Empirical Analysis Тowards the Effect of Social Media on Cryptocurrency Price and Volume

Rothman, Tiran; Yakar, Chen

doi:10.19044/esj.2019.v15n31p52

“…Narman et al [54] described how positive and negative comments on social media affect the prices of cryptocurrencies. Rothman et al [55] used videos and posts on YouTube, Facebook, Telegram, and Reddit for sentimental analysis of cryptocurrencies. Kraaijeveld et al [56] analyzed public Twitter sentiment, including Twitter bots, to predict the prices of nine different cryptocurrencies, and Zhang et al [57] examined the relationship between investor attention and cryptocurrencies using Google Trends.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Cryptocurrency Price Prediction Scheme With Inter-Dependent Relations

Tanwar¹,

Patel

²

,

Patel

³

et al. 2021

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Blockchain technology is becoming increasingly popular because of its applications in various fields. It gives an edge over the traditional centralized methods as it provides decentralization, immutability, integrity, and anonymity. The most popular application of this technology is cryptocurrencies, which showed a massive rise in their popularity and market capitalization in recent years. Individual investors, big institutions, and corporate firms are investing heavily in it. However, the crypto market is less stable than traditional commodity markets. It can be affected by many technical, sentimental, and legal factors, so it is highly volatile, uncertain, and unpredictable. Plenty of research has been done on various cryptocurrencies to forecast accurate prices, but the majority of these approaches can not be applied in real-time. Motivated from the aforementioned discussion, in this paper, we propose a deep-learning-based hybrid model (includes Gated Recurrent Units (GRU) and Long Short Term Memory (LSTM)) to predict the price of Litecoin and Zcash with inter-dependency of the parent coin. The proposed model can be used in real-time scenarios and it is well trained and evaluated using standard data sets. Results illustrate that the proposed model forecasts the prices with high accuracy compared to existing models.

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“…Bitcoin miners receive their rewards through verified transactions added to the blockchain list [19]. It is a digital currency or cash that uses the peer-to-peer Internet to instantly confirm transactions between different users [20]. Digital money can be moved and converted into cash through the use of third-party exchange brokers, including ATMs at a specific market value or peer-to-peer when you sell your Bitcoin [21], [22], [23].…”

Section: Introductionmentioning

confidence: 99%

Height-end Multi-layer Perceptron and Machine Learning Methods of Forecasting Bitcoin Price Time Series

Ziweritin¹

2022

Preprint

0

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show abstract

“…Bitcoin miners receive their rewards through verified transactions added to the blockchain list [19]. It is a digital currency or cash that uses the peer-to-peer Internet to instantly confirm transactions between different users [20]. Digital money can be moved and converted into cash through the use of third-party exchange brokers, including ATMs at a specific market value or peer-to-peer when you sell your Bitcoin [21], [22], [23].…”

Section: Introductionmentioning

confidence: 99%

Height-end Multi-layer Perceptron and Machine Learning Methods of Forecasting Bitcoin Price Time Series

Ziweritin¹

2022

Preprint

0

View full text Add to dashboard Cite

show abstract

Empirical Analysis Тowards the Effect of Social Media on Cryptocurrency Price and Volume

Cited by 3 publications

References 10 publications

Deep Learning-Based Cryptocurrency Price Prediction Scheme With Inter-Dependent Relations

Deep Learning-Based Cryptocurrency Price Prediction Scheme With Inter-Dependent Relations

Height-end Multi-layer Perceptron and Machine Learning Methods of Forecasting Bitcoin Price Time Series

Height-end Multi-layer Perceptron and Machine Learning Methods of Forecasting Bitcoin Price Time Series

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