Short-term bitcoin market prediction via machine learning

Jaquart, Patrick; Dann, David; Weinhardt, Christof

doi:10.1016/j.jfds.2021.03.001

Cited by 114 publications

(65 citation statements)

References 55 publications

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“…As shown in the previous sections, the purpose of this survey paper is to present and compare multiple research papers that employed multiple artificial neural network-based approaches to predict cryptocurrency prices. Noting the pros and cons of each of the methods presented (in terms of time elapsed, prediction accuracy, MAPE, and R-squared), it appears that in a holistic manner the methods used in Jaquart et al (2021), and the MICDL method in Livieris et al (2021) are of significant efficiency.…”

Section: Discussionmentioning

confidence: 99%

“…Based on the data and further tests, the authors argue that MICDL is the most appropriate model in terms of performance prediction. Another research paper with prediction models featuring LSTM is Jaquart et al (2021). The paper focuses on shortterm movement predictions of the cryptocurrency market and attempts the question using an LSTM and GRU (Gated Recurrent Units) approach.…”

Section: Basic Modelsmentioning

confidence: 99%

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Prediction of Cryptocurrency Price Index Using Artificial Neural Networks: A Survey of the Literature

Charandabi

Kamyar

2021

EJBMR

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This paper initially presents a brief overview of the cryptocurrency and its history. We discuss the novel nature of literature attempting to create hybrid artificial neural network models to predict prices of cryptocurrency. For the remaining majority of the paper, we present the details of various hybrid artificial neural networks that have successfully been implemented to predict cryptocurrency prices in the form of a survey. Comparison of methods and results follow in the results section.

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Section: Discussionmentioning

confidence: 99%

Section: Basic Modelsmentioning

confidence: 99%

Prediction of Cryptocurrency Price Index Using Artificial Neural Networks: A Survey of the Literature

Charandabi

Kamyar

2021

EJBMR

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“…(21) Bitcoin or any other crypto currency is not under the influence of any country or government, for this reason, it can be invested by anyone around the globe without the fear of being imposed of taxes from other countries. (7) The success of Bitcoin is measured by its huge capitalism growth and price, it leads to the emerging of various other crypto currencies which differ from Bitcoin in just a few parameters. (22) One of the primary reasons for people to dive into the crypto market is that it's very easy and simple to buy and sell assets via trading platforms such as WazirX, Binance, etc.…”

Section: Problem Statementmentioning

confidence: 99%

“…The price of Bitcoin in January 2017 was 1,000USD and by the end of December 2017, its value went up to16000 USD and its value as on July 2021 is 32818 USD (6,7) . We can say that the crypto market is very volatile, and among all the crypto currencies in the market, Bitcoin is experienced by most of the investors due to its anonymity and transparency in the system (8,9) .…”

Section: Introductionmentioning

confidence: 99%

Bitcoin Price Prediction Using Machine Learning and Artificial Neural Network Model

Ho¹,

Vatambeti²,

Ravichandran³

2021

IJST

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Objective: This paper explains the working of the linear regression and Long Short-Term Memory model in predicting the value of a Bitcoin. Due to its raising popularity, Bitcoin has become like an investment and works on the Block chain technology which also gave raise to other crypto currency. This makes it very difficult to predict its value and hence with the help of Machine Learning Algorithm and Artificial Neural Network Model this predictor is tested. Methodology: In this study, we have used data sets for Bitcoin for testing and training the ML and AI model. With the help of python libraries, the data filtration process was done. Python has provided with a best feature for data analysis and visualization. After the understanding of the data, we trim the data and use the features or attributes best suited for the model. Implementation of the model is done and the result is recorded. Finding: It was discovered that the linear regression model's accuracy rate is very high when compared to other Machine Learning models from related works; it was found to be 99.87 percent accurate. The LSTM model, on the other hand, shows a mini error rate of 0.08 percent. This, in turn, demonstrates that the neural network model is more optimized than the machine learning model. Novelty: In this work, a small GUI has been created using the tkinter library that will allow the user to input the High, Low, and Open features values and then predict the next value for the coin. This paper compares the prediction outcomes of a machine learning model and an artificial neural network model. Because linear regression provided the highest accuracy compared to the other machine learning models, we used it to compare it to the LSTM model.

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“…In Dutta et al, 2020;Jaquart et al, 2021;Li & Dai, 2020;Struga & Qirici, 2018) Dow Jones index https://tr.investing.com Lamothe-Fernández et al, 2020;Struga & Qirici, 2018) Gold price https://tr.investing.com Dutta et al, 2020;Jaquart et al, 2021;Lamothe-Fernández et al, 2020;Li & Dai, 2020;) Crude oil price https://tr.investing.com Jaquart et al, 2021;Li & Dai, 2020; All input variables and an output variable stated in (195) for testing are allocated. It should also be emphasized that the test dataset is from a future period that is not known at the current moment.…”

Section: Data Descriptionsmentioning

confidence: 99%

Forecasting Bitcoin Prices With the Anfis Model

Karabiyik

Ergün

2021

Dicle Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi

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Recently, Bitcoin has gained great importance in the cryptocurrency market with the highest market capitalization. Investors and researchers have attempted to find out the drivers of Bitcoin prices and if they are predictable. However, there is only limited research in the literature that identifies the most effective economic and technical variables for predicting Bitcoin prices using machine learning models. Thus, in this study, the future Bitcoin prices utilizing several economic and technical factors using the ANFIS model are aimed to forecasted between 01.05.2013 -26.02.2021 periods. The findings show that the ANFIS model produced accurate and consistent predicting results that are in line with the real data. As a result, investors who wish to make a profit by predicting future Bitcoin values might consider using the ANFIS approach as a forecasting tool.

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Short-term bitcoin market prediction via machine learning

Cited by 114 publications

References 55 publications

Prediction of Cryptocurrency Price Index Using Artificial Neural Networks: A Survey of the Literature

Prediction of Cryptocurrency Price Index Using Artificial Neural Networks: A Survey of the Literature

Bitcoin Price Prediction Using Machine Learning and Artificial Neural Network Model

Forecasting Bitcoin Prices With the Anfis Model

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