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

Shun Chen (), Lingling Guo () and Lei Ge ()
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Shun Chen: Huazhong University of Science and Technology
Lingling Guo: Huazhong University of Science and Technology
Lei Ge: Southwestern University of Finance and Economics

Computational Economics, 2024, vol. 64, issue 5, No 11, 2853-2878

Abstract: 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.

Keywords: Temporal convolutional network; Deep learning; Stock prediction; Trading strategies (search for similar items in EconPapers)
JEL-codes: C45 C53 G17 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10614-024-10547-y

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