Abstract: Embodiments disclosed herein provide for systems and methods of calculating the coefficients and creating a linear multivariate model of price returns for a given target portfolio by using the factor characteristic data of the fund's constituents at a particular point in time. The systems and methods provide for creating quantile matrices based on the target portfolio and a plurality of synthetic factor portfolios, and computing weights on each synthetic factor portfolio such that the sum of squared differences between each cell in the profile matrix of the fund and the factor portfolios is minimized.

Abstract: Embodiments disclosed herein provide for systems and methods of calculating the coefficients and creating a linear multivariate model of price returns for a given target portfolio by using the factor characteristic data of the fund's constituents at a particular point in time. The systems and methods provide for creating quantile matrices based on the target portfolio and a plurality of synthetic factor portfolios, and computing weights on each synthetic factor portfolio such that the sum of squared differences between each cell in the profile matrix of the fund and the factor portfolios is minimized.

Abstract: An embodiment of the present invention is directed to a portfolio insights observation engine. The observation engine performs: identifying a portfolio; identifying one or more goals and concerns specific to the portfolio; selecting a benchmark model portfolio; identifying a set of metrics for comparison between the portfolio and the benchmark model portfolio; evaluating one or more deviations relative to the set of metrics associated with the benchmark model portfolio; applying the one or more goals and concerns to the deviations; generating observations based on the benchmark portfolio; ranking the observations based on risk; identifying a subset of ranked observations; and providing, via the interactive interface, customized insights and solutions for each of the ranked observations.