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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Sanghyuk Yoo & Sangyong Jeon & Seunghwan Jeong & Heesoo Lee & Hosun Ryou & Taehyun Park & Yeonji Choi & Kyongjoo Oh, 2021. "Prediction of the Change Points in Stock Markets Using DAE-LSTM," Sustainability, MDPI, vol. 13(21), pages 1-15, October.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:11822-:d:665026
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    References listed on IDEAS

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    1. Petajisto, Antti, 2011. "The index premium and its hidden cost for index funds," Journal of Empirical Finance, Elsevier, vol. 18(2), pages 271-288, March.
    2. Cao, Jian & Li, Zhi & Li, Jian, 2019. "Financial time series forecasting model based on CEEMDAN and LSTM," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 127-139.
    3. Deepak Gupta & Mahardhika Pratama & Zhenyuan Ma & Jun Li & Mukesh Prasad, 2019. "Financial time series forecasting using twin support vector regression," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-27, March.
    4. A. N. Pettitt, 1979. "A Non‐Parametric Approach to the Change‐Point Problem," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 28(2), pages 126-135, June.
    5. Peng Liu & Peijun Zheng & Ziyu Chen, 2019. "Deep Learning with Stacked Denoising Auto-Encoder for Short-Term Electric Load Forecasting," Energies, MDPI, vol. 12(12), pages 1-15, June.
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    Cited by:

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