Bitcoin price prediction in the time of COVID-19

Luo, Jiayun

doi:10.1109/msieid52046.2020.00050

Cited by 14 publications

(6 citation statements)

References 8 publications

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“…The data was collected for a year, from January 1 to December 31, 2021. The reason for this data scrapping duration was due to the effect from COVID-19 pandemic season that happened since end of 2019 and at its peak throughout the year 2021, thus, the data are more adequate to explore the economic impact through the public's sentiment, particularly towards cryptocurrency-related matter [43], [44]. During this period, social media and online news are optimally used due to Movement Control Order (MCO) that restrict people from going out.…”

Section: Methodsmentioning

confidence: 99%

Crypto-sentiment Detection in Malay Text Using Language Models with an Attention Mechanism

Zamani,

Kamaruddin

2023

J. Inf. Syst. Eng. Bus. Intell.

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Background: Due to the increased interest in cryptocurrencies, opinions on cryptocurrency-related topics are shared on news and social media. The enormous amount of sentiment data that is frequently released makes data processing and analytics on such important issues more challenging. In addition, the present sentiment models in the cryptocurrency domain are primarily focused on English with minimal work on Malay language, further complicating problems. Objective: The performance of the sentiment regression model to forecast sentiment scores for Malay news and tweets is examined in this study. Methods: Malay news headlines and tweets on Bitcoin and Ethereum are used as the input. A hybrid Generalized Autoregressive Pretraining for Language Understanding (XLNet) language model in combination with Bidirectional-Gated Recurrent Unit (Bi-GRU) deep learning model is applied in the proposed sentiment regression implementation. The effectiveness of the proposed sentiment regression model is also investigated using the multi-head self-attention mechanism. Then, a comparison analysis using Bidirectional Encoder Representations from Transformers (BERT) is carried out. Results: The experimental results demonstrate that the number of attention heads is vital in improving the XLNet-GRU sentiment model performance. There are slight improvements of 0.03 in the adjusted R2 values with an average MAE of 0.163 (Malay news) and 0.174 (Malay tweets). In addition, an average RMSE of 0.267 and 0.255 were obtained respectively for Malay news and tweets, which show that the proposed XLNet-GRU sentiment model outperforms the BERT sentiment model with lesser prediction errors. Conclusion: The proposed model contributes to predicting sentiment on cryptocurrency. Moreover, this study also introduced two carefully curated Malay corpora, CryptoSentiNews-Malay and CryptoSentiTweets-Malay, which are extracted from news and tweets, respectively. Further works to enhance Malay news and tweets corpora on cryptocurrency-related issues will be expended with implementing the proposed XLNet Bi-GRU deep learning model for greater financial insight. Keywords: Cryptocurrency, Deep learning model, Malay text, Sentiment analysis, Sentiment regression model

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Section: Methodsmentioning

confidence: 99%

Crypto-sentiment Detection in Malay Text Using Language Models with an Attention Mechanism

Zamani,

Kamaruddin

2023

J. Inf. Syst. Eng. Bus. Intell.

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“…In a separate study, Jiayun Iuo et al [9] employed techniques such as Random Forest (RF), Decision Tree, Support Vector Machine (SVM), and AdaBoost to improve cryptocurrency price prediction accuracy. Collectively, their findings indicate significant enhancements, with Decision Tree achieving 95% accuracy and AdaBoost surpassing this with an impressive 97.5% accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Unveiling Ethereum’s Future: LSTM-Based Price Prediction and a Systematic Blockchain Analysis

Likhitha,

Raj,

Ul Islam

2023

E3S Web Conf.

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Cryptocurrency has emerged as a revolutionary innovation that has been replacing traditional finances and enthralling the worldwide technology landscape. This has gained a lot of popularity worldwide for its potential to enable peer-to-peer transactions and offer opportunities for investment and novelty. Nevertheless, it gives rise to issues concerning regulatory adherence, instability, and security apprehensions, turning them into a topic of continuous evaluation and investigation within the fields of finance and technology. This research paper presents a comprehensive exploration of the historical evolution of “Ethereum” as one of the leading blockchain platforms, with a primary focus on price prediction using a long-short-term memory (LSTM) machine learning model. The study includes various critical aspects of Ethereum, starting from its historical evolution to its potential future scope in scaling solutions and payments, and also covering the insights of Ethereum’s tokenomics, utility, and beyond. In addition, the methodology involves using the LSTM model to analyze data from Ethereum. The accuracy of price predictions is assessed by evaluating error metrics and further improved by visualizing the data through graphs that show indicators. This paper gives an in-depth perspective for anyone who is seeking a holistic understanding of cryptocurrencies, mainly concentrated on Ethereum, and also provides valuable guidance to investors, developers, and enthusiasts, encouraging them to make knowledgeable decisions in the everchanging blockchain ecosystem.

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“…Linear Regression, RNN, and LSTM [19], [20], [22], [23], ARIMAX [21], Linear discriminant analysis [24], ARIMA [25], Logistic regression, Naive Bayes, SVM [26], Multiple linear regression [27], Tweet corpus in COVID-19 era [28], Random Forest, Decision tree, AdaBoost [29], XGBoost-Composite model [30], Q-Learning [This paper].…”

Section: Striving For Accurate Predictionmentioning

confidence: 99%

“…al. [29] tried to feed four different machine learning models with three different data: Bitcoin exchange data, COVID-19 data, and Twitter data from January 2020 to July 2020. One of the findings of this study is COVID-19 data does not help to improve the prediction.…”

Section: B Striving For Accurate Predictionmentioning

confidence: 99%

Twitter Attribute Classification with Q-Learning on Bitcoin Price Prediction

Sattarov¹,

Choi²

2022

Preprint

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Aspiring to achieve an accurate Bitcoin price prediction based on people's opinions on Twitter usually requires millions of tweets, using different text mining techniques (preprocessing, tokenization, stemming, stop word removal), and developing a machine learning model to perform the prediction. These attempts lead to the employment of a significant amount of computer power, central processing unit (CPU) utilization, random-access memory (RAM) usage, and time. To address this issue, in this paper, we consider a classification of tweet attributes that effects on price changes and computer resource usage levels while obtaining an accurate price prediction. To classify tweet attributes having a high effect on price movement, we collect all Bitcoin-related tweets posted in a certain period and divide them into four categories based on the following tweet attributes: (i ) the number of followers of the tweet poster, (i i ) the number of comments on the tweet, (i i i ) the number of likes, and (i v ) the number of retweets. We separately train and test by using the Q-learning model with the above four categorized sets of tweets and find the best accurate prediction among them. Especially, we design several reward functions to improve the prediction accuracy of the Q-leaning. We compare our approach with a classic approach where all Bitcoin-related tweets are used as input data for the model, by analyzing the CPU workloads, RAM usage, memory, time, and prediction accuracy. The results show that tweets posted by users with the most followers have the most influence on a future price, and their utilization leads to spending 80% less time, 88.8% less CPU consumption, and 12.5% more accurate predictions compared with the classic approach.

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Bitcoin price prediction in the time of COVID-19

Cited by 14 publications

References 8 publications

Crypto-sentiment Detection in Malay Text Using Language Models with an Attention Mechanism

Crypto-sentiment Detection in Malay Text Using Language Models with an Attention Mechanism

Unveiling Ethereum’s Future: LSTM-Based Price Prediction and a Systematic Blockchain Analysis

Twitter Attribute Classification with Q-Learning on Bitcoin Price Prediction

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