Abstract: Some embodiments are directed to a resource allocation analysis system implemented via a back-end application computer server. A resource data store may contain electronic records associated with a set of resource types, each electronic record including an electronic record identifier and resource parameter. The back-end application computer server may receive, from the resource data store, information about a set of resource types to be analyzed, including the associated resource parameters. The computer server may then execute a differential evolutionary algorithm to optimize the set of resource types based on at least one non-linear constraint and generate resource analysis results. The back-end application computer server may, according to some embodiments, perform a resampling process that uses non-parameterized historical data, regression on at least one resource type, and moment matching.

Abstract: Some embodiments are directed to a resource allocation analysis system implemented via a back-end application computer server. A resource data store may contain electronic records associated with a set of resource types, each electronic record including an electronic record identifier and resource parameter. The back-end application computer server may receive, from the resource data store, information about a set of resource types to be analyzed, including the associated resource parameters. The computer server may then execute a differential evolutionary algorithm to optimize the set of resource types based on at least one non-linear constraint and generate resource analysis results. The back-end application computer server may, according to some embodiments, perform a resampling process that uses non-parameterized historical data, regression on at least one resource type, and moment matching.

Abstract: Some embodiments are directed to a resource allocation analysis system implemented via a back-end application computer server. A resource data store may contain electronic records associated with a set of resource types, each electronic record including an electronic record identifier and resource parameter. The back-end application computer server may receive, from the resource data store, information about a set of resource types to be analyzed, including the associated resource parameters. The computer server may then execute a differential evolutionary algorithm to optimize the set of resource types based on at least one non-linear constraint and generate resource analysis results. The back-end application computer server may, according to some embodiments, perform a resampling process that uses non-parameterized historical data, regression on at least one resource type, and moment matching.

Abstract: A computer system is provided for selecting an asset allocation for an investment portfolio intended to produce a target payout starting at a target date. The computer system is programmed to receive inputs that may relate to the target payout, the target date and a nominal rate of contributions to the investment portfolio. The computer system calculates a plurality of simulated investment outcomes based on assumed investment return rates and assumed inflation rates. The assumed investment return rates and assumed inflation rates are randomly selected in multi-year clusters from historical data. The computer system selects the asset allocation based on relative performance of the assumed asset allocations in the simulated investment outcomes.

Abstract: A computer system is provided for selecting an asset allocation for an investment portfolio intended to produce a target payout starting at a target date. The computer system is programmed to receive inputs that may relate to the target payout, the target date and a nominal rate of contributions to the investment portfolio. The computer system calculates a plurality of simulated investment outcomes based on assumed investment return rates and assumed inflation rates. The assumed investment return rates and assumed inflation rates are randomly selected in multi-year clusters from historical data. The computer system selects the asset allocation based on relative performance of the assumed asset allocations in the simulated investment outcomes.

Abstract: A computer system is provided for selecting an asset allocation for an investment portfolio intended to produce a target payout starting at a target date. The computer system is programmed to receive inputs that may relate to the target payout, the target date and a nominal rate of contributions to the investment portfolio. The computer system calculates a plurality of simulated investment outcomes based on assumed investment return rates and assumed inflation rates. The assumed investment return rates and assumed inflation rates are randomly selected in multi-year clusters from historical data. The computer system selects the asset allocation based on relative performance of the assumed asset allocations in the simulated investment outcomes.

Abstract: A computer system for determining investment fund holdings of a fund of funds accesses data relating to holdings of investment funds over time and investment rules of the funds of funds. The computer system analyzes the holdings of the investment funds over time to obtain investment performance data of the investment funds by asset class and volatility data. Using the investment performance data of the investment funds by asset class, the volatility data, and investment rules of the fund of funds, the computer system determines a selection of investment funds for holdings of the fund of funds.

Abstract: A computer system is provided for selecting an asset allocation for an investment portfolio intended to produce a target payout starting at a target date. The computer system is programmed to receive inputs that may relate to the target payout, the target date and a nominal rate of contributions to the investment portfolio. The computer system calculates a plurality of simulated investment outcomes based on assumed investment return rates and assumed inflation rates. The assumed investment return rates and assumed inflation rates are randomly selected in multi-year clusters from historical data. The computer system selects the asset allocation based on relative performance of the assumed asset allocations in the simulated investment outcomes.

Abstract: A computer system is provided for selecting an asset allocation for an investment portfolio intended to produce a target payout starting at a target date. The computer system is programmed to receive inputs that may relate to the target payout, the target date and a nominal rate of contributions to the investment portfolio. The computer system calculates at least one thousand simulated investment outcomes based on assumed investment return rates and assumed inflation rates. The assumed investment return rates and assumed inflation rates are randomly selected in multi-year clusters from historical data. The computer system selects the asset allocation based on relative performance of the assumed asset allocations in the simulated investment outcomes.

Abstract: A computer system is provided for selecting an asset allocation for an investment portfolio intended to produce a target payout starting at a target date. The computer system is programmed to receive inputs that may relate to the target payout, the target date and a nominal rate of contributions to the investment portfolio. The computer system calculates at least one thousand simulated investment outcomes based on assumed investment return rates and assumed inflation rates. The assumed investment return rates and assumed inflation rates are randomly selected in multi-year clusters from historical data. The computer system selects the asset allocation based on relative performance of the assumed asset allocations in the simulated investment outcomes.