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Earnings Attribution and Information Transfers

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  • David S. Koo
  • J. Julie Wu
  • P. Eric Yeung

Abstract

Managers frequently attribute the news in their earnings forecasts to various economic events. Using textual analysis, we identify the economic factors underlying earnings news from press releases. We document a wide range of industry†wide shocks and firm†specific actions to which the earnings news in management forecasts is attributed. As expected, earnings attributions significantly affect peer firms’ price reactions to the earnings news. Specifically, earnings news attributed to industry†wide trends or firm structural changes leads to positive information transfers but earnings news attributed to firm competitive moves triggers negative information transfers. Information transfers are much stronger when each economic factor is mentioned the first time in a given industry†year. Further analysis reveals that the strength of information transfers varies with firm†level rivalry within the industry (i.e., similar business strategies, market position, and level of competition).Dans leurs prévisions de résultats, les dirigeants attribuent souvent les nouvelles qu'ils communiquent à divers événements économiques. À l'aide d'une analyse textuelle, les auteurs recensent les facteurs économiques sous†jacents aux nouvelles relatives aux résultats publiées dans les communiqués de presse. Ils relèvent un vaste éventail de chocs de portée sectorielle et de décisions propres aux entreprises auxquels les nouvelles relatives aux résultats que contiennent les prévisions de la direction peuvent être attribuées. Conformément aux prévisions, les facteurs auxquels sont attribués les résultats ont une incidence sensible sur les réactions du cours des titres des entreprises homologues aux nouvelles relatives aux résultats. Plus précisément, les nouvelles relatives aux résultats qui sont attribuées à des tendances sectorielles ou à des changements structurels de l'entreprise engendrent des transferts d'information positifs, mais les nouvelles relatives aux résultats qui sont attribuées à des tactiques concurrentielles engendrent des transferts d'information négatifs. Les transferts d'information sont beaucoup plus marqués lorsque chaque facteur économique est mentionné pour la première fois dans un secteur et une année donnés. Une analyse plus approfondie révèle que la force des transferts d'information varie avec la rivalité des entreprises du secteur (c'est†à †dire la similarité de leurs stratégies d'activité, leur position sur le marché et l'intensité de la concurrence qu'elles se livrent).

Suggested Citation

  • David S. Koo & J. Julie Wu & P. Eric Yeung, 2017. "Earnings Attribution and Information Transfers," Contemporary Accounting Research, John Wiley & Sons, vol. 34(3), pages 1547-1579, September.
    • Handle: RePEc:wly:coacre:v:34:y:2017:i:3:p:1547-1579
    DOI: 10.1111/1911-3846.12308
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    Cited by:

    1. Fujitani, Ryosuke & Kim, Hyonok & Yamada, Kazuo, 2021. "Peer Effects of Corporate Disclosure in Pandemic Era," Working Paper Series 240, Management Innovation Research Center, School of Business Administration, Hitotsubashi University Business School.
    2. Antoni-Komar, Irene & Rommel, Marius & Posse, Dirk & Wittkamp, Moritz & Paech, Niko, 2021. "Stable Schools in der Solidarischen Landwirtschaft. Ein transdisziplinäres Setting zur Förderung organisationaler Stabilität," 61st Annual Conference, Berlin, Germany, September 22-24, 2021 317057, German Association of Agricultural Economists (GEWISOLA).
    3. Larjosto, Vilja, 2019. "Research through Design as a transformative approach," Forschungsberichte der ARL: Aufsätze, in: Abassiharofteh, Milad & Baier, Jessica & Göb, Angelina & Thimm, Insa & Eberth, Andreas & Knaps, Falc (ed.), Räumliche Transformation: Prozesse, Konzepte, Forschungsdesigns, volume 10, pages 217-225, ARL – Akademie für Raumentwicklung in der Leibniz-Gemeinschaft.
    4. Peng Guo, 2017. "Business Strategy and Intra-Industry Information Transfers," Accounting and Finance Research, Sciedu Press, vol. 6(3), pages 1-1, August.
    5. Berkin, Anil & Aerts, Walter & Van Caneghem, Tom, 2023. "Feasibility analysis of machine learning for performance-related attributional statements," International Journal of Accounting Information Systems, Elsevier, vol. 48(C).
    6. Steven Crawford & Ying Huang & Ningzhong Li & Ziyun Yang, 2020. "Customer Concentration and Public Disclosure: Evidence from Management Earnings and Sales Forecasts†," Contemporary Accounting Research, John Wiley & Sons, vol. 37(1), pages 131-159, March.

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