Patent Application
20210390562
Aspects of the present disclosure relate to the creation of business indices which include, but are not limited to, analysis and scoring. More specifically, certain embodiments of the disclosure relate to a system and method for scoring companies which is then embodied in business indices.
Conventional approaches for scoring businesses and embodying the resulting analysis and scoring in corresponding indices are traditionally limited to analyzing quantitative data, with little or no ongoing measure of qualitative (unstructured) data.
Further limitations and disadvantages of conventional and traditional approaches includes: static point-in-time results, single-dimensional factor analysis and high costs (limiting the approach for smaller to mid-sized firms)
A system and/or method are provided for analyzing and scoring businesses and then embodying those results in corresponding indices as shown in and/or described in connection with at least one of the figures, as set forth more completely in the claims.
These and other advantages, aspects and novel features of the present disclosure, as well as details of an illustrated embodiment thereof, will be more fully understood from the following description and drawings.
By the systems and methods disclosed herein, unstructured data (including but not limited to text) is aggregated from various sources and operations on the resulting dataset(s) such as normalization and other transformations. These operations produce analytics and other results that can be integrated into various workflows. By way of example and without limitation, the analytics and other results may be used for credit risk monitoring and surveillance, market forecasts for particular companies and industries, stock portfolio selection and monitoring, finding potential customers, and performing due diligence on potential customers and other third parties. Risk and Growth scores/indices may be derived purely from unstructured data or a combination of structured and unstructured data.
Risk and Growth scores/indices are not limited to analyzing quantitative data, static point-in-time results, and single-dimensional factor analysis. Rather, Risk and Growth scores/indices as disclosed herein may utilize an ongoing measure of qualitative (unstructured) data.
Deriving company sentiment starts with a sentence-by-sentence analysis of a news article 101 and/or 103. A sentence may be parsed into verbs, nouns, modifiers, and other language components that are assigned a polarity. Modifiers may be assessed for polarity and directionality, and the overall sentence direction may be assessed through a sequencing of words. The relative magnitude of the sentence sentiment may be assigned according to algorithms, such as artificial intelligence algorithms, that are trained over a range of content. The overall sentiment score for the article 105 and 107 may be based on an average of the sentence sentiments within the article.
A daily sentiment score may be based on one or more sentiment scores corresponding to the one or more articles published on the particular day. Each article may be tagged with an entity, such as a company or other entity. For example, if articles 101 and 103 are tagged with the ACME Company, the daily sentiment score for the ACME Company would be based on at least the sentiment 105 and 107 of the corresponding articles 101 and 103 that were published on Jan. 12, 2020. The daily sentiment scores for a particular entity may be further averaged over a predetermined time period to determine an average sentiment score. The daily sentiment scores may be weighted to place a higher weight on more recent sentiment scores.
Each article may also be tagged with one or more of a plurality of signals 109 and 111. For example, article 101 may be tagged to a supplier issue, where a supplier issue is one of a plurality of predetermined signals related to financial risks. Likewise, article 103 may be tagged to stock rating, where stock rating is one of a plurality of predetermined signals related to financial risks.
By tagging articles with appropriate signals, risk may be correlated with sentiment to contribute to the definition and calculation of an entity's risk sentiment score. In some embodiments, negative sentiment is highly correlated with emerging risks.
A company's risk score may also be adjusted according to predictive insights, such as a likelihood of a credit downgrade, bankruptcy, etc. Predictive analytics using historical event-driven data may be used to assess the likelihood of future events, like bankruptcies, occurring. One or more signals may be associated with a potential risk for bankruptcy. For example, historical event-driven data may indicate that commercial bankruptcies in the US have certain events in common. The potential risk for bankruptcy signal may be tagged by a senior executive change, at least two credit down-grades, a steady decline in company sentiment, a number of significant lawsuits, and secured debt financing. If a company is tagged by a statistically significant number potential risk signals for bankruptcy, the company risk score will be raised.
Example scores include growth, risk, and sentiment. Many scores may be classified as risk and/or growth indicators. Growth and risk may be influenced by, for example, sentiment, average daily number of people at a site, and the number of job openings at a company. Those scores can be broken down even more, number of sales jobs, number of engineering jobs, etc. Furthermore, scores of one company may be influenced by another company's (or an industry's) risk, growth and sentiment scores. For example, the scores of suppliers, supply chain companies, an industry as a whole, partners, customers (particularly key customers) and competitors may all effect a company's risk and growth score.
