Model Ensemble Implementation

3.1 Machine Learning Model

Key advantages of the ML model used for our application include:

• Native handling of categorical features: The model inherently understands and processes categorical data without requiring manual one-hot encoding or other preprocessing techniques. This simplifies the data pipeline and preserves the relationships within categorical variables.

• Robust performance with temporal data: The model effectively captures patterns and trends in time-series data, making it suitable for applications involving forecasting, anomaly detection, or sequence analysis.

• Efficient processing of high-cardinality features: The model can manage features with a large number of unique values (high cardinality) without a significant increase in computational complexity or memory requirements.

• Built-in handling of missing values: The model can gracefully accommodate missing data without requiring imputation or deletion, ensuring that valuable information is not lost due to incomplete datasets.

• Advanced regularization techniques to prevent overfitting: The model incorporates techniques like L1 and L2 regularization, dropout, or early stopping to control model complexity and prevent overfitting, leading to improved generalization performance on unseen data.

3.2 Model Outputs and Interpretation

The aggregated outputs from all models in the ensemble generate robust predictions that consider model uncertainty and diverse perspectives on market conditions. Each model produces a multi-dimensional output representing different aspects of the predicted asset performance, including information about directionality, uncertainty, tail risk and recommended leverage.