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Increasing the Hong Kong Stock Market Predictability: A Temporal Convolutional Network Approach

Author

Listed:
  • Shun Chen

    (Huazhong University of Science and Technology)

  • Lingling Guo

    (Huazhong University of Science and Technology)

  • Lei Ge

    (Southwestern University of Finance and Economics)

Abstract

Recently, a substantial body of literature in finance has implemented deep learning algorithms as predicting approaches. The principal merit of these methods is the ability to approximate any nonlinear and linear behaviors without understanding the data generation process, making them suitable for predicting stock market movement. This paper explores deep learning approaches to forecast stock price movement in the Hong Kong stock market. The forecasting performance of a temporal convolutional network (TCN) approach and several recurrent neural network (RNN) models is compared. The results show that the TCN can outperform all compared RNN models. Further parameter tuning results also show the superiority of the TCN approach. In addition, we demonstrate that a profitable strategy can be built based on the forecasting results of the proposed model.

Suggested Citation

  • Shun Chen & Lingling Guo & Lei Ge, 2024. "Increasing the Hong Kong Stock Market Predictability: A Temporal Convolutional Network Approach," Computational Economics, Springer;Society for Computational Economics, vol. 64(5), pages 2853-2878, November.
    • Handle: RePEc:kap:compec:v:64:y:2024:i:5:d:10.1007_s10614-024-10547-y
    DOI: 10.1007/s10614-024-10547-y
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    References listed on IDEAS

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    1. Isil Erel & Léa H Stern & Chenhao Tan & Michael S Weisbach, 2021. "Selecting Directors Using Machine Learning," NBER Chapters, in: Big Data: Long-Term Implications for Financial Markets and Firms, pages 3226-3264, National Bureau of Economic Research, Inc.
    2. Leippold, Markus & Wang, Qian & Zhou, Wenyu, 2022. "Machine learning in the Chinese stock market," Journal of Financial Economics, Elsevier, vol. 145(2), pages 64-82.
    3. Andrea Bucci, 2020. "Realized Volatility Forecasting with Neural Networks," Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 502-531.
    4. Yao, Haixiang & Xia, Shenghao & Liu, Hao, 2022. "Six-factor asset pricing and portfolio investment via deep learning: Evidence from Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 76(C).
    5. Alhashel, Bader S. & Almudhaf, Fahad W. & Hansz, J. Andrew, 2018. "Can technical analysis generate superior returns in securitized property markets? Evidence from East Asia markets," Pacific-Basin Finance Journal, Elsevier, vol. 47(C), pages 92-108.
    6. Eunho Koo & Geonwoo Kim, 2023. "A New Neural Network Approach for Predicting the Volatility of Stock Market," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1665-1679, April.
    7. Magnus Wiese & Robert Knobloch & Ralf Korn & Peter Kretschmer, 2020. "Quant GANs: deep generation of financial time series," Quantitative Finance, Taylor & Francis Journals, vol. 20(9), pages 1419-1440, September.
    8. Germán Creamer, 2012. "Model calibration and automated trading agent for Euro futures," Quantitative Finance, Taylor & Francis Journals, vol. 12(4), pages 531-545, December.
    9. Marija Gorenc Novak & Dejan Velušček, 2016. "Prediction of stock price movement based on daily high prices," Quantitative Finance, Taylor & Francis Journals, vol. 16(5), pages 793-826, May.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Temporal convolutional network; Deep learning; Stock prediction; Trading strategies;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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