Not all sub-scores (e.g., other company scores) will be weighted equally. Additionally, weighting may be based on recency where more weight is given to events that were more recent. For seasonal data, weighting may be based on events that occur in a comparative season or month. Weighting may also be controlled by an industry's average, highest peak, lowest valley or moving average for a particular time period. For example, changes and/or patterns in scores associated with a particular day/week/month over a time period for web traffic may have a large influence in growth and risk. Furthermore, scores (e.g., related to a stock price, average salary, number of customers) may predict financial health and revenues or expenses before such status is disclosed (e.g., in a quarterly report, layoff notice or facility expansion).
By tagging articles (or other information or data) with appropriate signals, growth may be correlated with sentiment to define an entity's growth sentiment score. In some embodiments, positive sentiment is highly correlated with growth potential.
By tagging articles with appropriate signals, ESG (environmental/social/governance) concerns may be correlated with sentiment to define an entity's ESG sentiment score.
A user may configure a sentiment gauge by company to trigger an alert (e.g., by email) of a change in any index. As
As indicated above and elsewhere in this specification, it is possible for the system to ingest and analyze many forms of information and data. By way of example and not limitation, the system can ingest mobile cell phone geolocation data, analyze it, and correlate it against other ingested data and/or one or more scores the system generates. By way of example and not limitation, the system may be used in a commercial real estate application where a prediction is made by the system of whether a company is going to renew or break a lease for one of its facilities.
By way of example and not limitation, the system may analyze the mobile data to identify important events or benchmarks reflected in other information or data in the system, and compare those to what is reflected in the mobile data, for example, to determine whether there is a lag between an announcement by a company, whether actual behaviors changed as indicated in the announcement, or whether the company is otherwise acting according to the announcement. Such a score may predict unexpected spikes or drops that may be used to influence the risk and growth scores.
A score based on the analysis of mobile data may also predict whether a company will break its lease, not renew its lease, or be looking to expand or change facilities. For example, if a company announces they are ramping up production at a particular plant, but there are no increased volumes at that site, the score may increase risk and decrease growth.
By way of example and not limitation, questions that may be investigated in the comparison of mobile data vs other data in the system may include:
What is the normal work population for the company?
What is the holiday work population for the company?
What is the natural business cycle for the company?
Does the company have a Work from Home (WFM) plan?
When did the company announce that plan?
When did the company institute that plan?
How well did the company follow that plan?
Does the company have a return to work plan?
When did the company announce that plan?
When did the company institute that plan?
How well did the company follow that plan?
Will work at that company be permanently changed?
Will the tenant downsize or reduce footprint?
What work is tied to that specific site?
Will they renew the lease if leased?
Will they break the lease if leased?
Will manufacturing or production be impacted?
If it is, what other companies will be impacted, such as competitors, suppliers, partners, customers?
As utilized herein the terms “circuits” and “circuitry” refer to physical electronic components (i.e. hardware) and any software and/or firmware (“code”) which may configure the hardware, be executed by the hardware, and or otherwise be associated with the hardware. As used herein, for example, a particular processor and memory may comprise a first “circuit” when executing a first one or more lines of code and may comprise a second “circuit” when executing a second one or more lines of code. As utilized herein, “and/or” means any one or more of the items in the list joined by “and/or”. As an example, “x and/or y” means any element of the three-element set {(x), (y), (x, y)}. In other words, “x and/or y” means “one or both of x and y”. As another example, “x, y, and/or z” means any element of the seven-element set {(x), (y), (z), (x, y), (x, z), (y, z), (x, y, z)}. In other words, “x, y and/or z” means “one or more of x, y and z”. As utilized herein, the term “exemplary” means serving as a non-limiting example, instance, or illustration. As utilized herein, the terms “e.g.,” and “for example” set off lists of one or more non-limiting examples, instances, or illustrations. As utilized herein, a battery, circuitry or a device is “operable” to perform a function whenever the battery, circuitry or device comprises the necessary hardware and code (if any is necessary) or other elements to perform the function, regardless of whether performance of the function is disabled or not enabled (e.g., by a user-configurable setting, factory trim, configuration, etc.).
While the present invention has been described with reference to certain embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the scope of the present invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the present invention without departing from its scope. Therefore, it is intended that the present invention not be limited to the particular embodiment disclosed, but that the present invention will include all embodiments falling within the scope of the appended claims.
