Deep Learning Methods for Modeling Bitcoin Price

Prosper Lamothe-Fernández, David Alaminos, Prosper Lamothe-López and Manuel A. Fernández-Gámez
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Prosper Lamothe-Fernández: Department of Financing and Commercial Research, UDI of Financing, Calle Francisco Tomás y Valiente, 5, Universidad Autónoma de Madrid, 28049 Madrid, Spain
David Alaminos: Department of Economic Theory and Economic History, Campus El Ejido s/n, University of Malaga, 29071 Malaga, Spain
Prosper Lamothe-López: Rho Finanzas Partner, Calle de Zorrilla, 21, 28014 Madrid, Spain
Manuel A. Fernández-Gámez: Department of Finance and Accounting, Campus El Ejido s/n, University of Malaga, 29071 Malaga, Spain

Mathematics, 2020, vol. 8, issue 8, 1-13

Abstract: A precise prediction of Bitcoin price is an important aspect of digital financial markets because it improves the valuation of an asset belonging to a decentralized control market. Numerous studies have studied the accuracy of models from a set of factors. Hence, previous literature shows how models for the prediction of Bitcoin suffer from poor performance capacity and, therefore, more progress is needed on predictive models, and they do not select the most significant variables. This paper presents a comparison of deep learning methodologies for forecasting Bitcoin price and, therefore, a new prediction model with the ability to estimate accurately. A sample of 29 initial factors was used, which has made possible the application of explanatory factors of different aspects related to the formation of the price of Bitcoin. To the sample under study, different methods have been applied to achieve a robust model, namely, deep recurrent convolutional neural networks, which have shown the importance of transaction costs and difficulty in Bitcoin price, among others. Our results have a great potential impact on the adequacy of asset pricing against the uncertainties derived from digital currencies, providing tools that help to achieve stability in cryptocurrency markets. Our models offer high and stable success results for a future prediction horizon, something useful for asset valuation of cryptocurrencies like Bitcoin.

Keywords: bitcoin; deep learning; deep recurrent convolutional neural networks; forecasting; asset pricing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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