PATENT CLAIM ANALYSIS

Application Number: 15979162
Application Type: Utility
Filing Date: 2018-05
Publication Date: 2018-09
Patent Classification: ["705", "03600R"]

Abstract:
Approaches to the construction of indexes are addressed wherein a portfolio of securities such as stocks, bonds, or the like and their associated investment weights or shares is generated. Indexes can be used as investment tools in various ways. For instance, indexes comprising a plurality of securities can often be bought and sold more cheaply than buying and selling the individual constituents of the index. This pricing differential allows investment with reduced transaction costs. Alternatively, in passive and enhanced indexing, investments are made with reference to an index. Performance statistics such as return and risk are reported with respect to the reference index. Factor indexes can serve as active manager benchmarks or the underlyers for investable products such as exchange traded funds and mutual funds. Computer based systems, methods and software are addressed for constructing indexes that replicate the returns of a quantitative factor such as medium term momentum or value. Further, processes and methodology are described by which the index can have the best possible replication of the underlying factor returns as well as other desirable characteristics. The methodology provides an approach to determine the index even when all desirable characteristics of the index are not simultaneously achievable.

Claim (Index 1):
A computer based method of constructing a factor index of portfolio weights that replicate returns associated with a target factor while simultaneously reducing implementation costs flowing from a large number of names and unintended bets on other factors comprising:\n selecting a set comprising a plurality of possible investments; defining a benchmark portfolio of weights comprising a subset of the set comprising a plurality of possible investments and a weight for each member of the subset; selecting a first factor risk model defined for the set comprising a plurality of possible investments, said first factor risk model defining a first matrix of factor exposures which gives a numerical exposure value for every possible investment for a set of factors; selecting a target factor for one of the factors defined by the first factor risk model; constructing a target factor portfolio of weights for the target factor for the set comprising a plurality of possible investments, the benchmark portfolio of weights, and the first factor risk model, and whose weighted average exposure to the target factor is different than the weighted average exposure of the benchmark portfolio to the target factor; selecting a second factor risk model defined for the set comprising a plurality of possible investments, said second factor risk model defining a second matrix of factor exposures, wherein at least one factor exposure in the second matrix of factor exposures of the second factor risk model is different than all the factor exposures of the first matrix of factor exposures of the first factor risk model; performing a first optimization by determining weights of each of the plurality of possible investments for a factor index so that a tracking error between the factor index and the target factor portfolio of weights as predicted by the second factor risk model is less than or equal to 5% and a one-way turnover is less than 7.5%; and outputting the factor index weights as an electronic output displayed on a display.

Metadata:
- Claim Count in Document: 1.0
- Percentile: 93.0
- Lexical Diversity: 1.71429
- Patent Class: 705.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['12958778', '13593415', '11931913', '14801775', '13216238']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.109901984451165
- 35 USC 102 Novelty (BERT): 0.5931869988593954
- Combined Prediction Score: 0.158230485891988
- Mean Citation Score: 339.72285
- Max Citation Score: 531.7676
- Similarity Product: 456.6343728692532

Labels:
- Claim Label 101: 0
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 0
- Combined Label: 0
- Label 101 Adjusted: 0

Dataset: test