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News and Idiosyncratic Volatility: The Public Information Processing Hypothesis
[A Theory of Intraday Patterns: Volume and Price Variability]

Author

Listed:
  • Robert F Engle
  • Martin Klint Hansen
  • Ahmet K Karagozoglu
  • Asger Lunde
Abstract
Motivated by the recent availability of extensive electronic news databases and advent of new empirical methods, there has been renewed interest in investigating the impact of financial news on market outcomes for individual stocks. We develop the information processing hypothesis of return volatility to investigate the relation between firm-specific news and volatility. We propose a novel dynamic econometric specification and test it using time series regressions employing a machine learning model selection procedure. Our empirical results are based on a comprehensive dataset comprised of more than 3 million news items for a sample of 28 large U.S. companies. Our proposed econometric specification for firm-specific return volatility is a simple mixture model with two components: public information and private processing of public information. The public information processing component is defined by the contemporaneous relation with public information and volatility, while the private processing of public information component is specified as a general autoregressive process corresponding to the sequential price discovery mechanism of investors as additional information, previously not publicly available, is generated and incorporated into prices. Our results show that changes in return volatility are related to public information arrival and that including indicators of public information arrival explains on average 26% (9–65%) of changes in firm-specific return volatility.

Suggested Citation

  • Robert F Engle & Martin Klint Hansen & Ahmet K Karagozoglu & Asger Lunde, 2021. "News and Idiosyncratic Volatility: The Public Information Processing Hypothesis [A Theory of Intraday Patterns: Volume and Price Variability]," Journal of Financial Econometrics, Oxford University Press, vol. 19(1), pages 1-38.
    • Handle: RePEc:oup:jfinec:v:19:y:2021:i:1:p:1-38.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbaa038
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    Citations

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    Cited by:

    1. Aysan, Ahmet Faruk & Caporin, Massimiliano & Cepni, Oguzhan, 2024. "Not all words are equal: Sentiment and jumps in the cryptocurrency market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    2. Gu, Leilei & Li, Xiaoyu & Peng, Yuchao & Zhou, Junnan, 2022. "Voluntary CEO turnover, online information, and idiosyncratic volatility," Finance Research Letters, Elsevier, vol. 49(C).
    3. Luca Vincenzo Ballestra & Enzo D’Innocenzo & Andrea Guizzardi, 2024. "Score-Driven Modeling with Jumps: An Application to S&P500 Returns and Options," Journal of Financial Econometrics, Oxford University Press, vol. 22(2), pages 375-406.
    4. Xiaohong Shen & Gaoshan Wang & Yue Wang & Alfred Peris, 2021. "The Influence of Research Reports on Stock Returns: The Mediating Effect of Machine-Learning-Based Investor Sentiment," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-14, December.
    5. He, Feng & Liu, Guanchun & Hao, Jing & Li, Youwei, 2023. "CSR performance and firm idiosyncratic risk in a data-rich environment: The role of retail investor attention," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 89(C).
    6. Lee, Kangsan & Jeong, Daeyoung, 2023. "Too much is too bad: The effect of media coverage on the price volatility of cryptocurrencies," Journal of International Money and Finance, Elsevier, vol. 133(C).
    7. Jeon, Yoontae & McCurdy, Thomas H. & Zhao, Xiaofei, 2022. "News as sources of jumps in stock returns: Evidence from 21 million news articles for 9000 companies," Journal of Financial Economics, Elsevier, vol. 145(2), pages 1-17.
    8. Chen, Sipeng & Li, Gang, 2023. "Why does option-implied volatility forecast realized volatility? Evidence from news events," Journal of Banking & Finance, Elsevier, vol. 156(C).

    More about this item

    Keywords

    firm-specific news; realized volatility; public information arrival; information processing hypothesis of return volatility;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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