FETILDA: An Effective Framework For Fin-tuned Embeddings For Long Financial Text Documents

Understand

Unstructured data, especially text, continues to grow rapidly in various domains.

• In particular, in the financial sphere, there is a wealth of accumulated unstructured financial data, such as the textual disclosure documents that companies submit on a regular basis to regulatory agencies, such as the Securities and Exchange Commission (SEC).
• These documents are typically very long and tend to contain valuable soft information about a company's performance.
• It is therefore of great interest to learn predictive models from these long textual documents, especially for forecasting numerical key performance indicators (KPIs).