Event Extraction and Impact Analysis of Financial News Based on Attention Mechanism

Event Extraction and Impact Analysis of Financial News Based on Attention Mechanism

Topic Description
Financial news often contains significant events that impact the market (such as corporate mergers and acquisitions, earnings releases, policy changes, etc.). How to automatically extract event information from unstructured text and analyze its potential impact on market prices is a key issue in fintech. This topic requires an understanding of the technical process of event extraction, with a focus on mastering the role of attention mechanisms in improving the accuracy of event element extraction, as well as the methods for analyzing the correlation between events and market impact.

Step-by-Step Explanation of Key Knowledge Points

Step 1: Basic Definition and Task Breakdown of Event Extraction

  1. Event Definition: A financial event is typically composed of a trigger word (such as "acquire," "rise," "plunge") and elements (such as time, subject, object, numerical value).
    Example: In the sentence "Company A's stock price plunged 20% today," the trigger word is "plunged," and the elements include subject (Company A), numerical value (-20%), and time (today).
  2. Task Phases:
    • Event Detection: Identify whether the text contains an event and locate the trigger word.
    • Element Extraction: Extract structured elements of the event, such as participating subjects, time, location, etc.

Step 2: Limitations of Traditional Methods
Traditional methods (such as rule matching, statistical models) face issues:

  • Diverse synonyms in financial texts (e.g., "fall," "decline," "shrink" all indicate a drop), making rules difficult to cover comprehensively.
  • Elements may be scattered in long texts, and reliance on syntactic parsing can lead to error propagation.

Step 3: Core Idea of the Attention Mechanism

  1. Motivation: Allow the model to dynamically focus on key parts of the text related to the event while ignoring redundant information.
  2. Calculation Steps (taking attention in neural networks as an example):
    • Compute attention weights for the embedding vectors of input words (e.g., Word2Vec, BERT outputs):

\[ \alpha_i = \frac{\exp(\mathbf{v}^\top \tanh(\mathbf{W} \mathbf{h}_i))}{\sum_j \exp(\mathbf{v}^\top \tanh(\mathbf{W} \mathbf{h}_j))} \]

 where $\mathbf{h}_i$ is the vector of the $i$-th word, and $\mathbf{W}$ and $\mathbf{v}$ are learnable parameters.
  • Obtain the context vector via weighted sum: \(\mathbf{c} = \sum_i \alpha_i \mathbf{h}_i\).
  1. Advantage: The model can autonomously learn strong associations between words like "plunge," "20%," and "Company A," even if they are far apart in the text.

Step 4: Event Extraction Model Combined with Attention Mechanism (Using BERT+Attention as an Example)

  1. Input Processing: Feed the news text into the BERT model to obtain contextual vectors for each word.
  2. Trigger Word Identification:
    • Pass the BERT output for each word through a fully connected layer and use Softmax classification to determine if it is a trigger word (e.g., labeling "plunge" as a "drop-type trigger word").
  3. Element Extraction:
    • Use self-attention mechanism to compute association weights between words and the trigger word.
    • Words with high weights serve as candidate elements, which are then classified by a classifier to determine their roles (e.g., classifying "Company A" as "subject").
  4. Training Data: Requires annotated trigger words and elements in financial news (using datasets such as ACE2005, FinEvent, etc.).

Step 5: Analysis of Event Impact on the Market

  1. Event Type Mapping: Categorize extracted events into predefined types (e.g., "earnings announcement," "regulatory penalty") and match them with a historical market reaction database.
  2. Impact Quantification:
    • Statistically analyze the average price change of related assets after similar historical events (e.g., stock price rises an average of 2% within 3 days after better-than-expected earnings reports).
    • Use regression models to control for other variables (such as overall market volatility) and analyze the independent impact of events.
  3. Real-time Application: After automated systems extract news events, generate trading signals or risk alerts based on historical patterns.

Summary
The attention mechanism enhances the robustness of event extraction by focusing on key information. When combined with historical data analysis, it can quantify event impact. In practical applications, challenges such as domain adaptation (e.g., fine-tuning BERT for financial terminology) and handling overlapping events (multiple events in the same text) must be addressed.