Textual data analytics

A system for textual data analysis, the system comprising:

• a storage device configured to store program instructions;
• and
• one or more processors operably connected to the storage device and configured to execute the program instructions to cause the system to:
• parsing, according to natural language processing, text data extracted from a first number of transcripts related to a number of companies;
• create a number of intermediate metrics comprising numerical representations of the parsed text data and derivations of the parsed text data;
• combine the intermediate metrics into a number of different combinations, wherein each combination comprises a headline analytic;
• test, according to a machine learning model, each headline analytic for standalone predictive efficacy based on historical data;
• select headline analytics with standalone predictive efficacies above a first threshold;
• test, according to the machine learning model, each selected headline analytic for additive predictive efficacy to determine if the selected headline analytic incrementally increases the predictive efficacy of a preexisting economic analytic above a second threshold based on historical data;
• and
• apply, to a second number of transcripts, the selected headline analytics with an additive predictive efficacy above the second threshold, in combination with the preexisting economic analytic, to predict financial performance of companies that are the subjects of the second number of transcripts.

A computer program product for textual data analysis, the computer program product comprising:

• a computer-readable storage medium having program instructions embodied thereon to perform the steps of:
• parsing, according to natural language processing, text data extracted from a first number of transcripts related to a number of companies;
• creating a number of intermediate metrics comprising numerical representations of the parsed text data and derivations of the parsed text data;
• combining the intermediate metrics into a number of different combinations, wherein each combination comprises a headline analytic;
• testing, according to a machine learning model, each headline analytic for standalone predictive efficacy based on historical data;
• selecting headline analytics with standalone predictive efficacies above a first threshold;
• testing, according to the machine learning model, each selected headline analytic for additive predictive efficacy to determine if the selected headline analytic incrementally increases the predictive efficacy of a preexisting economic analytic above a second threshold based on historical data;
• and
• applying, to a second number of transcripts, the selected headline analytics with an additive predictive efficacy above the second threshold, in combination with the preexisting economic analytic, to predict financial performance of companies that are the subjects of the second number of transcripts.