A Comparative Analysis of Statistical and Machine Learning Models for Outlier Detection in Bitcoin Limit Order Books

Paper: arXiv 2507.14960

Authors: Ivan Letteri

Abstract

The detection of outliers within cryptocurrency limit order books (LOBs) is of paramount importance for comprehending market dynamics, particularly in highly volatile and nascent regulatory environments. This study conducts a comprehensive comparative analysis of robust statistical methods and advanced machine learning techniques for real-time anomaly identification in cryptocurrency LOBs. Within a unified testing environment, named AITA Order Book Signal (AITA-OBS), we evaluate the efficacy of thirteen diverse models to identify which approaches are most suitable for detecting potentially manipulative trading behaviours. An empirical evaluation, conducted via backtesting on a dataset of 26,204 records from a major exchange, demonstrates that the top-performing model, Empirical Covariance (EC), achieves a 6.70% gain, significantly outperforming a standard Buy-and-Hold benchmark. These findings underscore the effectiveness of outlier-driven strategies and provide insights into the trade-offs between model complexity, trade frequency, and performance. This study contributes to the growing corpus of research on cryptocurrency market microstructure by furnishing a rigorous benchmark of anomaly detection models and highlighting their potential for augmenting algorithmic trading and risk management.

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 6.0/10 (capped from 6.5: transaction costs only partially modelled; see penalty flags)
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Detect Outliers in BTC LOBs"] --> B["Data Input:<br>26,204 LOB Records"]
  B --> C["Methodology:<br>AITA-OBS Framework"]
  C --> D["Model Evaluation:<br>13 Statistical & ML Models"]
  D --> E["Backtesting:<br>Performance Benchmarking"]
  E --> F{"Key Outcomes"}
  F --> G["Top Model: Empirical<br>Covariance (EC)"]
  F --> H["Result: 6.70% Gain vs.<br>Buy-and-Hold"]
  F --> I["Insight: Effective for<br>Risk Management"]

Cited References (41)

1. Manipulation of the Bitcoin market: an agent-based study (2022) — link
2. Identifying and treating outliers in finance (2019) — link
3. Price Discovery in Cryptocurrency Markets — → 2506.08718
4. Return and volatility spillovers among cryptocurrencies (2018) — link
5. Trading Strategy Validation Using Forwardtesting with Deep Neural Networks : (2023) — link
6. DNN-ForwardTesting: A New Trading Strategy Validation using Statistical Timeseries Analysis and Deep Neural Networks (2022) — link
7. A Stock Trading System for a Medium Volatile Asset using Multi Layer Perceptron (2022) — link
8. Stock Market Forecasting Using Machine Learning Models Through Volatility-Driven Trading Strategies (2024) — link

1 of 41 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.