Abstract: Disclosed is a system implementing smart beta factor deposition based on assets in an existing portfolio. The system typically includes a processor, a memory, and a module stored in the memory. The module is typically configured to: retrieve factor data for one or more securities associated with an existing portfolio; determine a score for beta factor models for the securities; receive threshold conditions associated with the existing portfolio; defining a plurality of factor sleeves for an investment portfolio; for each factor sleeve's beta factor model, determine a score of each of a plurality of securities; select one or more securities based on the score of each security according to the factor sleeve's beta factor model and the score of each of the securities in the existing portfolio until the factor sleeve's size has been reached; and combine the positions of each factor sleeve to create the investment portfolio.

Abstract: Disclosed is a system implementing smart beta factor deposition based on assets in an existing portfolio. The system typically includes a processor, a memory, and a module stored in the memory. The module is typically configured to: retrieve factor data for one or more securities associated with an existing portfolio; determine a score for beta factor models for the securities; receive threshold conditions associated with the existing portfolio; defining a plurality of factor sleeves for an investment portfolio; for each factor sleeve's beta factor model, determine a score of each of a plurality of securities; select one or more securities based on the score of each security according to the factor sleeve's beta factor model and the score of each of the securities in the existing portfolio until the factor sleeve's size has been reached; and combine the positions of each factor sleeve to create the investment portfolio.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for efficiently identifying investment opportunities for non-managed investment accounts. The system typically includes a processor, a memory, and a module stored in the memory. The module is typically configured to determine a score and percentile ranking for each constituent holding in a portfolio; receive threshold conditions from the customer, wherein each threshold condition is associated with a recommended action; determine whether the score and/or the percentile ranking satisfy at least one threshold condition; and generate and alert indicating that the at least one threshold condition has been satisfied and recommend execution of a corresponding action.

Abstract: Embodiments of the invention relate to systems, methods, and computer program products for monitoring financial risk indicators to provide a composite financial risk indicator and, based on the composite financial risk indicator, determining the number of years for which financial investors in distribution mode should have self-liquidating assets available (i.e., the number of rungs on a bond ladder) to insure that cash flow needs are met in future years regardless of the risk present in the financial markets. In response to determining the number of years for which the investors should have self-liquidating assets, the financial portfolios of the investors are automatically adjusted to provide for self-liquidating assets for each of the determined number of years (i.e., self-liquidating assets are bought or sold).

Abstract: Disclosed is a smart beta factor deposition system for constructing an investment portfolio. The smart beta factor deposition system typically includes a processor, a memory, and a deposition module stored in the memory. The deposition module is typically configured for: defining a plurality of factor sleeves, each factor sleeve defining (i) a beta factor model, (ii) one or more asset classes and/or asset class categories, (iii) a size, and (iv) one or more position sizes; for each sleeve's beta factor model, determining a score of each of a plurality of securities; for each factor sleeve, selecting one or more securities based on the score of each security according to the sleeve's beta factor model to form positions until the sleeve's size has been reached; and combining the positions of each factor sleeve to create the investment portfolio.

Abstract: Disclosed is a smart beta factor deposition system for constructing an investment portfolio. The system is typically configured for: defining a plurality of factor sleeves, each factor sleeve defining (i) a beta factor model, (ii) one or more asset classes and/or asset class categories, (iii) a size, and (iv) one or more position sizes; for each sleeve's beta factor model, determining a score of each of a plurality of securities; for each factor sleeve, selecting one or more securities based on the score of each security according to the sleeve's beta factor model to form positions until the sleeve's size has been reached; and combining the positions of each factor sleeve to create the investment portfolio. Each factor sleeve may be assigned to one of a plurality of customer accounts based on transaction costs associated with each customer account.

Abstract: Disclosed is an exchange traded fund (ETF) research system. The ETF research system typically includes a processor, a memory, and a scoring module stored in the memory. The scoring module is typically configured for determining a first score of each of a plurality of exchange traded fund according to a first multifactor model; based on the first score of each exchange traded fund, determining a first percentile ranking of each exchange traded fund relative to the plurality of exchange traded funds; determining a first percentile ranking for each of a plurality of asset class categories; and graphically presenting a first user interface including a numeric representation and a color representation of the first percentile ranking of one or more of the asset class categories.