Earticle

초록

영어

This study proposes a multimodal hybrid framework that integrates corporate disclosure texts, stock prices, and market indicators for enhanced stock price prediction and portfolio optimization. Long Short-Term Memory (LSTM) networks are employed to capture temporal dependencies from stock and market data, while FinBERT is used to extract semantic embeddings from disclosure documents. These heterogeneous modalities are fused within a classification model to evaluate the significance of corporate and market events on stock movements. Experimental results reveal substantial performance variation across industries, motivating industry-specific investment strategies. Portfolio optimization based on these multimodal strategies achieves superior riskadjusted returns compared to benchmark approaches.

목차

Abstract
I. INTRODUCTION
II. PROPOSED METHOD
A. Dataset
B. Problem Formulation
C. Feature Representation and Embedding
D. Classification Framework
III. EXPERIMENT
A. Baseline Model Comparison
B. Label-wise Analysis
C. Sectoral Differences
D. Portfolio Optimization
IV. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Noori Yun [ School of Electrical Engineering Korea University Seoul, Korea ]
• Junhee Seok [ School of Electrical Engineering Korea University Seoul, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004