Binary Tree Option Pricing Under Market Microstructure Effects: A Random Forest Approach

TL;DR

This paper introduces a machine learning-enhanced binomial option pricing model that incorporates market microstructure effects using Random Forest classifiers trained on high-frequency data, improving realism and accuracy.

Contribution

It extends classical binomial models by embedding real-world trading dynamics through data-driven transition probabilities, maintaining no-arbitrage conditions.

Findings

Achieved 88.25% AUC in predicting price movements.

Option prices deviate by 13.79% from Black-Scholes.

Order flow imbalance is the most influential predictor.

Abstract

We propose a machine learning-based extension of the classical binomial option pricing model that incorporates key market microstructure effects. Traditional models assume frictionless markets, overlooking empirical features such as bid-ask spreads, discrete price movements, and serial return correlations. Our framework augments the binomial tree with path-dependent transition probabilities estimated via Random Forest classifiers trained on high-frequency market data. This approach preserves no-arbitrage conditions while embedding real-world trading dynamics into the pricing model. Using 46,655 minute-level observations of SPY from January to June 2025, we achieve an AUC of 88.25% in forecasting one-step price movements. Order flow imbalance is identified as the most influential predictor, contributing 43.2% to feature importance. After resolving time-scaling inconsistencies in tree construction, our model yields option prices that deviate by 13.79% from Black-Scholes benchmarks, highlighting the impact of microstructure on fair value estimation. While computational limitations restrict the model to short-term derivatives, our results offer a robust, data-driven alternative to classical pricing methods grounded in empirical market behavior.