通过价格模式分类比较加密货币与股票市场
文章 arXiv papers · 作者: Yu Zhang et al.
总结
本研究探讨加密货币投资者的交易行为是否不同于股票投资者,并利用资产价格历史作为行为证据。研究首先应用多种机器学习模型,对同期观测到的加密货币和股票时间序列进行分类。报告的高分类准确率表明,两类资产的价格模式可以区分;但仅凭分类结果,无法确定这些差异为何产生或是否会持续。
随后,作者计算了时间序列特征,包括分布矩、极值和多个阶数的自相关,并使用逻辑回归、随机森林、支持向量机等分类器评估这些特征的解释价值。结果表明,这些特征有助于区分两类资产。文中未提供抽样选择、样本外验证或具体准确率数据,因此仅凭本摘要,无法独立评估这些差异的可信度及其交易相关性。该方法属于比较分析,并非交易规则,也不能证明能够获得盈利。
核心观点
- 价格时间序列可用于区分加密货币与股票的行为特征。
- 研究报告称,在同期数据上对这两类资产进行分类时准确率较高。
- 分布统计量和自相关特征有助于解释观察到的模式差异。
- 受测模型包括逻辑回归、随机森林和支持向量机。
- 分类证据揭示了差异,但不能说明差异的成因或盈利能力。
标签
全文
# Classification-Based Analysis of Price Pattern Differences Between Cryptocurrencies and Stocks # Classification-Based Analysis of Price Pattern Differences Between Cryptocurrencies and Stocks Cryptocurrencies are digital tokens built on blockchain technology, with thousands actively traded on centralized exchanges (CEXs). Unlike stocks, which are backed by real businesses, cryptocurrencies are recognized as a distinct class of assets by researchers. How do investors treat this new category of asset in trading? Are they similar to stocks as an investment tool for investors? We answer these questions by investigating cryptocurrencies' and stocks' price time series which can reflect investors' attitudes towards the targeted assets. Concretely, we use different machine learning models to classify cryptocurrencies' and stocks' price time series in the same period and get an extremely high accuracy rate, which reflects that cryptocurrency investors behave differently in trading from stock investors. We then extract features from these price time series to explain the price pattern difference, including mean, variance, maximum, minimum, kurtosis, skewness, and first to third-order autocorrelation, etc., and then use machine learning methods including logistic regression (LR), random forest (RF), support vector machine (SVM), etc. for classification. The classification results show that these extracted features can help to explain the price time series pattern difference between cryptocurrencies and stocks.
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