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Transformer-based approach for Ethereum Price Prediction Using Crosscurrency correlation and Sentiment Analysis

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  • Shubham Singh
  • Mayur Bhat

Abstract

The research delves into the capabilities of a transformer-based neural network for Ethereum cryptocurrency price forecasting. The experiment runs around the hypothesis that cryptocurrency prices are strongly correlated with other cryptocurrencies and the sentiments around the cryptocurrency. The model employs a transformer architecture for several setups from single-feature scenarios to complex configurations incorporating volume, sentiment, and correlated cryptocurrency prices. Despite a smaller dataset and less complex architecture, the transformer model surpasses ANN and MLP counterparts on some parameters. The conclusion presents a hypothesis on the illusion of causality in cryptocurrency price movements driven by sentiments.

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  • Shubham Singh & Mayur Bhat, 2024. "Transformer-based approach for Ethereum Price Prediction Using Crosscurrency correlation and Sentiment Analysis," Papers 2401.08077, arXiv.org.
    • Handle: RePEc:arx:papers:2401.08077
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    File URL: http://arxiv.org/pdf/2401.08077
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    References listed on IDEAS

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    1. Dimitrios Koutmos, 2023. "Investor sentiment and bitcoin prices," Review of Quantitative Finance and Accounting, Springer, vol. 60(1), pages 1-29, January.
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