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-430, CARF F-Series from Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo

Abstract: This paper explores Bitcoin 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 of Bitcoin returns 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.

Pages: 23 pages
Date: 2018-03
New Economics Papers: this item is included in nep-big, nep-cmp and nep-pay
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Citations: View citations in EconPapers (44)

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Persistent link: https://EconPapers.repec.org/RePEc:cfi:fseres:cf430

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