Predicting stock price crash risk in China: A modified graph WaveNet model

Zhongbo Jing, Qin Li, Hongyi Zhao and Yang Zhao

Finance Research Letters, 2024, vol. 64, issue C

Abstract: The stock price of a firm is dynamically influenced by its own factors as well as those of its peers. In this study, we introduce a Graph Attention Network (GAT) integrated with WaveNet architecture—termed the GAT-WaveNet model—to capture both time-series and spatial dependencies for forecasting the stock price crash risk of Chinese listed firms from 2012 to 2023. Utilizing node-rolling techniques to prevent overfitting, our results show that the GAT-WaveNet model significantly outperforms traditional machine learning models in prediction accuracy. Moreover, investment portfolios leveraging the GAT-WaveNet model substantially exceed the cumulative returns of those based on other models.

Keywords: Stock price crash risk; Graph neural networks; Graph attention networks; Machine learning (search for similar items in EconPapers)
JEL-codes: C52 C55 G11 G17 G32 M41 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:64:y:2024:i:c:s1544612324004987

DOI: 10.1016/j.frl.2024.105468

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