Can Machine Learning Algorithms Outperform Traditional Models for Option Pricing?

ArXiv ID: 2510.01446 “View on arXiv”

Authors: Georgy Milyushkov

Abstract

This study investigates the application of machine learning techniques, specifically Neural Networks, Random Forests, and CatBoost for option pricing, in comparison to traditional models such as Black-Scholes and Heston Model. Using both synthetically generated data and real market option data, each model is evaluated in predicting the option price. The results show that machine learning models can capture complex, non-linear relationships in option prices and, in several cases, outperform both Black-Scholes and Heston models. These findings highlight the potential of data-driven methods to improve pricing accuracy and better reflect market dynamics.

Keywords: Option Pricing, Neural Networks, Random Forests, CatBoost, Black-Scholes, Options (Equity)

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced stochastic calculus (Itô’s Lemma, coupled SDEs) and numerical methods (semi-analytical solutions, characteristic functions), yet it also demonstrates strong empirical rigor through synthetic and real market data, model implementation (Python/QuantLib), GitHub code availability, and standard error metrics.

  flowchart TD
    A["Research Goal<br>Can ML Outperform Traditional<br>Option Pricing Models?"] --> B["Methodology<br>Compare ML vs. Black-Scholes & Heston"]
    B --> C{"Data Sources"}
    C --> D["Synthetic Data"]
    C --> E["Real Market Data"]
    D --> F["Computation<br>Train Neural Networks, Random Forests, CatBoost"]
    E --> F
    F --> G["Analysis<br>Evaluate Prediction Accuracy"]
    G --> H["Key Findings<br>ML models capture non-linear relationships<br>ML often outperforms traditional models"]