Sentiment Scores and Behavioral Metrics from Textual Data Analytics Offer Alternative Data-Sets to Make Informed Investment Decisions
S&P Global Market Intelligence announced the launch of Textual Data Analytics (TDA), a sophisticated new data offering which applies Natural Language Processing to generate sentiment scores and behavioral metrics based on company transcripts.
TDA makes it possible for businesses and investors to incorporate more qualitative measures of company performance into their investment strategy by quantifying sentiment and behavior during company calls. This new alternative data set complements the already existing coverage machine-readable transcripts, through Xpressfeed.
S&P Global Market Intelligence which offers a market leading coverage of corporate transcripts sourced from earnings calls, M&A calls and industry conferences for over 9,500 companies globally, has recently signed an exclusive partnership agreement with Scripts Asia to further extend its coverage through the addition of transcripts of Japanese earnings calls.
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Warren Breakstone, Managing Director and Chief Product Officer of Data Management Solutions at S&P Global Market Intelligence, said: “Clients are increasingly interested in applying natural language processing to unstructured text to uncover new insights that can help them make informed business and investment decisions. Our textual data analytics package of 40 behavioral and sentiment scores will further enrich our existing transcripts coverage, while offering clients a new alternative data set via our Xpressfeed delivery channel.”
Textual Data Analysis has been productized from two Quantamental Research papers on Natural Language Processing published by Frank Zhao at S&P Global market Intelligence (Paper I & Paper II).
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Frank Zhao, Senior Director of Quantamental Research, S&P Global Market Intelligence, said: “Our two research publications, which provide the empirical underpinnings for TDA, show that firms whose executives exhibited the most positive sentiment or provided the most transparency during their earnings calls, outperformed the broad U.S. equity market by 2.75% and 2.00% per annum between 2010 and 2017 respectively. Our analysis shows that textual analytics derived from earnings calls provide additional stock selection power, which is complementary to the existing analytics commonly used by institutional portfolio managers in selecting stocks.”
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