Using Genetic Algorithm and NARX Neural Network to Forecast Daily Bitcoin Price
Jin-Bom Han (),
Myong-Hun Jang and
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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 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)
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