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

Sanghyuk Yoo, Sangyong Jeon, Seunghwan Jeong, Heesoo Lee, Hosun Ryou, Taehyun Park, Yeonji Choi and Kyongjoo Oh
Additional contact information
Sanghyuk Yoo: Department of Industrial Engineering, Yonsei University, Seoul 03722, Korea
Sangyong Jeon: Department of Investment Information Engineering, Yonsei University, Seoul 03722, Korea
Seunghwan Jeong: Department of Industrial Engineering, Yonsei University, Seoul 03722, Korea
Heesoo Lee: Department of Business Administration, Sejong University, Seoul 05006, Korea
Hosun Ryou: Department of Industrial Engineering, Yonsei University, Seoul 03722, Korea
Taehyun Park: Department of Industrial Engineering, Yonsei University, Seoul 03722, Korea
Yeonji Choi: Department of Industrial Engineering, Yonsei University, Seoul 03722, Korea
Kyongjoo Oh: Department of Industrial Engineering, Yonsei University, Seoul 03722, Korea

Sustainability, 2021, vol. 13, issue 21, 1-15

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 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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