Pricing European Options with Google AutoML, TensorFlow, and XGBoost

TL;DR

This paper compares Google AutoML, TensorFlow, and XGBoost for pricing European options, demonstrating that machine learning models outperform the traditional Black Scholes Model in accuracy by leveraging historical data.

Contribution

It provides a comparative analysis of modern machine learning techniques for option pricing, highlighting their superior performance over classical models.

Findings

All models outperformed Black Scholes in mean absolute error.

Machine learning models effectively learn complex patterns from historical data.

AutoML, TensorFlow, and XGBoost show promising results for financial modeling.

Abstract

Researchers have been using Neural Networks and other related machine-learning techniques to price options since the early 1990s. After three decades of improvements in machine learning techniques, computational processing power, cloud computing, and data availability, this paper is able to provide a comparison of using Google Cloud's AutoML Regressor, TensorFlow Neural Networks, and XGBoost Gradient Boosting Decision Trees for pricing European Options. All three types of models were able to outperform the Black Scholes Model in terms of mean absolute error. These results showcase the potential of using historical data from an option's underlying asset for pricing European options, especially when using machine learning algorithms that learn complex patterns that traditional parametric models do not take into account.