Random Forest Methodology

Our Goal

Our goal is to model the relationship between certain predictors and future cryptocurrency price move-ments. In machine learning, this boils down to projecting returns onto different variables or ”features” that can be used to describe the relationship between data and returns.

The Model

We utilize a random forest model. Traditional econometric approaches fail to capture these non- linearities due to their parametric assumptions and inability to model higher-order feature interactions. represents a significant advance in machine learning, leveraging bootstrap aggregation and random feature subsampling to construct a collection of decorrelated decision trees. Each tree in the forest is trained on a bootstrap sample of the historical data and considers only a random subsetof features at each split node. This dual source of randomness serves two critical functions:

• it reduces overfitting through variance reduction

• enables the capture of complex feature interactions that would be computationally intractable in traditional models.

The algorithm’s power emerges from its non-parametric nature. Unlike linear models that impose rigid functional forms, each decision tree recursively partitions the feature space based on variance reduction, automatically discovering threshold values and interaction effects. The ensemble aggregation, through averaging of individual tree predictions, provides robust return estimates that are invariant to outliers and capable of modeling arbitrary non-linear response surfaces.

In the context of financial time series, Random Forest excels at identifying regime-dependent patterns. The algorithm can simultaneously model scenarios where momentum indicators drive return magnitudes while liquidity, volatility and microstructure signals determine return distributions. This adaptivecapacity is particularly valuable in cryptocurrency markets where regime shifts occur frequently and unpredictably.