An Exploratory Study of Stock Price Movements from Earnings Calls

Sourav Medya, Mohammad Rasoolinejad, Yang Yang, Brian Uzzi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Financial market analysis has focused primarily on extracting signals from accounting, stock price, and other numerical "hard"data reported in P&L statements or earnings per share reports. Yet, it is well-known that decision-makers routinely use "soft"text-based documents that interpret the hard data they narrate. Recent advances in computational methods for analyzing unstructured and soft text-based data at scale offer possibilities for understanding financial market behavior that could improve investments and market equity. A critical and ubiquitous form of soft data are earnings calls. Earnings calls are periodic (often quarterly) statements usually by CEOs who attempt to influence investors' expectations of a company's past and future performance. Here, we study the statistical relationship between earnings calls, company sales, stock performance, and analysts' recommendations. Our study covers a decade of observations with approximately 100,000 transcripts of earnings calls from 6,300 public companies from January 2010 to December 2019. In this study, we report three novel findings. First, the buy, sell and hold recommendations from professional analysts made prior to the earnings have low correlation with stock price movements after the earnings call. Second, using our graph neural network based method that processes the semantic features of earnings calls, we reliably and accurately predict stock price movements in five major areas of the economy. Third, the semantic features of transcripts are more predictive of stock price movements than sales and earnings per share, i.e., traditional hard data in most of the cases.

Original languageEnglish (US)
Title of host publicationWWW 2022 - Companion Proceedings of the Web Conference 2022
PublisherAssociation for Computing Machinery, Inc
Number of pages12
ISBN (Electronic)9781450391306
StatePublished - Apr 25 2022
Event31st ACM Web Conference, WWW 2022 - Virtual, Online, France
Duration: Apr 25 2022 → …

Publication series

NameWWW 2022 - Companion Proceedings of the Web Conference 2022


Conference31st ACM Web Conference, WWW 2022
CityVirtual, Online
Period4/25/22 → …


  • Earnings call
  • natural language processing
  • stock price movement

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software


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