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

TL;DR

This paper investigates the difficulty of predicting binary options movements using machine learning, demonstrating that most models fail to outperform simple baselines due to the inherent randomness of the market.

Contribution

It provides empirical evidence that machine learning models cannot reliably predict binary options movements, emphasizing the stochastic nature of such financial instruments.

Findings

Models did not outperform the ZeroR baseline.

Randomness dominates binary options price movements.

Machine learning struggles in highly stochastic markets.

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.