On Bitcoin Price Prediction

ArXiv ID: 2504.18982 “View on arXiv”

Authors: Grégory Bournassenko

Abstract

In recent years, cryptocurrencies have attracted growing attention from both private investors and institutions. Among them, Bitcoin stands out for its impressive volatility and widespread influence. This paper explores the predictability of Bitcoin’s price movements, drawing a parallel with traditional financial markets. We examine whether the cryptocurrency market operates under the efficient market hypothesis (EMH) or if inefficiencies still allow opportunities for arbitrage. Our methodology combines theoretical reviews, empirical analyses, machine learning approaches, and time series modeling to assess the extent to which Bitcoin’s price can be predicted. We find that while, in general, the Bitcoin market tends toward efficiency, specific conditions, including information asymmetries and behavioral anomalies, occasionally create exploitable inefficiencies. However, these opportunities remain difficult to systematically identify and leverage. Our findings have implications for both investors and policymakers, particularly regarding the regulation of cryptocurrency brokers and derivatives markets.

Keywords: Efficient Market Hypothesis (EMH), machine learning, time series modeling, arbitrage, predictability, Cryptocurrency (Bitcoin)

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Philosophers
• Why: The paper discusses theoretical concepts like the Efficient Market Hypothesis and uses some statistical tests, but the math is generally accessible, focusing on literature review and high-level analysis rather than dense derivations. Empirically, it includes some backtesting snippets and figures (e.g., confusion matrices, returns charts) but lacks detailed implementation data, robust datasets, or comprehensive performance metrics, making it more conceptual than fully backtest-ready.

  flowchart TD
    A["Research Goal: Assess Bitcoin Price Predictability & Market Efficiency"] --> B["Data & Inputs"]
    B --> B1["Historical Bitcoin Price Data"]
    B --> B2["Market Microstructure Data"]
    B --> B3["Economic Indicators"]
    
    A --> C["Methodology & Computation"]
    C --> C1["Theoretical Review: EMH"]
    C --> C2["Time Series Modeling"]
    C --> C3["Machine Learning Approaches"]
    
    B1 & B2 & B3 --> C2 & C3
    
    C1 & C2 & C3 --> D{"Analysis & Findings"}
    
    D --> E["General Trend: Market tends toward efficiency"]
    D --> F["Specific Outcomes: Exploitable inefficiencies exist"]
    D --> G["Challenges: Difficult to systematically identify/arbitrage"]