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.

With Chen Shen and Bersant Hobdari, we study how director interlocks and political ties on corporate boards may react to the patterns of promarket institutional changes in emerging markets, and how such changes affect firm performance such as sales growth and profitability.

In-Press at Industrial and Corporate Change