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
- 발행연도
- 2020.11
- 수록면
- 127 - 135 (9page)
이용수
초록· 키워드
오류제보하기
In the financial market, many studies have been conducted to forecast stock prices, but there are limitations in accurately predicting them due to the high volatility of price and high noise. In addition, in prediction, traditional time series analysis techniques have been widely used, but there are methodological limitations. In this study, the noise of the stock price data is removed by applying a denoising filter, and the prediction performance is then improved using the deep learning long short-term memory (LSTM) model. In addition, as the length of time series data increases, we apply an attention mechanism to predict the KOSPI200 index to minimize the loss of information in the deep learning LSTM model. Four deep learning prediction models were conducted using daily and 30-minute data of the KOSPI200 index according to whether denoising filter and attention mechanisms were applied, and the learning performance was compared and analyzed. As a result, the deep learning LSTM model with both denoising filter and the attention mechanism showed smaller error between the actual and predicted values.
목차
ABSTRACT
1. Introduction
2. Proposed Model
3. Empirical Study
4. Conclusion
References
참고문헌 (15)
참고문헌 신청
박종학 / 2017 / An Investment Strategy Using Genetic Algorithm to Maximize Downside Risk-Adjusted Returns for Long-Term Investors / Quantitative Bio-Science 36(2):111~117
고한준 / 2019 / Segmentation Strategy for Asset Groups in the Korean Exchange-Traded Funds Market Using Genetic Algorithm / Quantitative Bio-Science 38(2):87~92
황선영 / 2018 / Volatility for High Frequency Time Series Toward fGARCH (1,1) as a Functional Model / Quantitative Bio-Science 37(2):73~79
Long W / 2018 / Deep learning-based feature engineering for stock price movement prediction / Knowl-Based Syst 164:163~173
김성수 / 2017 / 순환 신경망 기술을 이용한 코스피 200 지수에 대한 예측 모델 개발 및 성능 분석 연구 / 한국산업정보학회논문지 22(6):23~29
고한준 / 2019 / Segmentation Strategy for Asset Groups in the Korean Exchange-Traded Funds Market Using Genetic Algorithm / Quantitative Bio-Science 38(2):87~92
황선영 / 2018 / Volatility for High Frequency Time Series Toward fGARCH (1,1) as a Functional Model / Quantitative Bio-Science 37(2):73~79
Long W / 2018 / Deep learning-based feature engineering for stock price movement prediction / Knowl-Based Syst 164:163~173
김성수 / 2017 / 순환 신경망 기술을 이용한 코스피 200 지수에 대한 예측 모델 개발 및 성능 분석 연구 / 한국산업정보학회논문지 22(6):23~29
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UCI(KEPA) : I410-ECN-0101-2021-047-000077811