On technical trading and social media indicators for cryptocurrency price classification through deep learning

Uras, Nicola; Conversano, Claudio; Bartolucci, Silvia; Destefanis, Giuseppe

doi:10.1016/j.eswa.2022.116804

Cited by 63 publications

(26 citation statements)

References 36 publications

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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%

“…The authors reported 3.7 percent improvement in 2 in predicting the next 4-hour Bitcoin price compared to models that did not use sentiment as input. Ortu et al (2022) also utilised a BERT model to extract sentiments from 4,423 Github and 33,000 Reddit user comments. The authors reported a significantly improved accuracy when predicting the next-hour Bitcoin and Ehthreum price change direction for all four models in their experiment: MLP, LSTM, Multivariate Attention LSTM with Fully Convolutional Network (MALSTM-FCN), and CNN.…”

Section: Btc Price Prediction With Nlpmentioning

confidence: 99%

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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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Section: Btc Price Prediction With Nlpmentioning

confidence: 99%

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

View full text Add to dashboard Cite

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“…Before, academic work has been conducted on the interaction between social media and cryptocurrency. One such example is the social media indicator for cryptocurrency price moves prediction [23]. Besides, Phillips et al investigated which certain topics discussed on social media are indicative of cryptocurrency price moves using a statistical Hawkes model [13] and they illustrated the results by the words that precede positive or negative return [24].…”

Section: Nft Market and Social Mediamentioning

confidence: 99%

Understanding NFT Price Moves through Social Media Keywords Analysis

Luo¹,

Jia²,

Li³

2022

Preprint

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Non-Fungible Token (NFT) is evolving with the rise of the cryptocurrency market and the development of blockchain techniques, which leads to an emerging NFT market that has become prosperous rapidly. The overall rise procedure of the NFT market has not been well understood. To this end, we consider that social media communities evolving alongside the market growth, are worth exploring and reasoning about, as the mineable information might unveil the market behaviors. We explore the procedure from the perspective of NFT social media communities and its impact on the NFT price moves with two experiments. We perform a Granger causality test on the number of tweets and the NFT price time series and find that the number of tweets has a positive impact on (Granger-causes) the price or reversely for more than half of the 19 top authentic NFT projects but seldom copycat projects. Besides, to investigate the price moves predictability using social media features, we conduct an experiment of predicting Markov normalized NFT price (representing the direction and magnitude of price moves) given social-media-extracted word features and interpret the feature importance to find insights into the NFT communities. Our results show that social media words as the predictors result in all 19 top projects having a testing accuracy above the random baseline. Based on the feature importance analysis, we find that both general market-related words and NFT event-related words have a markedly positive contribution in predicting price moves.

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“…Given the digital and decentralised nature of crypto assets, a major focus has been to understand the drivers of price fluctuations and how to properly value these assets. Studies using empirical data have focused on understanding the price dynamics using machine learning techniques [13][14][15][16][17][18], also including socioeconomic signals (e.g., sentiment gathered from social media platforms) that appears to be intertwined with the price dynamics [19][20][21][22].Research has also shown that movements in the market can be tied to macroeconomic indicators, media exposure and public interest [23,24], policies and regulations [25], and indeed the behavior of other financial assets [26].…”

Section: Introductionmentioning

confidence: 99%

Crypocurrency co-investment network: token returns reflect investment patterns

Mungo¹,

Bartolucci²,

Alessandretti³

2023

Preprint

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Since the introduction of Bitcoin in 2009, the dramatic and unsteady evolution of the cryptocurrency market has also been driven by large investments by traditional and cryptocurrency-focused hedge funds. Notwithstanding their critical role, our understanding of the relationship between institutional investments and the evolution of the cryptocurrency market has remained limited, also due to the lack of comprehensive data describing investments over time. In this study, we present a quantitative study of cryptocurrency institutional investments based on a dataset collected for 1324 currencies in the period between 2014 and 2022 from Crunchbase, one of the largest platforms gathering business information. We show that the evolution of the cryptocurrency market capitalization is highly correlated with the size of institutional investments, thus confirming their important role. Further, we find that the market is dominated by the presence of a group of prominent investors who tend to specialise by focusing on particular technologies. Finally, studying the co-investment network of currencies that share common investors, we show that assets with shared investors tend to be characterized by similar market behavior. Our work sheds light on the role played by institutional investors and provides a basis for further research on their influence in the cryptocurrency ecosystem.

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On technical trading and social media indicators for cryptocurrency price classification through deep learning

Cited by 63 publications

References 36 publications

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

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

Understanding NFT Price Moves through Social Media Keywords Analysis

Crypocurrency co-investment network: token returns reflect investment patterns

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