Using Genetic Algorithm and NARX Neural Network to Forecast Daily Bitcoin Price

Jin-Bom Han (), Sun-Hak Kim, Myong-Hun Jang and Kum-Sun Ri
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Jin-Bom Han: Kim Il Sung University
Sun-Hak Kim: Kim Il Sung University
Myong-Hun Jang: Kim Il Sung University
Kum-Sun Ri: Kim Il Sung University

Computational Economics, 2020, vol. 56, issue 2, No 3, 337-353

Abstract: Abstract The main purpose of this paper is to suggest daily bitcoin return model using a genetic algorithm and NARX neural network. We found that the genetic algorithm is effective to decide the architecture of the NARX neural network than information criteria-Akaike information criterion and the Schwarz information criterion using a Monte Carlo simulation and a hypothesis test. Finally, we forecasted daily bitcoin geometric return using this hybrid model of the genetic algorithm and NARX neural network and compare it with a feed-forward neural network forecasting model through a hypothesis test.

Keywords: Genetic algorithm; Nonlinear autoregressive with exogenous inputs (NARX) neural network; Bitcoin price forecasting; Daily average Bitcoin price; Recurrent neural network (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s10614-019-09928-5

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