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

ArXiv ID: 2308.08554 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: On-chain Analysis, Clustering, Classification, Risk Prediction, K-Nearest Neighbor

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders
• Why: The paper applies standard statistical methods (correlation, K-means, K-NN) rather than advanced mathematics, but is heavily data-driven with specific metrics (F1-score, timeframes, market data sources) and a focus on backtest-ready classification.

  flowchart TD
    A["Research Goal:<br>Identify Price Factors &<br>Risky Cryptocurrencies"] --> B["Data Collection<br>Historical On-Chain Data"]
    B --> C{"Computational Analysis"}
    
    C --> D["Correlation Analysis<br>Price vs. Max/Total Supply<br>and 24h Volume"]
    C --> E["Clustering<br>K-Means: 5 Distinct Groups"]
    C --> F["Classification<br>Classifiers for Risk Prediction"]
    
    D --> G["Key Findings"]
    E --> G
    F --> G
    
    subgraph G ["Outcomes"]
        H["Negative Correlation:<br>Price vs. Supply"]
        I["Positive Correlation:<br>Price vs. 24h Volume"]
        J["Risk Prediction:<br>76% F1-Score (KNN)"]
        K["Market Survival:<br>39% Disappeared, 10% >1000 Days"]
    end