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Towards Earnings Call and Stock Price Movement

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  • Zhiqiang Ma
  • Grace Bang
  • Chong Wang
  • Xiaomo Liu

Abstract

Earnings calls are hosted by management of public companies to discuss the company's financial performance with analysts and investors. Information disclosed during an earnings call is an essential source of data for analysts and investors to make investment decisions. Thus, we leverage earnings call transcripts to predict future stock price dynamics. We propose to model the language in transcripts using a deep learning framework, where an attention mechanism is applied to encode the text data into vectors for the discriminative network classifier to predict stock price movements. Our empirical experiments show that the proposed model is superior to the traditional machine learning baselines and earnings call information can boost the stock price prediction performance.

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  • Zhiqiang Ma & Grace Bang & Chong Wang & Xiaomo Liu, 2020. "Towards Earnings Call and Stock Price Movement," Papers 2009.01317, arXiv.org.
    • Handle: RePEc:arx:papers:2009.01317
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    1. Monique, W.M. Donders & Roy Kouwenberg & Ton, C. F. Vorst, 2000. "Options and earnings announcements: an empirical study of volatility, trading volume, open interest and liquidity," European Financial Management, European Financial Management Association, vol. 6(2), pages 149-171, June.
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    Cited by:

    1. Sourav Medya & Mohammad Rasoolinejad & Yang Yang & Brian Uzzi, 2022. "An Exploratory Study of Stock Price Movements from Earnings Calls," Papers 2203.12460, arXiv.org.

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