Abstract
Today, there is a growing number of digital assets, often built on questionable technical foundations. We design and implement neural networks 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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Acknowledgements
The authors thank Alexis Anastasiou of Pythagoras Systems and Jason Theodorakopoulos for their precious comments and suggestions throughout this work. The first two authors are partially supported by the European Union’s H2020 research and innovation programme under grant agreement No 734242 (LAMBDA). The first author was also partially employed by Pythagoras Systems during this work.
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Christoforou, E., Emiris, I.Z., Florakis, A. (2020). Neural Networks for Cryptocurrency Evaluation and Price Fluctuation Forecasting. In: Pardalos, P., Kotsireas, I., Guo, Y., Knottenbelt, W. (eds) Mathematical Research for Blockchain Economy. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-37110-4_10
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