Forecasting cryptocurrencies’ price with the financial stress index: a graph neural network prediction strategy

Wei Yin, Ziling Chen, Xinxin Luo and Berna Kirkulak-Uludag

Applied Economics Letters, 2024, vol. 31, issue 7, 630-639

Abstract: This article proposes a graph neural network strategy (GNN), in which the long short-term memory (LSTM) and graph convolution network (GCN) are applied to capture both temporal and spatial features to forecast the price of Bitcoin, Litecoin, Ethereum, and Dash Coin with the ‘stable-coin’ Tether (USDT) and financial stress index (FSI). The main results show that the GNN strategy has better performance than univariate LSTM and multivariate LSTM in all of the seven steps forward forecasting. A sensitivity check shows that USDT and FSI/sub-FSI are important factors in the construction of the graphs and they verify the validity of the results.

Date: 2024
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DOI: 10.1080/13504851.2022.2141436

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