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Neural Networks for Cryptocurrency Evaluation and Price Fluctuation Forecasting

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Mathematical Research for Blockchain Economy


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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  1. 1.,,

  2. 2.,


  1. Abadi, M., Barham, P., Chen, J., Chen, Z., et al.: Tensorflow: a system for large-scale machine learning. In: Proceedings of the USENIX Symposium on Operating Systems Design & Implement, pp. 265–283 (2016)

    Google Scholar 

  2. Ariyo, A., Adewumi, A., Ayo, C.: Stock price prediction using the ARIMA model. In: Proceedings of the UKSim-AMSS International Conference on Computer Modelling and Simulation, pp. 106–112 (2014)

    Google Scholar 

  3. Ba, J., Kiros, R., Hinton, G.E.: Layer normalization. Tech. Rep. 1607, arXiv (2016)

    Google Scholar 

  4. Calès, L., Chalkis, A., Emiris, I.Z., Fisikopoulos, V.: Practical volume computation of structured convex bodies, and an application to modeling portfolio dependencies and financial crises. In: Proceedings of the International Symposium on Computational Geometry, Budapest, pp. 19:1–19:15 (2018)

    Google Scholar 

  5. Chatzis, S.P., Siakoulis, V., Petropoulos, A., Stavroulakis, E., Vlachogiannakis, N.: Forecasting stock market crisis events using deep and statistical machine learning techniques. Expert. Syst. Appl. 112, 353–371 (2018)

    Article  Google Scholar 

  6. Cho, K., van Merrinboer, B., Gulcehre, C., Bougares, F., et al.: Learning phrase representations using rnn encoder-decoder for statistical machine translation (2014).

  7. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling (2014)

    Google Scholar 

  8. Hileman, G., Rauchs, M.: 2017 global cryptocurrency benchmarking study. SSRN Electron. J. (2017)

    Google Scholar 

  9. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997).

    Article  Google Scholar 

  10. Jang, H., Lee, J.: An empirical study on modeling and prediction of bitcoin prices with bayesian neural networks based on blockchain information. IEEE Access 6, 5427–5437 (2018)

    Article  Google Scholar 

  11. Jiang, Z., Liang, J.: Cryptocurrency portfolio management with deep reinforcement learning (2017).

    Article  Google Scholar 

  12. Kim, Y.B., Lee, J., Park, N., Choo, J., et al.: When Bitcoin encounters information in an online forum: using text mining to analyse user opinions and predict value fluctuation. PLOS One 12, e0177630 (2017)

    Article  Google Scholar 

  13. Kristjanpoller, W., Minutolo, M.C.: A hybrid volatility forecasting framework integrating GARCH, artificial neural network, technical analysis and principal components analysis. Expert. Syst. Appl. 109, 1–11 (2018)

    Article  Google Scholar 

  14. Lahmiri, S., Bekiros, S.: Cryptocurrency forecasting with deep learning chaotic neural networks. Chaos Solitons Fractals 118, 35–40 (2019)

    Article  Google Scholar 

  15. McNally, S., Roche, J., Caton, S.: Predicting the price of bitcoin using machine learning. In: Euromicro International Conference on Parallel, Distributed & Network-Based Processing (PDP), pp. 339–343 (2018)

    Google Scholar 

  16. Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system. Cryptography Mailing list at (2009)

  17. Nakano, M., Takahashi, A., Takahashi, S.: Bitcoin technical trading with artificial neural network. Phys. A Stat. Mech. Appl. 510, 587–609 (2018)

    Article  Google Scholar 

  18. Saad, M., Mohaisen, A.: Towards characterizing blockchain-based cryptocurrencies for highly-accurate predictions. In: Proceedings of the IEEE Conference on Computer Communications (Infocom) Workshops, pp. 704–709 (2018)

    Google Scholar 

  19. Schuster, M., Paliwal, K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45, 2673–2681 (1997).

  20. Semeniuta, S., Severyn, A., Barth, E.: Recurrent dropout without memory loss. In: Proceedings of the COLING International Conference on Computational Linguistics, pp. 1757–1766 (2016)

    Google Scholar 

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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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Correspondence to Emmanouil Christoforou .

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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.

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