Bitcoin Technical Trading with Articial Neural Network

Masafumi Nakano, Akihiko Takahashi and Soichiro Takahashi
Additional contact information
Masafumi Nakano: Graduate School of Economics, Faculty of Economics, The University of Tokyo
Akihiko Takahashi: Faculty of Economics, The University of Tokyo
Soichiro Takahashi: Graduate School of Economics, Faculty of Economics, The University of Tokyo

No CIRJE-F-1090, CIRJE F-Series from CIRJE, Faculty of Economics, 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 pages
Date: 2018-07
New Economics Papers: this item is included in nep-big, nep-cmp and nep-pay
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (29)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tky:fseres:2018cf1090

Access Statistics for this paper

More papers in CIRJE F-Series from CIRJE, Faculty of Economics, University of Tokyo Contact information at EDIRC.
Bibliographic data for series maintained by CIRJE administrative office ().