Pleno-Alignment Framework for Stock Trend Prediction.

Area of Science:

• Computational finance and natural language processing.
• Machine learning for financial market analysis.

Background:

• Stock trend prediction is complex, involving market dynamics, human behavior, and sentiment.
• Existing methods using time-series or sentiment analysis often fail to integrate multimodal data effectively.
• Challenges include capturing dynamic interactions between text and price data and handling diverse textual perspectives.

Purpose of the Study:

• To propose the pleno-alignment framework (PAFrame) for enhanced multimodal stock information integration.
• To improve stock trend prediction accuracy by addressing limitations of previous approaches.
• To capture market dynamics through intermodal and intramodal alignment.

Main Methods:

• Integrating textual and time-series data into a shared representation space for modal-invariant learning.
• Employing contrastive learning to extract abstract semantic meanings from objective and subjective textual perspectives.
• Utilizing a hybrid approach with cross-attention mechanisms and prompt-guided language models for final prediction.

Main Results:

• The PAFrame framework demonstrates superior performance in stock trend prediction.
• Experiments on five real-world datasets confirm the effectiveness of the proposed method.
• The approach successfully captures dynamic interactions and diverse sentiments for improved accuracy.

Conclusions:

• The pleno-alignment framework offers a novel and effective approach to multimodal stock prediction.
• Integrating diverse data sources and perspectives enhances the robustness and accuracy of financial forecasting.
• PAFrame represents a significant advancement in applying machine learning to stock market analysis.