Finding Stakeholder-Material Information from 10-K Reports using Fine-Tuned BERT and LSTM Models
This addresses the challenge for financial analysts and regulators in processing large corporate documents, though it is incremental as it applies existing NLP methods to a specific domain.
The paper tackled the problem of efficiently extracting stakeholder-material information from lengthy 10-K reports by fine-tuning BERT and LSTM models, achieving an accuracy of 0.904 and F1 score of 0.899, significantly above a baseline keyword search model.
All public companies are required by federal securities law to disclose their business and financial activities in their annual 10-K reports. Each report typically spans hundreds of pages, making it difficult for human readers to identify and extract the material information efficiently. To solve the problem, I have fine-tuned BERT models and RNN models with LSTM layers to identify stakeholder-material information, defined as statements that carry information about a company's influence on its stakeholders, including customers, employees, investors, and the community and natural environment. The existing practice uses keyword search to identify such information, which is my baseline model. Using business expert-labeled training data of nearly 6,000 sentences from 62 10-K reports published in 2022, the best model has achieved an accuracy of 0.904 and an F1 score of 0.899 in test data, significantly above the baseline model's 0.781 and 0.749 respectively. Furthermore, the same work was replicated on more granular taxonomies, based on which four distinct groups of stakeholders (i.e., customers, investors, employees, and the community and natural environment) are tested separately. Similarly, fined-tuned BERT models outperformed LSTM and the baseline. The implications for industry application and ideas for future extensions are discussed.