  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.
