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Natural Language Processing – Part II: Stock Selection

Highlights

Learn how natural language processing can be used to uncover the signal within the signal for stock selection.

Access the second part of S&P Global Market Intelligence's research report and make stock selection decisions with confidence.

Sep. 12 2018 — Astute investors have shifted their attention to explore the information content in unstructured data sets to differentiate their alpha. S&P Global Market Intelligence’s earnings call transcripts data is one such example that may offer that differentiated source of alpha.

  • Sentiment-based signals: Firms whose executives and analysts exhibited the highest positivity in sentiment during earnings calls outperformed their counterparts by 4.14% annually with significance at the 1% level. Firms with the largest year-over-year positive sentiment change and firms with the strongest positive sentiment trend outperformed their respective counterparts by 3.07% and 3.96% annually with significance at the 1% level. 
  • Behavioral-based signals: Firms whose executives provided the most transparency by using the simplest language and by presenting results with numbers outperformed their respective counterparts by 2.11% and 4.43% annually with significance at the 1% level.
  • Sentiment- and behavioral-based signals are not subsumed by commonly used alpha and risk signals. After adjustments, the signals generated excess long-short returns ranging from 1.65% to 3.64% annually with significance at the 1% level. The sentiment- and behavioral-based signals had some of the highest information ratios among all considered strategies and are lowly and negatively correlated with each other.
  • Positive language from the unscripted responses by the executives during the Q&A drove the overall predictability of the positive sentiment signal. 
  • The sentiment of CEOs has historically been more important than the sentiment of other executives. A strategy based on the sentiment of CEOs generated 3.63% per year on a long-short basis with significance at the 1% level. 
  • The aggregate sentiment of analysts historically enhanced the predictability of the 3-month FY1 EPS analyst revision signal. A strategy using the aggregate sentiment of analysts from earnings calls yielded 4.24% per year on a long-short basis with significance at the 1% level.

Natural Language Processing – Part II: Stock Selection

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Natural Language Processing, Part I: Primer

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Webinar Replay: Natural Language Processing - Unlocking New Frontiers In Corporate Earnings Sentiment Analysis

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Natural Language Processing – Part III: Feature Engineering

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