Dependence structure between bitcoin price and its influence factors

Chen, Weili; Zheng, Zibin; Ma, Mingjie; Wu, Jiajing; Zhou, Yuren; Yao, Jiaquan

doi:10.1504/ijcse.2020.106058

Cited by 12 publications

(6 citation statements)

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“…In addition, we used 29 independent variables, classified into demand and supply variables, attractiveness, and macroeconomic and financial variables, as possible predictors of the Bitcoin future price ( Table 1). These variables were used throughout the previous literature [1,3,4,14]. The sample is fragmented into three mutually exclusive parts, one for training (70% of the data), one for validation (10% of the data), and the third group for testing (20% of the data).…”

Section: Data and Variablesmentioning

confidence: 99%

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Deep Learning Methods for Modeling Bitcoin Price

et al. 2020

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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.

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Section: Data and Variablesmentioning

confidence: 99%

“…For Bitcoin, the concept originated from the concept of cryptocurrency, or virtual currency [3]. Cryptocurrencies are a monetary medium that is not affected by public regulation, nor is it subject to a regulatory body.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Methods for Modeling Bitcoin Price

et al. 2020

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“…De su aplicación al ámbito de las criptomonedas se deduce que el precio del criptoactivo está asociado a medidas de volumen tales como número de transacciones diarias y número de direcciones. Y por otra parte, también es interesante considerar la influencia de la velocidad de circulación de la criptomoneda, que tiene una relación inversa con el valor de la misma (Chen et al, 2020). En este contexto, el papel de los denominados holders 1 es muy importante ya que su existencia y capacidad financiera contribuyen a reducir la velocidad de circulación al «atesorar» criptomonedas reduciendo las posibilidades de transmisión de las mismas.…”

Section: Revisión De La Literaturaunclassified

Identificación de factores de influencia en el precio de las criptomonedas: evidencia para bitcoin y ethereum

Fernández

López²,

Gámez

et al. 2020

CICE

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El presente estudio tiene como objetivo el desarrollo de nuevos modelos para determinar con alta precisión los factores que explican el precio de las principales criptomonedas. Para ello, se ha utilizado una amplia base de datos de variables relacionadas con el bitcoin y el ethereum, y se han aplicado técnicas de redes neuronales artificiales. Los resultados obtenidos han permitido identificar que los aspectos relacionados con el número de publicaciones en foros, el volumen de transacciones en blockchain y la tasa de hash proporcionan una excelente estrategia para predecir el precio del bitcoin. También, que el volumen de transacciones, el tamaño de los bloques, las comisiones de mineros y los precios del petróleo son los mejores predictores del valor de mercado del ethereum

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“…Due to the still-small size of the cryptocurrency market, even a tiny investment can have a significant impact on a cryptocurrency's price. Most trading algorithms include various parameters, largely rely on historical data, and fail to handle high volatility [3,4]. There are several methods for them such as ARIMA and LSTM-based on Recurrent Neural Networks (RNNs).…”

Section: Introductionmentioning

confidence: 99%

Empirical Forecasting Analysis of Bitcoin Prices

Tripathy,

Hota,

Mishra

et al. 2024

Int. j. electr. comput. eng. syst. (Online)

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Bitcoin has drawn a lot of interest recently as a possible high-earning investment. There are significant financial risks associated with its erratic price volatility. Therefore, investors and decision-makers place great significance on being able to precisely foresee and capture shifting patterns in the Bitcoin market. However, empirical studies on the systems that support Bitcoin trading and forecasting are still in their infancy. The suggested method will predict the prices of all key cryptocurrencies with accuracy. A number of factors are going to be taken into account in order to precisely predict the pricing. By leveraging encryption technology, cryptocurrencies may serve as an online accounting framework and a medium of exchange. The main goal of this work is to predict Bitcoin price. To address the drawbacks of traditional forecasting techniques, we use a variety of machine learning, deep learning, and ensemble learning algorithms. We conduct a performance analysis of Auto-Regressive Integrated Moving Averages (ARIMA), Long-Short-Term Memory (LSTM), FB-Prophet, XGBoost, and a pair of hybrid formulations, LSTM-GRU and LSTM-1D_CNN. Utilizing historical Bitcoin data from 2012 to 2020, we compared the models with their Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The hybrid LSTM-GRU model outperforms the rest with a Mean Absolute Error (MAE) of 0.464 and a Root Mean Squared Error (RMSE) of 0.323. The finding has significant ramifications for market analysts and investors in digital currencies.

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Dependence structure between bitcoin price and its influence factors

Cited by 12 publications

References 0 publications

Deep Learning Methods for Modeling Bitcoin Price

Deep Learning Methods for Modeling Bitcoin Price

Identificación de factores de influencia en el precio de las criptomonedas: evidencia para bitcoin y ethereum

Empirical Forecasting Analysis of Bitcoin Prices

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