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

Dawei Shang, Ziyu Guo and Hui Wang

Finance Research Letters, 2024, vol. 67, issue PB

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.

Keywords: Improved convolutional neural network; Attention-based allocation function; Cryptocurrency price; Interpretable machine learning approach; Times series (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:67:y:2024:i:pb:s1544612324008766

DOI: 10.1016/j.frl.2024.105846

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