Deep Learning Methods for Modeling Bitcoin Price

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.

Neural Networks for Estimating Speculative Attacks Models

“…An angle is a specification that represents geometrical aspects and is defined such that: . Quantum gates may be applied for adjusting the probabilities because of weight upgrading [ 31 , 37 ]. An example of rotation gate can be: expressed as appears in the expression (25): …”

“…Upgrading of quantum hidden layer weight in quantum backpropagation algorithm, the weights are upgraded by quantum gate conforming to Equation (26), so in this case, the equation would be as it appears in the Equation (32): where , the index i represents the number of outputs from quantum neuron and the index j defines the number of outputs from network, , and is the learning rate [ 36 , 37 ]. This ratio usually takes the value of 0.1.…”

“…, the index i represents the number of outputs from quantum neuron and the index j defines the number of outputs from network, γ ij = γ ij + η∆θ ij , and η is the learning rate [36,37]. This ratio usually takes the value of 0.1.…”

Predictions of bitcoin prices through machine learning based frameworks

“…Let us quote other relevant works. Shintate & Pichl (2019) , Ji, Kim & Im (2019) , Livieris et al (2020) , Lamothe-Fernández et al (2020) , and Chen, Li & Sun (2020) predicted Bitcoin price at different frequencies using several machine learning techniques and investigating the importance of the sample dimension. Greaves & Au (2015) investigated the predictive power of blockchain network-based features on the bitcoin price.…”

Financial Performance Analysis in European Football Clubs

“…To perform multiple-step-ahead prediction to obtain a greater robustness of results, we applied an iterative strategy. For this, we trained the models for the prediction of one step and two forward steps, that is, of the moments t + 1 and t + 2 [22]. These projected data for t + 1 and t + 2 were included in the data sample as actual observations.…”