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An Exploratory Study of Stock Price Movements from Earnings Calls

Published: 16 August 2022 Publication History

Abstract

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.

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  • (2025)Are Natural Language Processing methods applicable to EPS forecasting in Poland?Data Science in Finance and Economics10.3934/DSFE.20250035:1(35-52)Online publication date: 2025
  • (2025)Extracting key insights from earnings call transcript via information-theoretic contrastive learningInformation Processing & Management10.1016/j.ipm.2024.10399862:3(103998)Online publication date: May-2025
  • (2024)COMET: NFT Price Prediction with Wallet ProfilingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671621(5893-5904)Online publication date: 25-Aug-2024
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    cover image ACM Conferences
    WWW '22: Companion Proceedings of the Web Conference 2022
    April 2022
    1338 pages
    ISBN:9781450391306
    DOI:10.1145/3487553
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 16 August 2022

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    Author Tags

    1. Earnings call
    2. natural language processing
    3. stock price movement

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    WWW '22: The ACM Web Conference 2022
    April 25 - 29, 2022
    Virtual Event, Lyon, France

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    Cited By

    View all
    • (2025)Are Natural Language Processing methods applicable to EPS forecasting in Poland?Data Science in Finance and Economics10.3934/DSFE.20250035:1(35-52)Online publication date: 2025
    • (2025)Extracting key insights from earnings call transcript via information-theoretic contrastive learningInformation Processing & Management10.1016/j.ipm.2024.10399862:3(103998)Online publication date: May-2025
    • (2024)COMET: NFT Price Prediction with Wallet ProfilingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671621(5893-5904)Online publication date: 25-Aug-2024
    • (2023)Global Counterfactual Explainer for Graph Neural NetworksProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570376(141-149)Online publication date: 27-Feb-2023
    • (2022)Truth or Fiction: Multimodal Learning Applied to Earnings Calls2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020307(3607-3612)Online publication date: 17-Dec-2022

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