Modeling Bitcoin Prices using Signal Processing Methods, Bayesian Optimization, and Deep Neural Networks

Tripathi, Bhaskar; Sharma, Rakesh Kumar

doi:10.1007/s10614-022-10325-8

Cited by 23 publications

(12 citation statements)

References 45 publications

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“…In recent years, many scholars directly studied on prediction of BTC (Adcock and Gradojevic, 2019; Atsalakis et al ., 2019; Pabuccu et al ., 2020; Hau et al ., 2021; Tripathi and Sharma, 2023; Wang and Hausken, 2022). Huang and Gao (2022) used least absolute shrinkage and selection operator (LASSO) approach to predict BTC returns from 2018 to 2019.…”

Section: Literature Reviewmentioning

confidence: 99%

Do crude oil, gold and the US dollar contribute to Bitcoin investment decisions? An ANN-DCC-GARCH approach

Liu,

Naktnasukanjn,

Tamprasirt

et al. 2024

AJEB

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PurposeBitcoin (BTC) is significantly correlated with global financial assets such as crude oil, gold and the US dollar. BTC and global financial assets have become more closely related, particularly since the outbreak of the COVID-19 pandemic. The purpose of this paper is to formulate BTC investment decisions with the aid of global financial assets.Design/methodology/approachThis study suggests a more accurate prediction model for BTC trading by combining the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model with the artificial neural network (ANN). The DCC-GARCH model offers significant input information, including dynamic correlation and volatility, to the ANN. To analyze the data effectively, the study divides it into two periods: before and during the COVID-19 outbreak. Each period is then further divided into a training set and a prediction set.FindingsThe empirical results show that BTC and gold have the highest positive correlation compared with crude oil and the USD, while BTC and the USD have a dynamic and negative correlation. More importantly, the ANN-DCC-GARCH model had a cumulative return of 318% before the outbreak of the COVID-19 pandemic and can decrease loss by 50% during the COVID-19 pandemic. Moreover, the risk-averse can turn a loss into a profit of about 20% in 2022.Originality/valueThe empirical analysis provides technical support and decision-making reference for investors and financial institutions to make investment decisions on BTC.

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Section: Literature Reviewmentioning

confidence: 99%

Do crude oil, gold and the US dollar contribute to Bitcoin investment decisions? An ANN-DCC-GARCH approach

Liu,

Naktnasukanjn,

Tamprasirt

et al. 2024

AJEB

View full text Add to dashboard Cite

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“…According to Tripathi and Sharma (2022), DNNs demonstrate superior performance compared with LSTM and CNN-LSTM models when predicting BTC prices using technical indicators as model inputs. In addition, Omar et al (2022) employed a DNN to process denoised inputs, and the proposed model outperformed traditional statistical methods such as ARIMA.…”

Section: Deep Neural Network (Dnns)mentioning

confidence: 99%

Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022

Zhang,

Sjarif,

Ibrahim

2023

WIREs Data Min & Knowl

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Accurately predicting the prices of financial time series is essential and challenging for the financial sector. Owing to recent advancements in deep learning techniques, deep learning models are gradually replacing traditional statistical and machine learning models as the first choice for price forecasting tasks. This shift in model selection has led to a notable rise in research related to applying deep learning models to price forecasting, resulting in a rapid accumulation of new knowledge. Therefore, we conducted a literature review of relevant studies over the past 3 years with a view to aiding researchers and practitioners in the field. This review delves deeply into deep learning‐based forecasting models, presenting information on model architectures, practical applications, and their respective advantages and disadvantages. In particular, detailed information is provided on advanced models for price forecasting, such as Transformers, generative adversarial networks (GANs), graph neural networks (GNNs), and deep quantum neural networks (DQNNs). The present contribution also includes potential directions for future research, such as examining the effectiveness of deep learning models with complex structures for price forecasting, extending from point prediction to interval prediction using deep learning models, scrutinizing the reliability and validity of decomposition ensembles, and exploring the influence of data volume on model performance.This article is categorized under: Technologies > Prediction Technologies > Artificial Intelligence

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“…The findings have indicated that the methodology is put into use to forecast more accurately [4]. However, because of the erratic price swings of bitcoin and the constantly shifting structure of the market, traders as well as investors are exposed to dangers that could result in significant monetary losses, making it challenging for scholars to innovative methods for price prediction that increase accuracy [5].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Bitcoin Closing Price by Four Machine Learning Algorithms

Zhang

2024

HBEM

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Bitcoin has increased in popularity as a speculative asset. Since 2013, eventually becoming the most recognizable cryptocurrency. But it's worth noting that the price of Bitcoin has a very high degree of volatility and diversity, which means the ability to estimate prices accurately is crucial for making wise financial decisions. Although recent research has implemented machine learning to predict Bitcoin prices with greater precision, such as Long short-term memory (LSTM), few have focused on traditional machine learning methods. In this article, the author chose a data set including nearly eight years of daily bitcoin price data for closing price prediction. Four different machine learning algorithms were used simultaneously: the Linear Regression (LR), the Decision Tree (DT) and the Random Forest (RF). An artificial neural network, the Multilayer Perceptron (MLP) was also used in this study. The author altered parameter values using the cross-validation method before creating the models in order to get more precise predictions. Finally, Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R-squared are used as indicators to assess the outcomes from each model. The study's findings demonstrated that all three metrics of Linear Regression outperformed the performance of the other three models. Perhaps future research could focus more on traditional machine learning algorithms instead of going after complex models.

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Modeling Bitcoin Prices using Signal Processing Methods, Bayesian Optimization, and Deep Neural Networks

Cited by 23 publications

References 45 publications

Do crude oil, gold and the US dollar contribute to Bitcoin investment decisions? An ANN-DCC-GARCH approach

Do crude oil, gold and the US dollar contribute to Bitcoin investment decisions? An ANN-DCC-GARCH approach

Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022

Forecasting Bitcoin Closing Price by Four Machine Learning Algorithms

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