Cryptocurrencies as a financial asset: A systematic analysis

Corbet, Shaen; Lucey, Brian M.; Urquhart, Andrew; Yarovaya, Larisa

doi:10.1016/j.irfa.2018.09.003

Cited by 798 publications

(311 citation statements)

References 109 publications

(65 reference statements)

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“…This section briefly reviews previous studies on Bitcoin price prediction using deep learning. For other statistical analysis, we refer the reader to the works by the authors of [3,4] and references therein. McNally et al [5] proposed two prediction models based on recurrent neural networks (RNNs) and long short-term memory (LSTM), and compared them with an autoregressive integrated moving average (ARIMA) model [19], which is a traditionally widely used time series forecasting model.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Bitcoin Price Prediction Using Deep Learning

Kim

2019

Mathematics

146

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Bitcoin has recently received a lot of attention from the media and the public due to its recent price surge and crash. Correspondingly, many researchers have investigated various factors that affect the Bitcoin price and the patterns behind its fluctuations, in particular, using various machine learning methods. In this paper, we study and compare various state-of-the-art deep learning methods such as a deep neural network (DNN), a long short-term memory (LSTM) model, a convolutional neural network, a deep residual network, and their combinations for Bitcoin price prediction. Experimental results showed that although LSTM-based prediction models slightly outperformed the other prediction models for Bitcoin price prediction (regression), DNN-based models performed the best for price ups and downs prediction (classification). In addition, a simple profitability analysis showed that classification models were more effective than regression models for algorithmic trading. Overall, the performances of the proposed deep learning-based prediction models were comparable.

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Section: Related Workmentioning

confidence: 99%

A Comparative Study of Bitcoin Price Prediction Using Deep Learning

Kim

2019

Mathematics

146

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“…The latter invalidates the view on cryptocurrencies as substitutes to monies, or as a store of value (like gold), and rather stresses they are assets of their own class. The review of the literature in Corbet et al (2019) summarizes the most interesting findings on the role of cryptocurrencies as a credible investment asset class and as a valuable and legitimate payment system.…”

Section: Cryptocurrencies and Neoclassical Financementioning

confidence: 99%

Cryptocurrencies: market analysis and perspectives

2019

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The papers in this special issue focus on the emerging phenomenon of cryptocurrencies. Cryptocurrencies are digital financial assets, for which ownership and transfers of ownership are guaranteed by a cryptographic decentralized technology. The rise of cryptocurrencies' value on the market and the growing popularity around the world open a number of challenges and concerns for business and industrial economics. Using the lenses of both neoclassical and behavioral theories, this introductory article discusses the main trends in the academic research related to cryptocurrencies and highlights the contributions of the selected works to the literature. A particular emphasis is on socio-economic, misconduct and sustainability issues. We posit that cryptocurrencies may perform some useful functions and add economic value, but there are reasons to favor the regulation of the market. While this would go against the original libertarian rationale behind cryptocurrencies, it appears a necessary step to improve social welfare.

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“…Some studies consider risk, hedge and safe haven attributes of Bitcoin and Ethereum (Beneki et al 2019, Bouri et al 2017. However, to the best of the authors' knowledge, there are no studies on the forecasting of test-sample (out-sample) Bitcoin price (Corbet et al 2019). Thus, this study presents a novel approach to forecasting daily Bitcoin price using both with and without model re-estimation at each step while comparing ARIMA and NNAR models.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, Bitcoin is coming into the mainstream with large institutional investors eyeing its potential. Despite its limitations, Bitcoin is the most valuable and popular cryptocurrency to date (Corbet et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Next-Day Bitcoin Price Forecast

Munim

Shakil

Alon

2019

JRFM

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This study analyzes forecasts of Bitcoin price using the autoregressive integrated moving average (ARIMA) and neural network autoregression (NNAR) models. Employing the static forecast approach, we forecast next-day Bitcoin price both with and without re-estimation of the forecast model for each step. For cross-validation of forecast results, we consider two different training and test samples. In the first training-sample, NNAR performs better than ARIMA, while ARIMA outperforms NNAR in the second training-sample. Additionally, ARIMA with model re-estimation at each step outperforms NNAR in the two test-sample forecast periods. The Diebold Mariano test confirms the superiority of forecast results of ARIMA model over NNAR in the test-sample periods. Forecast performance of ARIMA models with and without re-estimation are identical for the estimated test-sample periods. Despite the sophistication of NNAR, this paper demonstrates ARIMA enduring power of volatile Bitcoin price prediction.

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Cryptocurrencies as a financial asset: A systematic analysis

Cited by 798 publications

References 109 publications

A Comparative Study of Bitcoin Price Prediction Using Deep Learning

A Comparative Study of Bitcoin Price Prediction Using Deep Learning

Cryptocurrencies: market analysis and perspectives

Next-Day Bitcoin Price Forecast

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