Stock market prediction with deep learning: The case of China

Qingfu Liu, Zhenyi Tao, Yiuman Tse and Chuanjie Wang

Finance Research Letters, 2022, vol. 46, issue PA

Abstract: We consider stock price charts as images and use deep learning neural networks (DLNNs) for image modeling. DLNNs can imitate the work of a technical analyst to predict stock price movements in the short term with price charts and stock fundamentals (e.g., price-to-earnings ratio). We find that a deep learning model performs better than a single-layer model in the prediction of the Chinese stock market. DLNNs provide customizable statistical tools for analyzing price charts effectively. More importantly, price trends established by different periods of past daily closing prices dominate stock fundamentals in predicting future price movements.

Keywords: Deep learning; Stock market prediction; Trend analysis (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:46:y:2022:i:pa:s1544612321002762

DOI: 10.1016/j.frl.2021.102209

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