Bitcoin technical trading with artificial neural network

Masafumi Nakano, Akihiko Takahashi and Soichiro Takahashi
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Masafumi Nakano: Graduate School of Economics, University of Tokyo
Akihiko Takahashi: Graduate School of Economics, University of Tokyo
Soichiro Takahashi: Graduate School of Economics, University of Tokyo

No CARF-F-441, CARF F-Series from Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo

Abstract: This paper explores Bitcoin intraday technical trading based on artificial neural networks for the return prediction. In particular, our deep learning method successfully discovers trading signals through a seven layered neural network structure for given input data of technical indicators, which are calculated by the past time-series data over every 15 minutes. Under feasible settings of execution costs, the numerical experiments demonstrate that our approach significantly improves the performance of a buy-and-hold strategy. Especially, our model performs well for a challenging period from December 2017 to January 2018, during which Bitcoin suffers from substantial minus returns. Furthermore, various sensitivity analysis is implemented for the change of the number of layers, activation functions, input data and output classification to confirm the robustness of our approach.

Pages: 30
Date: 2018-07
New Economics Papers: this item is included in nep-big, nep-cmp and nep-pay
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Persistent link: https://EconPapers.repec.org/RePEc:cfi:fseres:cf441

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