机器学习预测二元期权涨跌，结果不如随机猜测。
Machine Learning vs. Randomness: Challenges in Predicting Binary Options Movements
- 用多种模型测试欧元兑美元走势预测
- 所有模型准确率均低于零基线
- 适合金融风控或反诈研究者阅读
二元期权常被宣传为可通过预测模型持续盈利，但其内在的随机性和随机过程导致价格波动极难预测。本研究基于2021至2023年欧元兑美元汇率数据，测试了随机森林、逻辑回归、梯度提升、k近邻（kNN）等经典机器学习模型，以及多层感知机（MLP）和长短期记忆网络（LSTM）等神经网络架构，并在超参数优化前后进行评估。尽管进行了全面尝试，所有模型的预测准确率均未超越零基线（ZeroR），凸显二元期权本质上的不可预测性。研究结论表明，二元期权缺乏可挖掘的规律性，不适宜采用机器学习方法进行建模预测。
原文摘要 · Abstract (English)
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.
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