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Enhancing digital cryptocurrency trading price prediction with an attention-based convolutional and recurrent neural network approach: The case of Ethereum

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  • Shang, Dawei
  • Guo, Ziyu
  • Wang, Hui

Abstract

To predict Ethereum price fluctuations, this study proposes a new two-stage Machine Learning approach using an improved convolutional neural network and a recurrent neural network framework, integrating an attention mechanism-based distribution function algorithm. We construct a dataset and perform model training, fitting, and forecasting. The results indicate that compared with traditional neural networks and time-series models such as GRU and ARIMA, respectively, this approach can effectively use the data information of digital cryptocurrency and improve the prediction accuracy and interpretability of attention-based allocation functions. This study contributes to the literature by offering a new approach for stakeholders.

Suggested Citation

  • Shang, Dawei & Guo, Ziyu & Wang, Hui, 2024. "Enhancing digital cryptocurrency trading price prediction with an attention-based convolutional and recurrent neural network approach: The case of Ethereum," Finance Research Letters, Elsevier, vol. 67(PB).
    • Handle: RePEc:eee:finlet:v:67:y:2024:i:pb:s1544612324008766
    DOI: 10.1016/j.frl.2024.105846
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