  The application of deep learning to non-stationary temporal datasets can lead
to overfitted models that underperform under regime changes. In this work, we
propose a modular machine learning pipeline for ranking predictions on temporal
panel datasets which is robust under regime changes. The modularity of the
pipeline allows the use of different models, including Gradient Boosting
Decision Trees (GBDTs) and Neural Networks, with and without feature
engineering. We evaluate our framework on financial data for stock portfolio
prediction, and find that GBDT models with dropout display high performance,
robustness and generalisability with reduced complexity and computational cost.
We then demonstrate how online learning techniques, which require no retraining
of models, can be used post-prediction to enhance the results. First, we show
that dynamic feature projection improves robustness by reducing drawdown in
regime changes. Second, we demonstrate that dynamical model ensembling based on
selection of models with good recent performance leads to improved Sharpe and
Calmar ratios of out-of-sample predictions. We also evaluate the robustness of
our pipeline across different data splits and random seeds with good
reproducibility.
