Textual Data Analytics: Sentiment Scores and Behavioural Metrics Derived from Earnings Call Transcripts
Textual Data Analytics: Sentiment Scores and Behavioural Metrics Derived from Earnings Call Transcripts

Abstract: 

The vast majority of the world’s data is unstructured, which can often make it difficult, time-consuming and expensive to analyze, understand and leverage. Textual Data Analytics from S&P Global Market Intelligence unlocks the text data from earnings calls for your analysis, leveraging Natural Language Processing technology to provide 39 sentiment and behavioural-based metrics for historical earnings call transcripts for over 9000 active companies.

Bio: 

Tim Morris leads Product Management EMEA for the Investment Management segment at S&P Global Market Intelligence, managing the company’s essential intelligence solutions for clients working across fundamental, quantitative and systematic investing. Specific areas of focus include work within Market Intelligence’s Alpha Factor Library and Quantamental Solutions products, Alternative Data developments and research across the continually-evolving library of SPGMI data that this year has brought Textual Data Analytics, Trucost Environmental Data and Panjiva Supply Chain Intelligence to market. Prior to his current role, he was a Founding Partner and Head of Trading at Rosiem Capital LLP, a globally-focused derivatives trading firm.

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