Classification-Based Analysis of Price Pattern Differences Between Cryptocurrencies and Stocks

ArXiv ID: 2504.12771 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Cryptocurrency, Price Time Series Analysis, Feature Extraction, Machine Learning Classification, Statistical Properties, Cryptocurrencies

Complexity vs Empirical Score

• Math Complexity: 3.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders
• Why: The paper relies on standard machine learning implementations (MLP, CNN, RNN, etc.) with minimal theoretical derivations, placing it lower in math complexity, but it demonstrates high empirical rigor through detailed data preprocessing, model training, and explicit backtesting-ready procedures on real financial datasets.

  flowchart TD
    A["Research Goal: Compare Price Patterns<br>Cryptocurrencies vs. Stocks"] --> B["Data Collection"]
    B --> C["Feature Extraction<br>Mean, Variance, Kurtosis, Autocorrelation"]
    C --> D["Machine Learning Classification"]
    D --> E{"Model Training & Testing"}
    E -- High Accuracy --> F["Key Finding 1:<br>Distinct Price Patterns"]
    E -- Feature Importance --> G["Key Finding 2:<br>Features Explain Differences"]
    F --> H["Conclusion:<br>Crypto & Stocks are distinct asset classes"]
    G --> H