“Show Me!” The Informativeness of images in firms’ annual reports
Azi Ben-Rephael; Joshua Ronen; Tavy Ronen; Mi Zhou · 2026 · Review of Accounting Studies
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Abstract We consider how images (i.e., photos but not graphs, charts, or infographics) in annual reports provide users with information and use machine-learning algorithms to assess their informativeness. We develop a metric of content reinforcement, defined as the degree to which information investors extract from images complements and reinforces details in textual narratives. We find that firms are more likely to use images when they experience greater asset growth, have greater business complexity, and provide less readable textual disclosures—suggesting images are used more often when inf
Abstract by Azi Ben-Rephael; Joshua Ronen; Tavy Ronen; Mi Zhou, Review of Accounting Studies (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1007/s11142-026-09975-y