Using GPT to Measure Business Complexity
利用GPT模型从企业披露信息中构建并验证了商业复杂性指标，发现其与资本市场定价速度相关，并用于研究债务复杂性，揭示非标准债务条款是复杂性的来源，且债务复杂性有助于企业在信贷恶化时管理财务风险。
ABSTRACT Business complexity involves important tradeoffs for managers and investors, but empirical evidence is limited by measurement issues. We construct and validate a measure of business complexity using a GPT model fine-tuned on narrative disclosures and inline XBRL tags. We first show that our measure is associated with slower price formation in capital markets, consistent with complexity increasing processing costs. Next, we apply our measure to study the complexity of debt, an economically important topic that encompasses a wide range of features. The results show that nonstandard debt features such as call and convertibility provisions underlie debt complexity. We also find that debt complexity correlates with more persistent interest expense and better performance when lending conditions worsen, suggesting it is in part an adaptive response to manage financial risk. Overall, our study underscores the tradeoffs of business complexity and provides a flexible measure of complexity for future research. Data Availability: Contact authors for data, model weights, and measure. JEL Classifications: D82; D83; G14; G30; M40; M41.