Paper: arXiv 2509.10810

Authors: Yao-Lin Chang, Chun-Yang Lin, Chi-Chun Liu, Stephen G. Ryan

Abstract

We examine the relative timeliness with which write-downs of long-lived assets incorporate adverse macroeconomic and industry outcomes versus adverse firm-specific outcomes. We posit that users of financial reports are more likely to attribute adverse firm-specific outcomes to suboptimal managerial actions, which provide managers with more incentive to delay write downs. We provide evidence that, controlling for other incentives to manage earnings, firms record write-downs in the current year that are driven by adverse macroeconomic and industry outcomes during both the current year and the next year, but they record write-downs driven by adverse firm-specific outcomes only in the current year.

Complexity vs Empirical Score

  • Math Complexity: 2.5/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper applies psychological attribution theory to corporate financial reporting, presenting a clear empirical design to test managerial incentives behind asset write-downs. While mathematical complexity is minimal (relying primarily on standard panel regression techniques), the empirical framework is rigorous and well-grounded in empirical accounting literature.

Research Flowchart

  flowchart TD
    A[Research Goal: Assess write-down timeliness for macro vs. firm-specific adverse outcomes] --> B[Data Inputs: Financial reports, macroeconomic indicators, & industry performance metrics]
    B --> C[Methodology: Decompose impairment drivers into macro/industry vs. firm-specific factors]
    C --> D[Empirical Analysis: Test lag between adverse performance and write-down recognition, controlling for earnings management]
    D --> E[Key Finding 1: Macro/Industry-driven write-downs incorporate current AND next-year adverse outcomes]
    D --> F[Key Finding 2: Firm-specific write-downs are delayed, incorporating ONLY current-year outcomes]

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  36. Data availability: Data will be made available on request. The data are available from the database identified in the manuscript

0 of 44 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.