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“Show Me!” The Informativeness of images in firms’ annual reports
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DOI：10.1007/s11142-026-09975-y.png)
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Abstract
En 中文
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.
Keywords:
Visual informativeness
Annual reports
Image-content reinforcement
Machine learning
Analyst forecast accuracy
Analyst forecast dispersion
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