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.