Financial Event Analysis and Applications Based on Pre-trained Models

Overview Financial event analysis is a powerful tool for understanding events in the finance domain. Pre‑trained models, which have shown strong capabilities in translation, search, and generation, can also be effectively applied to vertical domains such as finance.

Main Tasks The analysis consists of three core tasks: (1) Intelligent parsing of unstructured data – extracting clean, semantically clear text from formats like PDFs; (2) Event semantic understanding – detecting events, extracting event elements, and extracting event relations; (3) Event graph analysis – analyzing event chains and predicting future events.

To support these tasks, a financial event taxonomy is built, combining expert knowledge and machine‑learning‑driven induction to cover diverse scenarios.

Event Graph An event graph consists of nodes (events or entities such as companies) and edges (relationships between events, between events and entities, or between entities). By converting unstructured documents into structured graphs, downstream applications like search, QA, risk monitoring, and quantitative investment become feasible.

Event Chain A simplified version of the event graph, focusing on a participant’s sequence of events and their relationships, reduces model complexity while preserving essential information.

Event Chain Prediction The prediction model comprises three parts: (1) Event representation – combining current, historical, and target events; (2) Sequential representation – an LSTM captures temporal dependencies; (3) Dynamic network – predicts candidate events based on the enriched representation, outputting a distribution over possible event types.

Technology Highlights The framework supports zero‑shot/few‑shot learning via prompt‑based fine‑tuning, enabling rapid adaptation to new event types.

Financial Event Analysis Techniques Two key components are event detection and event extraction. Detection extracts trigger words and classifies events, often using prompts with pre‑trained models. Extraction extends detection by also extracting event arguments, using generative approaches that require fewer annotations.

The proposed TDE‑GTEE model integrates trigger‑word detection with a generative extractor, using separate encoders for detection and extraction. It achieves SOTA results on ACE and ERE datasets and performs well in zero‑shot/few‑shot settings.

Framework Summary The overall framework provides capabilities to (1) search events for a given entity, (2) explore industry‑chain events, and (3) predict future events for a company. Its modular design allows selective use of components based on specific needs.

Q&A

Q1: Are there dedicated databases for event graphs, and is Neo4j suitable?

A1: Specialized event‑graph databases exist; Neo4j can be used, especially for complex scenarios, while simplified event chains can be stored more flexibly.

Q2: How to evaluate stock‑price impact analysis?

A2: Evaluation combines back‑testing with historical data and human judgment of event‑driven price movements.

In summary, a comprehensive event‑analysis framework powered by pre‑trained models can greatly enhance information understanding in the financial sector, and the TDE‑GTEE method demonstrates strong performance across various data regimes.