Show Me! The Informativeness of images in firms' annual reports

• 成果类型: Article
• 署名作者: Ben-Rephael, Azi; Ronen, Joshua; Ronen, Tavy; Zhou, Mi
• 署名单位: Rutgers University System; Rutgers University New Brunswick; Rutgers University Newark; New York University; Virginia Commonwealth University
• 刊物名称: REVIEW OF ACCOUNTING STUDIES
• ISSN/ISSBN: 1380-6653; 1573-7136
• DOI: 10.1007/s11142-026-09975-y
• 发表日期: 2026-09
• 页码: 1924-1969
• 关键词: Visual informativeness; Annual reports; Image-content reinforcement; Machine Learning; analyst forecast accuracy; Analyst forecast dispersion; D83; G12; G14; M41; investor attention; visual-attention; earnings; performance; picture; REPRESENTATION; READABILITY; disclosure; management; allocation

摘要

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 information processing costs are high. Our main results indicate that increases in visual prevalence and the extent to which images reinforce text are associated with greater analyst forecast accuracy and lower dispersion, suggesting that images improve users' information processing. Firms also increase image use after an exogenous decline in analyst coverage. Overall, firms use images when their information environment is poorer, and visual informativeness facilitates information assimilation.