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 determining fair value prices of financial securities of international markets includes steps of selecting a universe of securities of a particular international market, computing overnight returns of each security in the selected universe over a predetermined past period of time, selecting at least one return factor of a domestic financial market from a plurality of return factors, computing, for each selected return factor, the return factor's daily return over said predetermined past period of time, calculating, for each selected return factor, a return factor coefficient for each security in the selected universe by performing a time series regression to obtain the contribution of each return factor's return to the security's overnight return, and storing each calculated return factor coefficient in a data file.

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: Methods and systems for optimizing a plurality of portfolios, each portfolio including one or more shares of one or more tradable assets, and may include the steps of: receiving asset data associated with the plurality of the portfolios; receiving one or more optimization constraints including at least one global constraint defining a constraint to be applied across an aggregate of the plurality of portfolios; for each portfolio, optimizing the asset data based on the one or more optimization constraints to create optimized portfolio data; aggregating the optimized portfolio data to create aggregate optimized asset data; determining if the aggregate optimized asset data satisfies the at least one global constraint; and only if the at least one global constraint is satisfied, outputting the optimized asset data.

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: Methods and systems for optimizing a plurality of portfolios, each portfolio including one or more shares of one or more tradable assets, and may include the steps of: receiving asset data associated with the plurality of the portfolios; receiving one or more optimization constraints including at least one global constraint defining a constraint to be applied across an aggregate of the plurality of portfolios; for each portfolio, optimizing the asset data based on the one or more optimization constraints to create optimized portfolio data; aggregating the optimized portfolio data to create aggregate optimized asset data; determining if the aggregate optimized asset data satisfies the at least one global constraint; and only if the at least one global constraint is satisfied, outputting the optimized asset data.

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 system and method for determining fair value prices of financial securities of international markets includes steps of selecting a universe of securities of a particular international market, computing overnight returns of each security in the selected universe over a predetermined past period of time, selecting at least one return factor of a domestic financial market from a plurality of return factors, computing, for each selected return factor, the return factor's daily return over said predetermined past period of time, calculating, for each selected return factor, a return factor coefficient for each security in the selected universe by performing a time series regression to obtain the contribution of each return factor's return to the security's overnight return, and storing each calculated return factor coefficient in a data file.

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, system and computer program product for forecasting the transaction cost of a portfolio trade execution that may be applied to any given trading strategy or an optimal trading strategy that minimizes transaction costs. The system accepts user-defined input variables from customers and generates a transaction cost estimation report based on those variables. Two models are utilized: discretionary and non-discretionary. A specific transaction cost estimation and optimization is performed that model the transaction costs of a specific trade execution based on the user's trading profile and market variables.

Abstract: A method for determining fair value prices of financial securities of international markets includes the steps of selecting a universe of securities of a particular international market; computing overnight returns of each security in the selected universe over a predetermined past period of time; selecting at least one return factor of a domestic financial market from a plurality of return factors; computing, for each selected return factor, the return factor's daily return over said predetermined past period of time; calculating, for each selected return factor, a return factor coefficient for each security in the selected universe by performing a time series regression to obtain the contribution of each return factor's return to the security's overnight return; and storing each calculated return factor coefficient in a data file; wherein the stored return factor coefficients can be used in conjunction with current return factor daily return values to predict current overnight returns for all securities in

Abstract: Methods and systems for optimizing a plurality of portfolios, each portfolio including one or more shares of one or more tradable assets, and may include the steps of: receiving asset data associated with said plurality of said portfolios; receiving optimization constraints including at least one global constraint defining a constraint to be applied across an aggregate of the plurality of portfolios; receiving one or more objectives to be applied to individual portfolios during optimization; aggregating the optimized portfolio data to create aggregate optimized asset data; determining if the aggregate optimized asset data satisfies the global constraint; and only if said at least one global constraint is satisfied, outputting said optimized asset data.

Abstract: The preferred embodiments provide improved systems, methods and products for the optimization of a portfolio and/or multi-portfolios of assets, such as stocks. In some preferred embodiments, new methodology can be employed wherein a confidence region for a mean-varience efficiency set is utilized. In some preferred embodiments, new methodology can be employed for improved computation of a reward-to-variability ratio or Sharpe Ratio. In some preferred embodiments, new methodology can be employed for multiportfolio optimization. In some preferred embodiments, a portfolio optimization engine or module can be adapted to implement one or more of these new methodologies, along with any other desired methodologies.

Abstract: Methods and systems for optimizing a plurality of portfolios, each portfolio including one or more shares of one or more tradable assets, and may include the steps of: receiving asset data associated with the plurality of the portfolios; receiving one or more optimization constraints including at least one global constraint defining a constraint to be applied across an aggregate of the plurality of portfolios; for each portfolio, optimizing the asset data based on the one or more optimization constraints to create optimized portfolio data; aggregating the optimized portfolio data to create aggregate optimized asset data; determining if the aggregate optimized asset data satisfies the at least one global constraint; and only if the at least one global constraint is satisfied, outputting the optimized asset data.

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 determining fair value prices of financial securities of international markets includes the steps of selecting a universe of securities of a particular international market; computing overnight returns of each security in the selected universe over a predetermined past period of time; selecting at least one return factor of a domestic financial market from a plurality of return factors; computing, for each selected return factor, the return factor's daily return over said predetermined past period of time; calculating, for each selected return factor, a return factor coefficient for each security in the selected universe by performing a time series regression to obtain the contribution of each return factor's return to the security's overnight return; and storing each calculated return factor coefficient in a data file; wherein the stored return factor coefficients can be used in conjunction with current return factor daily return values to predict current overnight returns for all securities in

Abstract: The preferred embodiments provide improved systems, methods and products for the optimization of a portfolio and/or multi-portfolios of assets, such as stocks. In some preferred embodiments, new methodology can be employed wherein a confidence region for a mean-varience efficiency set is utilized. In some preferred embodiments, new methodology can be employed for improved computation of a reward-to-variability ratio or Sharpe Ratio. In some preferred embodiments, new methodology can be employed for multiportfolio optimization. In some preferred embodiments, a portfolio optimization engine or module can be adapted to implement one or more of these new methodologies, along with any other desired methodologies.

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.