Computational Linguistic Analysis of Submitted SEC Information (CLASSI)

Text analysis is growing in research and practice of finance, management, and operations. Word associations offer deep insight at scale into dynamics, strategy, and tactics of industries, and thus automated text processing is of great interest. We report development of a new platform using a Latent Dirichlet Allocation (LDA) topic modeling process to analyze 10-K reports that publicly traded companies submit to the Securities and Exchange Commission (SEC). We describe evaluations of the system’s intrinsic performance and an important external measure, the ability to sort documents into Standard Industrial Classifications (SICs), a widely used measure of industry categories. We discuss potential applications in operations, finance, and management.