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.
Highlights
Bitcoin is a cryptocurrency built by free software based on peer-to-peer networks as an irreversible private payment platform
Ji and co-workers [15] predicted the price of Bitcoin with different methodologies such as deep neural network (DNN), the long short-term memory (LSTM) model, and convolutional neural network
Deep neural decision trees are decision tree (DT) models performed by deep learning neural networks, where a weight division corresponding to the Deep Neural Decision Trees (DNDT) belongs to a specific decision tree and, it is possible to interpret its information [21]
Summary
Bitcoin is a cryptocurrency built by free software based on peer-to-peer networks as an irreversible private payment platform. Ji and co-workers [15] predicted the price of Bitcoin with different methodologies such as deep neural network (DNN), the LSTM model, and convolutional neural network They obtained a precision of 60%, leaving the improvement of precision with deep learning techniques and a greater definition of significant variables as a future line of research. To contribute to the robustness of the Bitcoin price prediction models, in the present study a comparison of deep learning methodologies to predict and model the Bitcoin price is developed and, as a consequence, a new model that generates better forecasts of the Bitcoin price and its behavior in the future This model can predict achieving accuracy levels above 95%.
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