A Short-Term Cryptocurrency Price Movement Prediction Using Centrality Measures

Ho, Kin-Hon; Chiu, Wai-Han; Li, Chin

doi:10.1109/icdmw51313.2020.00058

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…This is consistent with herding theories. This attitude confirms and enriches the suggestion of Ho et al (2020), shedding light on the existing relationship between the centrality measures and the Bitcoin price pattern.…”

Section: Resultssupporting

confidence: 84%

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The logic of markets: From Micro to Macro Dynamics

Caferra¹

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This paper aims at investigating the relationship between news-driven sentiments and the convergence of behavior in cryptocurrencies market, contributing to the existing literature in the field. The novelty stands in the relation set between the tone of news and returns dispersion. The average daily sentiment score deriving from a worldwide online news dataset has been exploited as a proxy of market humor, in the attempt to identify how emotions spread by the press are related to traders' actions. By employing both Cross-sectional standard (CSSD) and absolute (CSAD) deviation, it is found that the rises and falls of optimism shape returns variability. Indeed, the paper evidences how an increase of news positivity is associated with a lower returns dispersion, evidencing the convergence of beliefs among investors.

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Section: Resultssupporting

confidence: 84%

“…The additional predictive power of network measures has been still suggested in Ho et al (2020). However, this paper limits the analysis to the inclusion of these variables in machine learning methods, using conventional asset correlation network with rolling windows without providing details on the relatioship among these variables.…”

Section: Hypothesis Setmentioning

confidence: 99%

The logic of markets: From Micro to Macro Dynamics

Caferra¹

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“…To effectively address the objectives outlined, this study enhances our previous research efforts [25], [26] by incorporating an updated and more comprehensive dataset. This strategic update allows us to delve deeper into the subject matter, yielding a range of new and significant findings and insights that contribute to the current state of the art in the field: Objective #1 (Social Network Analysis in Cryptocurrency Market Dynamics) related:…”

Section: Our Contributionsmentioning

confidence: 99%

Exploring Key Properties and Predicting Price Movements of Cryptocurrency Market Using Social Network Analysis

Ho,

Hou,

Georgiades

et al. 2024

IEEE Access

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The emerging cryptocurrency market is one of the largest financial markets in the world, with a market capitalization that is already surpassing the gross domestic product of many developed economies. Cryptocurrencies are increasingly being adopted as a means of transaction and ownership in the digital domain, particularly in areas like decentralized finance and non-fungible tokens. Known for its high volatility, this market offers investors the potential for higher returns than traditional financial markets like stocks, foreign exchange, and commodities. However, it remains underexplored in academic research. In this paper, we propose the use of social network analysis to effectively model and analyze the cryptocurrency market and conduct a comprehensive numerical study to explore its key properties, including correlation structure, topological characteristics, stability, and influence. Furthermore, we propose the use of centrality measures as novel indicators to improve the accuracy of cryptocurrency price movement predictions. Our research introduces a novel method for understanding and navigating the cryptocurrency market, enabling investors to integrate advanced analytical tools into their decision-making processes.INDEX TERMS Centrality measures, cryptocurrency, price movement prediction, and social network analysis.

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