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: A method for facilitating a construction of a rank-ordered list of companies based on a theme is provided. The method includes identifying a plurality of companies associated with at least one stock exchange; determining search terms that relate to the theme; and constructing a query based on the search terms. For each company, the query is applied to a first set of company-specific textual sources and documents, in order to determine a textual relevance score, and the query is also applied to a second set of sources that relate to company-specific revenue data, in order to determine a revenue exposure score. The two scores are then combined into a composite score, and the companies are rank-ordered based on the respective composite scores. The rank-ordered list may be used for constructing a thematic investment portfolio.

Abstract: Systems and methods for content filtering based on relevance of publications are disclosed. In one embodiment, in an information processing apparatus comprising at least one computer processor, a method for content filtering of publications may include: (1) receiving a trained neural network, the trained neural network being trained to predict a relevance probability of a publication and comprising a plurality of word vectors, each word vector having a set of trained weights; (2) receiving a publication for evaluation; (3) identifying textual data in the publication; (4) identifying a plurality of sentences in the textual data; (5) identifying a plurality of words in the plurality of sentences; (6) transforming each of the words into word vectors; (7) applying the trained neural network to predict the relevance probability for the publication using the word vectors; and (8) outputting the relevance probability for the publication.