Leveraging AI to Decipher How Supply Contracts Map to Revenues

Abstract

This paper leverages Generative Artificial Intelligence (GAI) tools to examine the role of supply contracts in revenue recognition, an important yet unexplored area in the literature. Using GAI to analyze material supply contracts disclosed in SEC filings, we first provide an overview of their common structure and specific contents. We then investigate how individual contract provisions relate to revenue recognition along three dimensions: (i) the revenues expected to be recognized from the contract, (ii) ex-ante uncertainty regarding the revenue to be recognized, and (iii) managerial discretion in revenue reporting. Finally, we apply machine learning techniques to assess the predictive power of GAI-extracted contract information for reported revenues and revenue-related accounting issues. The results show that contract information outperforms traditional financial variables and firm characteristics in both predictive settings. Overall, our paper highlights the value of detailed information embedded in supply contracts and the advantages of using GAI in contract analysis.

Keywords: Supply contracts, Revenue recognition, Revenue prediction, Generative AI, ChatGPT

JEL Classification: L14, M41, C45, C53, C81, G17