Abstract
This study develops a novel approach to identify characteristics of images and a combination thereof (expressiveness) that are likely to and found to be associated with investment decisions. We also create new methodologies to quantify these characteristics. We further develop a new machine learning-based measure of informativeness called additivity, which is the degree to which the images convey information beyond the content embedded in textual narratives; additivity is significantly associated with funding beyond other image characteristics. We also address the causal impact of image characteristics using the onset of COVID-19 as an exogenous shock and a difference-in-difference methodology. Our exploration of the implications for financing decisions goes beyond the existing imagery studies within this developing field.
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