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

Battle Count: 239

Highly relevant as a cautionary/negative result paper for quantitative trading. It provides strong empirical evidence that binary options markets exhibit high stochasticity, making ML-based prediction ineffective. This is directly applicable to practitioners considering ML strategies for binary options or highly speculative short-term trading. The methodology (feature selection, hyperparameter tuning, multiple model comparison against baseline) is a good template for evaluating ML applicability in other financial contexts. However, the scope is limited to binary options specifically, and the findings may not extend to all financial instruments.

The models used are relatively standard and well-documented (scikit-learn for traditional ML, basic neural network architectures). Feature engineering with SMA and RSI is straightforward. Hyperband optimization and 5-fold cross-validation are standard practices. The neural network architectures (MLP with 2 hidden layers, single-layer LSTM) are simple. The extended training experiment adds some complexity with custom data partitioning. Overall, a competent ML practitioner could reproduce this work in a few days using standard Python libraries.

The methodology is described in reasonable detail (dataset source HistData, specific hyperparameters, model architectures, training configurations). However, no GitHub repository or code is provided. The use of standard libraries (scikit-learn, likely TensorFlow/Keras) and publicly available EUR/USD data aids reproducibility, but exact random seeds, software versions, and preprocessing code are not specified. The extended training experiment with specific data partitioning adds complexity to exact reproduction.