지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국통계학회 CSAM(Communications for Statistical Applications and Methods) CSAM(Communications for Statistical Applications and Methods) 제28권 제4호
- 발행연도
- 2021.7
- 수록면
- 329 - 338 (10page)
이용수
초록· 키워드
오류제보하기
Deep learning methods have been developed, used in various fields, and they have shown outstanding performances in many cases. Many studies predicted a daily stock return, a classic example of time-series data, using deep learning methods. We also tried to apply deep learning methods to Korea’s stock market data. We used Korea’s stock market index (KOSPI) and several individual stocks to forecast daily returns and directions. We compared several deep learning models with other machine learning methods, including random forest and XGBoost. In regression, long short term memory (LSTM) and gated recurrent unit (GRU) models are better than other prediction models. For the classification applications, there is no clear winner. However, even the best deep learning models cannot predict significantly better than the simple base model. We believe that it is challenging to predict daily stock return data even if we use the latest deep learning methods.
목차
Abstract
1. Introduction
2. Data
3. Method
4. Result
5. Conclusion remarks
References
참고문헌 (0)
참고문헌 신청함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
KOSPI200 Index Prediction Using Sequence-to-Sequence based on Denoising Filter and Attention Mechanism
Quantitative Bio-Science
2020 .11
-
Recent deep learning methods for tabular data
CSAM(Communications for Statistical Applications and Methods)
2023 .03
-
Forecasting the KOSPI 200 Stock Index Based on LSTM Autoencoder
Quantitative Bio-Science
2020 .11
-
딥 러닝과 KSTAR 초전도 자석
물리학과 첨단기술
2022 .04
-
KOSPI200 Prediction through Low-Pass Filtered Long Short-Term Memory Algorithm
Quantitative Bio-Science
2020 .05
-
Application of Machine Learning under Changing Climatic Conditions
한국기상학회 학술대회 논문집
2021 .10
-
The roles of differencing and dimension reduction in machine learning forecasting of employment level using the FRED big data
CSAM(Communications for Statistical Applications and Methods)
2019 .09
-
Comparison of deep-learning- and numerical-based very short-term rainfall forecasts in South Korea
한국기상학회 학술대회 논문집
2023 .04
-
Integration of a deep-learning-based fire model into a global land surface model
한국기상학회 학술대회 논문집
2023 .11
-
Deep learning based marine biogeochemical prediction
한국기상학회 학술대회 논문집
2023 .04
-
COMPARATIVE ANALYSIS ON MACHINE LEARNING MODELS FOR PREDICTING KOSPI200 INDEX RETURNS
순수 및 응용수학
2017 .01
-
Evaluation of deep-learning-based very short-term rainfall forecasts in South Korea
한국기상학회 학술대회 논문집
2022 .10
-
Magnetic parameter estimation using Deep learning techniques
한국자기학회 학술연구발표회 논문개요집
2019 .11
-
Optimal stock investment strategy using prediction models
CSAM(Communications for Statistical Applications and Methods)
2024 .11
-
Density prediction of high energy materials using deep learning method (DNNs)
한국분석과학회 학술대회
2019 .05
-
Density prediction of high energy materials using deep learning method (DNNs)
한국분석과학회 학술대회
2019 .05
-
Design and Verification of Spacecraft Pose Estimation Algorithm using Deep Learning
Journal of Astronomy and Space Sciences
2024 .06
-
베이지안 방법론을 이용한 초미세먼지 오염원별 공간분포 추정 및 딥러닝 기법을 활용한 초미세먼지 내 화학성분 예측 모델 개발
한국대기환경학회 학술대회논문집
2021 .10
-
기계학습의 LSTM을 적용한 지상 기상변수 예측모델 개발
대기
2021 .03
-
수학 학습용 애플리케이션 유형 및 내용 분석
East Asian Mathematical Journal
2017 .01
이 논문의 저자 정보
이 논문과 함께 이용한 논문
최근 본 자료
댓글(0)
0
UCI(KEPA) : I410-ECN-0101-2023-310-001432501