Machine Learning for Bitcoin Pricing — A Structured Literature Review

Jaquart, Patrick; Dann, David; Martin, Carl

doi:10.30844/wi_2020_b4-jaquart

Cited by 10 publications

(4 citation statements)

References 58 publications

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“…[10]Sina(2021) introduced a survey related to the price index prediction of the crypto market. [11]Patrick(2021) broadly reviewed the ML problem of classification and regression for BTC only. [12]Fan(2022) proposed a survey that covers various aspects of cryptocurrency trading (e.g., bubble extreme conditions, volatility and return prediction, and trading systems).…”

Section: Related Surveysmentioning

confidence: 99%

Cryptocurrency market trend and direction prediction using Machine Learning: A Comprehensive Survey

Yamin

Chaudhry

2023

Preprint

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Bitcoin was the first cryptocurrency introduced as a cryptographic proof-based electronic payment system in 2009. Till now approximately more than 10,000 digital coins are active in the crypto market. Cryptocurrency is a virtual digital asset that uses cryptography and blockchain technology for transaction verification and records maintenance. Its trading is gaining attention due to volatile behavior, decentralized nature, and liquidity in this digital asset. Trading this digital asset provides anonymity and security in transactions. Groundless fluctuations in its price contribute to making its trade risky. Market Prediction of the cryptocurrency is trending because it can reduce the trade loss risk. Data related to this market is vast and publicly available on the internet. It is nearly impossible to infer the market by simple data analysis. Statistical price prediction approaches are less effective due to the absence of seasonality in cryptocurrency market data. Therefore researchers proposed efficient price prediction techniques utilizing statistical, algorithmic, and neural network-based Machine Learning models. This paper provides a detailed literature survey related to the state-of-the-art Machine learning-based prediction methodologies for the market prediction of the digital asset from 2014 to 2022. This research will categorize, summarize, and review the existing research in cryptocurrency market prediction using Machine Learning classifiers. This paper will benefit researchers to be productive in the right direction in the future.

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Section: Related Surveysmentioning

confidence: 99%

Cryptocurrency market trend and direction prediction using Machine Learning: A Comprehensive Survey

Yamin

Chaudhry

2023

Preprint

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“…Simultaneously, a literature review has shown that existing research streams are currently not yet in a mature state. Existing approaches are difficult to compare and lack a scientific level of transparency and reproducibility (Jaquart et al 2020a). Therefore, the BDA group is currently pursuing a research endeavour in which Bitcoin market's predictability is investigated using a variety of machine learning methods.…”

Section: Modelling Asset Developmentmentioning

confidence: 99%

Information and Market Engineering at KIT: Quo Vadis?

et al. 2021

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Information systems (IS) are nowadays at the core of many personal and institutional activities and influence daily life more than ever before. To understand, evaluate and envision the forms of how we interact with IS, interdisciplinary and multifaceted research efforts are required. At the Information and Market Engineering chair at the Karlsruhe Institute of Technology, this task is taken head-on via research that stretches from user experiences to system design. In this review, the present research foci at the department are outlined, together with a brief description of its origins and the global developments that underly the necessity of conducting these particular IS studies.

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“…No thorough examination of the predictability of the cryptocurrency market, particularly in the short term, has been performed to date. Furthermore, most research studies have focused primarily on technical aspects and have not examined the impacts of the features of ML models that have been utilized [11]. This is the context in which we attempted to close the research gap by comparing and contrasting various ML models for forecasting the market movements of some important cryptocurrencies at the time of the study.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Algorithm to Predict Cryptocurrency Fluctuation Prices: Increasing Investment Awareness

Ammer

Aldhyani

2022

Electronics

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Digital currencies such as Ethereum and XRP allow for all transactions to be carried out online. To emphasize the decentralized nature of fiat currency, we can refer, for example, to the fact that all virtual currency users may access services without third-party involvement. Cryptocurrency price swings are non-stationary and highly erratic, similarly to the price changes of conventional stocks. Owing to the appeal of cryptocurrencies, both investors and researchers have paid more attention to cryptocurrency price forecasts. With the rise of deep learning, cryptocurrency forecasting has gained great importance. In this study, we present a long short-term memory (LSTM) algorithm that can be used to forecast the values of four types of cryptocurrencies: AMP, Ethereum, Electro-Optical System, and XRP. Mean square error (MSE), root mean square error (RMSE), and normalize root mean square error (NRMSE) analyses were used to evaluate the LSTM model. The findings obtained from these models showed that the LSTM algorithm had superior performance in predicting all forms of cryptocurrencies. Thus, it can be regarded as the most effective algorithm. The LSTM model provided promising and accurate forecasts for all cryptocurrencies. The model was applied to forecast the future closing prices of cryptocurrencies over a period of 180 days. The Pearson correlation metric was applied to assess the correlation between the prediction and target values in the training and testing processes. The LSTM algorithm achieved the highest correlation values in training (R = 96.73%) and in testing (96.09%) in predicting XRP currency prices. Cryptocurrency prices could be accurately predicted using the established LSTM model, which displayed highly efficient performance. The relevance of applying these models is that they may have huge repercussions for the economy by assisting investors and traders in identifying trends in the sales and purchases of different types of cryptocurrencies. The results of the LSTM model were compared with those of existing systems. The results of this study demonstrate that the proposed model showed superior accuracy based on the low prediction errors of the proposed system.

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Machine Learning for Bitcoin Pricing — A Structured Literature Review

Cited by 10 publications

References 58 publications

Cryptocurrency market trend and direction prediction using Machine Learning: A Comprehensive Survey

Cryptocurrency market trend and direction prediction using Machine Learning: A Comprehensive Survey

Information and Market Engineering at KIT: Quo Vadis?

Deep Learning Algorithm to Predict Cryptocurrency Fluctuation Prices: Increasing Investment Awareness

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