CUBAN: Leveraging Semantic Comparables to Predict Financial Metrics Using Textual Descriptions of Companies

Forecasting companies’ financial metrics, such as profit or revenue, from textual data is typically heuristic and subjective due to the qualitative nature of business models and data. However, this paper shows that these metrics can be predicted with surprising accuracy using only a textual description of a company’s business and public data from peer companies using our novel framework CUBAN (Contextual Understanding of Business performance through Analysis of Neighboring companies). We introduce a multimodal transformer model with an annotation-gating mechanism that effectively integrates textual context with financial statements. Trained on 10-K reports from public companies since 2000, our model predicts the future log revenue and operating profit from descriptions of a company’s business and those of its peers (along with peer financial data), achieving a correlation coefficient (Pearson’s R) of 0.78 for log revenue prediction and a 79% F1-score for operating profit classification, demonstrating its efficacy in forecasting financial performance from primarily qualitative data.