Earticle

초록

영어

The stock market changes continuously as new information emerges, affecting the judgments of investors. Online news articles are valued as a traditional window to inform investors about various information that affects the stock market. This paper proposed new ways to utilize online news articles with technical indicators. The suggested hybrid model consists of three models. First, a self-attention-based convolutional neural network (CNN) model, considered to be better in interpreting the semantics of long texts, uses news content as inputs. Second, a self-attention-based, bi-long short-term memory (bi-LSTM) neural network model for short texts utilizes news titles as inputs. Third, a bi-LSTM model, considered to be better in analyzing context information and time-series models, uses 19 technical indicators as inputs. We used news articles from the previous day and technical indicators from the past seven days to predict the share price of the next day. An experiment was performed with Korean stock market data and news articles from 33 top companies over three years. Through this experiment, our proposed model showed better performance than previous approaches, which have mainly focused on news titles. This paper demonstrated that news titles and content should be treated in different ways for superior stock price prediction.

목차

ABSTRACT
Ⅰ. Introduction
Ⅱ. Background
2.1. Stock Prediction with Technical Indicators
2.2. Stock Prediction with Text Mining
2.3. Stock Prediction with Multiple Sources
2.4. Difference between CNN and LSTM in NLP
2.5. Self-attention Mechanism
Ⅲ. Model
3.1. SelfAttn/CNN
3.2. SelfAtt/LSTM
3.3. bi-LSTM
Ⅳ. Experiment
4.1. Data and Training
4.2. Experiments Design
4.3. Experiments Result
Ⅴ. Market Simulation
5.1. Simulation Strategy
5.2. Simulation Results
Ⅵ. Discussion
6.1. Differences between Titles and Content
6.2. Differences between CNN and LSTM
Ⅶ. Conclusion
Acknowledgement

키워드

저자

• Sang Hyung Jung [ Undergraduate student, Business Administration at the School of Business, Hanyang University, Korea ]
• Gyo Jung Gu [ Undergraduate student, Department of Finance at the School of Business, Hanyang University, Korea ]
• Dongsung Kim [ Postdoctoral researcher, Business Administration at the School of Business, Hanyang University, Korea ]
• Jong Woo Kim [ Professor, School of Business, Hanyang University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  Asia Pacific Journal of Information Systems

• 간기

  계간

• pISSN

  2288-5404

• eISSN

  2288-6818

• 수록기간

  1990~2026

• 등재여부

  KCI 등재,SCOPUS

• 십진분류

  KDC 325 DDC 658