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

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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.

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Funding

The authors gratefully acknowledge financial support from the Humanities and Social Sciences Fund of Ministry of Education (22YJAZH007, 23YJAZH037), Huazhong University of Science and Technology Double First-Class Funds for Humanities and Social Sciences. The computation is completed in the HPC Platform of Huazhong University of Science and Technology.

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Authors and Affiliations

  1. School of Economics, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Shun Chen & Lingling Guo

  2. School of Finance, Southwestern University of Finance and Economics, Chengdu, Sichuan, China

    Lei Ge

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  1. Shun Chen
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  2. Lingling Guo
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  3. Lei Ge
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Correspondence to Lei Ge.

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Chen, S., Guo, L. & Ge, L. Increasing the Hong Kong Stock Market Predictability: A Temporal Convolutional Network Approach. Comput Econ (2024). https://doi.org/10.1007/s10614-024-10547-y

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