can be used to determine a forecast of the variance-covariance matrix for the selected securities.

Abstract: A factor risk model based method for generating risk forecasts. In one embodiment, the method includes: selecting a set of securities; selecting a set of risk factors; determining the risk factor returns; constructing a risk factor covariance matrix; constructing an idiosyncratic variance matrix; determining, for each risk factor, a factor loading coefficient for each selected security; projecting the risk factor covariance matrix into a future forecast; and projecting the idiosyncratic variance matrix into a future forecast. The factor loading coefficients, the future forecast of the risk factor covariance matrix, and the future forecast of the idiosyncratic variance matrix can be used to determine a forecast of the variance-covariance matrix for the selected securities. In some embodiments, the step of estimating factor loadings includes performing a time series regression to obtain the sensitivity of each stocks' return to variations in the factor's return.

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.

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.

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.

Abstract: A factor risk model based method for generating risk forecasts. In one embodiment, the method includes: selecting a set of securities; selecting a set of risk factors; determining the risk factor returns; constructing a risk factor covariance matrix; constructing an idiosyncratic variance matrix; determining, for each risk factor, a factor loading coefficient for each selected security; projecting the risk factor covariance matrix into a future forecast; and projecting the idiosyncratic variance matrix into a future forecast. The factor loading coefficients, the future forecast of the risk factor covariance matrix, and the future forecast of the idiosyncratic variance matrix can be used to determine a forecast of the variance-covariance matrix for the selected securities. In some embodiments, the step of estimating factor loadings includes performing a time series regression to obtain the sensitivity of each stocks' return to variations in the factor's return.

Abstract: A method and system for forecasting the transaction cost of a portfolio trade execution that may be applied to any given trade strategy or an optimal trade strategy that minimizes transaction costs. In preferred embodiments, a server comprises one or more computers that act as an automated forecaster whereby it accepts user-defined input variables from customers and generates a transaction cost estimation report based on those variables. The server is programmed with specific transaction cost estimation and optimization algorithms that model the transaction costs of a specific trade execution based on the user's trading profile and market variables.