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Sustainability
Volume 13
Issue 21
10.3390/su132111822
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Article

Prediction of the Change Points in Stock Markets Using DAE-LSTM

by
Sanghyuk Yoo
1,
Sangyong Jeon
2,
Seunghwan Jeong
1,
Heesoo Lee
3,
Hosun Ryou
1,
Taehyun Park
1,
Yeonji Choi
1 and
Kyongjoo Oh
1,*
1
Department of Industrial Engineering, Yonsei University, Seoul 03722, Korea
2
Department of Investment Information Engineering, Yonsei University, Seoul 03722, Korea
3
Department of Business Administration, Sejong University, Seoul 05006, Korea
*
Author to whom correspondence should be addressed.
Sustainability 2021, 13(21), 11822; https://doi.org/10.3390/su132111822
Submission received: 26 September 2021 / Revised: 22 October 2021 / Accepted: 24 October 2021 / Published: 26 October 2021
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Abstract

Since the creation of stock markets, there have been attempts to predict their movements, and new prediction methodologies have been devised. According to a recent study, when the Russell 2000 industry index starts to rise, stocks belonging to the corresponding industry in other countries also rise accordingly. Based on this empirical result, this study seeks to predict the start date of industry uptrends using the Russell 2000 industry index. The proposed model in this study predicts future stock prices using a denoising autoencoder (DAE) long short-term memory (LSTM) model and predicts the existence and timing of future change points in stock prices through Pettitt’s test. The results of the empirical analysis confirmed that this proposed model can find the change points in stock prices within 7 days prior to the start date of actual uptrends in selected industries. This study contributes to predicting a change point through a combination of statistical and deep learning models, and the methodology developed in this study could be applied to various financial time series data for various purposes.
Keywords: change-point detection; denoising autoencoder; long short-term memory; Russell 2000 index change-point detection; denoising autoencoder; long short-term memory; Russell 2000 index

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MDPI and ACS Style

Yoo, S.; Jeon, S.; Jeong, S.; Lee, H.; Ryou, H.; Park, T.; Choi, Y.; Oh, K. Prediction of the Change Points in Stock Markets Using DAE-LSTM. Sustainability 2021, 13, 11822. https://doi.org/10.3390/su132111822

AMA Style

Yoo S, Jeon S, Jeong S, Lee H, Ryou H, Park T, Choi Y, Oh K. Prediction of the Change Points in Stock Markets Using DAE-LSTM. Sustainability. 2021; 13(21):11822. https://doi.org/10.3390/su132111822

Chicago/Turabian Style

Yoo, Sanghyuk, Sangyong Jeon, Seunghwan Jeong, Heesoo Lee, Hosun Ryou, Taehyun Park, Yeonji Choi, and Kyongjoo Oh. 2021. "Prediction of the Change Points in Stock Markets Using DAE-LSTM" Sustainability 13, no. 21: 11822. https://doi.org/10.3390/su132111822

APA Style

Yoo, S., Jeon, S., Jeong, S., Lee, H., Ryou, H., Park, T., Choi, Y., & Oh, K. (2021). Prediction of the Change Points in Stock Markets Using DAE-LSTM. Sustainability, 13(21), 11822. https://doi.org/10.3390/su132111822

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