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Analysts’ forecasting models and uncertainty about the past

Author

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  • Min Park

    (University of Kansas)

  • Tzachi Zach

    (Fisher College of Business, Ohio State University)

Abstract

We study the dynamics of information demand and supply in capital markets, focusing on how firms’ disclosures align with analysts’ information needs. Using a novel dataset from Visible Alpha, we analyze granular data from analysts’ forecasting models to understand the breadth of information they seek and how firms meet these demands through mandatory and voluntary disclosures. We document significant variation in the complexity of analysts’ models and the extent of firms’ disclosures, leading to some items in analysts’ models remaining undisclosed. This unmet information demand gives rise to a novel concept we term “uncertainty about the past” (UP). We investigate its implications for key capital market outcomes, including analyst forecast dispersion, market reactions to earnings announcements, and stock market liquidity. Our results demonstrate that UP plays a significant role in shaping the information environment, challenging the assumption that earnings announcements fully resolve uncertainty about past performance.

Suggested Citation

  • Min Park & Tzachi Zach, 2025. "Analysts’ forecasting models and uncertainty about the past," Review of Accounting Studies, Springer, vol. 30(3), pages 2376-2418, September.
    • Handle: RePEc:spr:reaccs:v:30:y:2025:i:3:d:10.1007_s11142-025-09898-0
    DOI: 10.1007/s11142-025-09898-0
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    JEL classification:

    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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