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Reporting Technologies and Textual Readability

Author

Listed:
  • Xitong Li

    (HEC Paris - Ecole des Hautes Etudes Commerciales)

  • Hongwei (harry) Zhu

    (UMass Lowell - University of Massachusetts [Lowell] - UMASS - University of Massachusetts System)

  • Luo Zuo

    (Cornell University [New York])

Abstract

Financial reporting technologies can significantly affect how firms construct and disseminate quantitative and qualitative disclosures. Leveraging the opportunity created by the eXtensible Business Reporting Language (XBRL) mandate in the United States, we use a difference-in-differences approach to examine whether and how a firm's XBRL adoption affects the readability of its textual disclosures. We find that the initial adopters' HTML-formatted annual reports become harder to read after the XBRL mandate. Further analysis reveals that this effect is concentrated among adopters with more quantitative disclosures, those with smaller firm size, and those with a higher level of financial complexity. Importantly, we show that managers' reduced attention to preparing HTML-formatted annual reports, rather than increased disclosures, is likely the explanation for this decrease in textual readability. We also find that the negative effect on textual readability persists at least in the subsequent year. Taken together, our results suggest that although XBRL can standardize numerical disclosures, its initial adoption can divert managerial attention and result in reduced readability of textual disclosures.

Suggested Citation

  • Xitong Li & Hongwei (harry) Zhu & Luo Zuo, 2021. "Reporting Technologies and Textual Readability," Working Papers hal-03501712, HAL.
  • Handle: RePEc:hal:wpaper:hal-03501712
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    Cited by:

    1. Xiaohui Zhang & Qianzhou Du & Zhongju Zhang, 2022. "A theory‐driven machine learning system for financial disinformation detection," Production and Operations Management, Production and Operations Management Society, vol. 31(8), pages 3160-3179, August.

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