Confused Readers: A Modular Measure of Business Complexity

Business complexity is an important informational friction but challenging to measure. We use a GPT large language model fine-tuned on narrative disclosures and inline XBRL tags to measure business complexity from a user perspective. Our measure negatively correlates with the speed of capital market price adjustments to financial reports and positively correlates with filing delays even after controlling for existing complexity measures. We exploit the modularity offered by the measure to construct complexity scores for different transaction categories, such as debt, business combinations, and compensation. Using this modular measure, we find that more complex transactions receive more regulatory scrutiny, correlate with greater cash holdings, and predict worse future performance. Overall, our study reinforces complexity's role as a friction to decision-making and provides a richer and more flexible measure of complexity for future research.