S&P Global has patented a computer-implemented method for textual data analysis. The method involves parsing text data from company transcripts, creating metrics, testing headline analytics for predictive efficacy, and combining them with economic analytics to predict financial performance. GlobalData’s report on S&P Global gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on S&P Global, was a key innovation area identified from patents. S&P Global's grant share as of May 2024 was 40%. Grant share is based on the ratio of number of grants to total number of patents.
Textual data analysis method using machine learning for financial prediction
A recently granted patent (Publication Number: US12001789B2) outlines a computer-implemented method for textual data analysis. The method involves using multiple processors to parse text data extracted from transcripts related to various companies, creating intermediate metrics, combining them into different combinations with headline analytics, and testing their predictive efficacy using a machine learning model based on historical data. The selected headline analytics are then applied to predict the financial performance of companies in combination with preexisting economic analytics. The method also includes checking the robustness of the selected headline analytics for additive predictive efficacy, ensuring they are not limited to specific industry sectors, calendar years, or constructions.
Furthermore, the patent describes a system and a computer program product for textual data analysis that follow a similar process of parsing, creating intermediate metrics, testing headline analytics, and predicting financial performance based on historical data. The system includes storage devices and processors to execute program instructions, while the computer program product comprises a computer-readable storage medium with embedded program instructions. Both the system and the computer program product emphasize the importance of removing data biases from the extracted text data before creating headline analytics, which may include look-ahead bias or survivorship bias. The headline analytics themselves can cover various aspects such as market-moving topics, sentiment scores, and call level transparency, providing a comprehensive approach to predicting financial outcomes based on textual data analysis.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
