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利用链上数据分析加密货币风险与价格因素

文章 arXiv papers · 作者: Abdulrezzak Zekiye et al.
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总结

本研究考察加密货币链上指标能否帮助解释价格并区分高风险资产。研究分析历史数据，测量价格与其他参数之间的相关性，通过聚类对加密货币分组，并运用分类算法将资产标记为高风险或非高风险。这些分组旨在帮助投资者将某种代币与链上特征相似的其他代币进行比较。

报告数据显示，39% 的加密货币消失，而 10% 存续超过 1,000 天。价格与最大供应量和总供应量显著负相关，与 24 小时交易量弱正相关。聚类得到五个组，使用 K 近邻方法时，报告的最佳风险分类 F1 得分为 76%。这些是历史相关性和分类结果，并不能证明这些特征会导致价格变化或保证未来能识别风险。摘录没有说明样本构建方式、验证设计或模型在不断变化的市场环境中的表现。

核心观点

  • 分析使用历史链上指标研究加密货币价格和风险分类。
  • 报告称，最大供应量和总供应量与价格显著负相关。
  • 24 小时交易量与价格弱正相关。
  • 聚类根据链上参数将加密货币分为五组。
  • K 近邻方法取得了所报告的最佳风险分类 F1 得分 76%。

标签

加密资产链上数据机器学习统计学风险管理
全文
# AI-Assisted Investigation of On-Chain Parameters: Risky Cryptocurrencies and Price Factors


# AI-Assisted Investigation of On-Chain Parameters: Risky Cryptocurrencies and Price Factors









Cryptocurrencies have become a popular and widely researched topic of interest in recent years for investors and scholars. In order to make informed investment decisions, it is essential to comprehend the factors that impact cryptocurrency prices and to identify risky cryptocurrencies. This paper focuses on analyzing historical data and using artificial intelligence algorithms on on-chain parameters to identify the factors affecting a cryptocurrency's price and to find risky cryptocurrencies. We conducted an analysis of historical cryptocurrencies' on-chain data and measured the correlation between the price and other parameters. In addition, we used clustering and classification in order to get a better understanding of a cryptocurrency and classify it as risky or not. The analysis revealed that a significant proportion of cryptocurrencies (39%) disappeared from the market, while only a small fraction (10%) survived for more than 1000 days. Our analysis revealed a significant negative correlation between cryptocurrency price and maximum and total supply, as well as a weak positive correlation between price and 24-hour trading volume. Moreover, we clustered cryptocurrencies into five distinct groups using their on-chain parameters, which provides investors with a more comprehensive understanding of a cryptocurrency when compared to those clustered with it. Finally, by implementing multiple classifiers to predict whether a cryptocurrency is risky or not, we obtained the best f1-score of 76% using K-Nearest Neighbor.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。