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Neural networks for cryptocurrency evaluation and price fluctuation forecasting

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Today, there is a growing number of digital assets, often built on questionable technical foundations. We design and implement supervized learning models in order to explore different aspects of a cryptocurrency affecting its performance, its stability as well as its daily price fluctuation. One characteristic feature of our approach is that we aim at a holistic view that would integrate all available information: First, financial information, including market capitalization and historical daily prices. Second, features related to the underlying blockchain from blockchain explorers like network activity: blockchains handle the supply and demand of a cryptocurrency. Lastly, we integrate software development metrics based on GitHub activity by the supporting team. We set two goals. First, to classify a given cryptocurrency by its performance, where stability and price increase are the positive features. Second, to forecast daily price tendency through regression; this is of course a well-studied problem. A related third goal is to determine the most relevant features for such analysis. We compare various neural networks using most of the widely traded digital currencies (e.g. Bitcoin, Ethereum and Litecoin) in both classification and regression settings. Simple Feedforward neural networks are considered, as well as Recurrent neural networks (RNN) along with their improvements, namely Long Short-Term Memory and Gated Recurrent Units. The results of our comparative analysis indicate that RNNs provide the most promising results.
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hal-03116041 , version 1 (20-01-2021)


  • HAL Id : hal-03116041 , version 1


Ioannis Z. Emiris, Emmanouil Christoforou, Apostolos Florakis. Neural networks for cryptocurrency evaluation and price fluctuation forecasting. MARBLE 2019 - The 1st International Conference on Mathematical Research for Blockchain Economy, May 2019, Santorini, Greece. ⟨hal-03116041⟩


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