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Decoding sentiment: Methodology behind SAFE's Manager Sentiment Index

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  • Fadavi, Sara
  • Hillert, Alexander

Abstract

SAFE's monthly Manager Sentiment Index is constructed by extracting sentiment from corporate financial disclosures of listed companies in Germany, offering significant insights into top management's perspectives. This white paper outlines the methodology behind the index and its financial implications. Information about managers' assessment of firms' performance and financial conditions is material to investors but, at the same time, hard to observe. The SAFE Manager Sentiment Index quantifies managers' beliefs using textual analysis of financial reports and earnings conference call transcripts. We show that the index is a strong predictor of future stock market returns. In summary, the SAFE Manager Sentiment Index provides a practical tool for key stakeholders such as investors, analysts, and policymakers seeking timely signals of corporate sentiment.

Suggested Citation

  • Fadavi, Sara & Hillert, Alexander, 2024. "Decoding sentiment: Methodology behind SAFE's Manager Sentiment Index," SAFE White Paper Series 109, Leibniz Institute for Financial Research SAFE.
  • Handle: RePEc:zbw:safewh:308085
    as

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    References listed on IDEAS

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    1. Price, S. McKay & Doran, James S. & Peterson, David R. & Bliss, Barbara A., 2012. "Earnings conference calls and stock returns: The incremental informativeness of textual tone," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 992-1011.
    2. Tarek A Hassan & Stephan Hollander & Laurence van Lent & Ahmed Tahoun, 2019. "Firm-Level Political Risk: Measurement and Effects," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(4), pages 2135-2202.
    3. Christina Bannier & Thomas Pauls & Andreas Walter, 2019. "Content analysis of business communication: introducing a German dictionary," Journal of Business Economics, Springer, vol. 89(1), pages 79-123, February.
    4. Jiang, Fuwei & Lee, Joshua & Martin, Xiumin & Zhou, Guofu, 2019. "Manager sentiment and stock returns," Journal of Financial Economics, Elsevier, vol. 132(1), pages 126-149.
    5. Diego García, 2013. "Sentiment during Recessions," Journal of Finance, American Finance Association, vol. 68(3), pages 1267-1300, June.
    6. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    Full references (including those not matched with items on IDEAS)

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    Keywords

    Manager Sentiment; Textual Analysis; Financial Disclosures; Return Predictability;
    All these keywords.

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