Semi-strong efficient market of Bitcoin and Twitter: An analysis of semantic vector spaces of extracted keywords and light gradient boosting machine models

Fang, Wendy; Gacesa, Marko

doi:10.1016/j.irfa.2023.102692

Cited by 4 publications

(1 citation statement)

References 53 publications

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“…A light gradient boosting machine (LGBM) classifier was employed in [38]. The results showed that 78.06% and 94.03% of hourly and daily bullish market movements can be attributed to public tweets, whereas 83.08% and 94.60% of hourly and daily bearish market movements could be justified by public tweets.…”

Section: Sentiment Analysismentioning

confidence: 99%

The Impact of Academic Publications over the Last Decade on Historical Bitcoin Prices Using Generative Models

Bâra,

Oprea

2024

JTAER

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Since 2012, researchers have explored various factors influencing Bitcoin prices. Up until the end of July 2023, more than 9100 research papers on cryptocurrencies were published and indexed in the Web of Science Clarivate platform. The objective of this paper is to analyze the impact of publications on Bitcoin prices. This study aims to uncover significant themes within these research articles, focusing on cryptocurrencies in general and Bitcoin specifically. The research employs latent Dirichlet allocation to identify key topics from the unstructured abstracts. To determine the optimal number of topics, perplexity and topic coherence metrics are calculated. Additionally, the abstracts are processed using BERT-transformers and Word2Vec and their potential to predict Bitcoin prices is assessed. Based on the results, while the research helps in understanding cryptocurrencies, the potential of academic publications to influence Bitcoin prices is not significant, demonstrating a weak connection. In other words, the movements of Bitcoin prices are not influenced by the scientific writing in this specific field. The primary topics emerging from the analysis are the blockchain, market dynamics, transactions, pricing trends, network security, and the mining process. These findings suggest that future research should pay closer attention to issues like the energy demands and environmental impacts of mining, anti-money laundering measures, and behavioral aspects related to cryptocurrencies.

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Section: Sentiment Analysismentioning

confidence: 99%