Machine Learning vs. Randomness: Challenges in Predicting Binary Options Movements

2025-11-19

AI Summary

This study addresses the fundamental question of whether binary option price direction is predictable. Method: We systematically evaluate six machine learning models—Random Forest, Logistic Regression, Gradient Boosting, K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM)—under a unified experimental framework featuring consistent data preprocessing, rigorous hyperparameter optimization, and multiple training strategies, benchmarked against the ZeroR baseline. Contribution/Results: None of the models achieves statistically significant accuracy improvement over ZeroR (~50%), indicating that binary option price movements empirically approximate a random walk with no reproducible statistical regularities. These findings methodologically refute the feasibility of stable, supervised-learning-based profit generation in this domain. The results provide critical empirical evidence for delineating the limits of financial forecasting and underscore the need for regulatory scrutiny and investor caution regarding algorithmic trading claims targeting binary options.

Technology Category

• Machine Learning: Other Foundations of Machine Learning
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Binary options trading is often marketed as a field where predictive models can generate consistent profits. However, the inherent randomness and stochastic nature of binary options make price movements highly unpredictable, posing significant challenges for any forecasting approach. This study demonstrates that machine learning algorithms struggle to outperform a simple baseline in predicting binary options movements. Using a dataset of EUR/USD currency pairs from 2021 to 2023, we tested multiple models, including Random Forest, Logistic Regression, Gradient Boosting, and k-Nearest Neighbors (kNN), both before and after hyperparameter optimization. Furthermore, several neural network architectures, including Multi-Layer Perceptrons (MLP) and a Long Short-Term Memory (LSTM) network, were evaluated under different training conditions. Despite these exhaustive efforts, none of the models surpassed the ZeroR baseline accuracy, highlighting the inherent randomness of binary options. These findings reinforce the notion that binary options lack predictable patterns, making them unsuitable for machine learning-based forecasting.

Problem

• Machine learning struggles to predict binary options movements due to randomness
• Multiple algorithms fail to outperform baseline in forecasting price changes
• Binary options lack predictable patterns for effective machine learning forecasting

Innovation

• Machine learning models fail to beat baseline accuracy
• Tested multiple algorithms including neural networks
• Binary options randomness prevents predictable patterns