Predicting Ethereum prices with machine learning based on Blockchain information

Kim, Han-Min; Bock, Gee-Woo; Lee, Gunwoong

doi:10.1016/j.eswa.2021.115480

Cited by 55 publications

(30 citation statements)

References 21 publications

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“…Consequently, the last uptrend and downtrend before We employ the most representative cryptocurrencies that are Bitcoin and Ethereum. Specifically, both cryptocurrencies have recorded the largest trading volumes and possess the highest market capitalizations (Kim et al, 2021). As of November 2021, Bitcoin has recorded a trading volume of USD 1240 billion (21,336,435 BTC) and a market capitalization of USD 1097 billion, and Ethereum has reached a trading volume of USD 546 billion (118,187,782 ETH) and a market capitalization of USD 547 billion.…”

Section: Experimental Datamentioning

confidence: 99%

“…Meanwhile, cryptocurrency markets have been affected and gained investors' attention during the current COVID-19 uncertainties. Bitcoin and Ethereum, which are the greatest illustrative cryptocurrencies, have documented the biggest trading volumes and occupy the highest market capitalizations (Kim et al, 2021). For instance, Bitcoin has noted a trading volume of USD 1240 billion (21,336,435 BTC) and a market capitalization of USD 1097 billion, and Ethereum has reached a trading volume of USD 546 billion (118,187,782 ETH) and a market capitalization of USD 547 billion in November 2021.…”

Section: Introductionmentioning

confidence: 99%

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The role of cryptocurrencies in predicting oil prices pre and during COVID-19 pandemic using machine learning

2022

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This study aims to explore the role of cryptocurrencies and the US dollar in predicting oil prices pre and during COVID-19 pandemic. The study uses three neural network models (i.e., Support vector machines, Multilayer Perceptron Neural Networks and Generalized regression neural networks (GRNN)) over the period from January 1, 2018, to July 5, 2021. Our results are threefold. First, our results indicate Bitcoin is the most influential in predicting oil prices during the bear and bull oil market before COVID-19 and during the downtrend during COVID-19. Second, COVID-19 variables became the most influential during the uptrend, especially the number of death cases. Third, our results also suggest that the most accurate model to predict the price of oil under the conditions of uncertainty that prevailed in the world during the bear and bull prices in the wake of COVID-19 is GRNN. Though the best prediction model under normal conditions before COVID-19 during an uptrend is SVM and during a downtrend is GRNN. Our results provide crucial evidence for investors, academics and policymakers, especially during global uncertainties.

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Section: Experimental Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The role of cryptocurrencies in predicting oil prices pre and during COVID-19 pandemic using machine learning

2022

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“…Just like stock prices are influenced by what people are saying on (social) media, so are digital asset prices. We see many sources of text data used in digital assets price prediction: Twitter data (Mohapatra, Ahmed and Alencar, 2019;Wołk, 2020;Raju and Tarif, 2020;Kim, Bock and Lee, 2021), Telegram (Smuts, 2019;Patel, Tanwar, Gupta and Kumar, 2020), Reddit (Ortu, Uras, Conversano, Bartolucci and Destefanis, 2022;Raju and Tarif, 2020), and more. Looking at existing literature, there are a number of models that again use the sentiment score derived from a text source as input to a predictive price model.…”

Section: Btc Price Prediction With Nlpmentioning

confidence: 99%

A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin

Zou¹,

Herremans²

2022

Preprint

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Proposes a multimodal model for Bitcoin extreme price movement prediction using Twitter contents, not just sentiment.• Ablation study of the impact of different modalities on extreme price prediction accuracy.• New publicly available dataset of 9,435,437 tweets over a period of years related to Bitcoin.• Describes a profitable strategy with reduced risk exposure for Bitcoin trading using the proposed multimodal model.• Demonstrates the influence of setting predictive thresholds on risk reduction of a trading strategy.

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“…The tertiary component has been ETL (Extract-Transform-Load) to a statistics data set and NNs methods. Kim et al [14] examine the connection amongst inherent Ethereum BC data and Ethereum prices. Moreover, it can be explored that BC data regarding other publicly accessible coins on the market has been connected with Ethereum prices.…”

Section: Introductionmentioning

confidence: 99%

Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

Kalyani¹,

Velmurugan²,

Abdeljaber³

et al. 2023

Computers, Materials &Amp; Continua

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In recent times, financial globalization has drastically increased in different ways to improve the quality of services with advanced resources. The successful applications of bitcoin Blockchain (BC) techniques enable the stockholders to worry about the return and risk of financial products. The stockholders focused on the prediction of return rate and risk rate of financial products. Therefore, an automatic return rate bitcoin prediction model becomes essential for BC financial products. The newly designed machine learning (ML) and deep learning (DL) approaches pave the way for return rate predictive method. This study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder (JSO-ELMAE) for return rate prediction of BC financial products. The presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial products. Besides, the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results. The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates. The experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many aspects. The experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.

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Predicting Ethereum prices with machine learning based on Blockchain information

Cited by 55 publications

References 21 publications

The role of cryptocurrencies in predicting oil prices pre and during COVID-19 pandemic using machine learning

The role of cryptocurrencies in predicting oil prices pre and during COVID-19 pandemic using machine learning

A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin

Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

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