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

Gabriel Arantes

doi:10.1007/978-3-032-10486-1_43

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

2026-01-01

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PRE

AI

Viviane Leite Dias de Mattos

F

Fabian Corrêa Cardoso

R

Rafael A. Berri

DOI：10.1007/978-3-032-10486-1_43

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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.

Keywords:

Binary Options

Machine Learning

Neural Networks

Financial Market Prediction

Time Series Analysis

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

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