Regularization-Aligned Learning for Sparse Portfolio Optimization

Sparse portfolio optimization seeks high risk-adjusted return from a small set of assets. Optimizing portfolios using estimated returns and risk can amplify estimation error and impair out-of-sample performance. We refer to systematic modifications intended to mitigate this instability broadly as regularization. Learned return forecasts provide one form of regularization, and decision-focused learning incorporates portfolio performance into model training. Yet the existing decision-focused sparse tangency method evaluates dense soft portfolios during training and selects coordinates that mix assets, so its learning objective need not reflect the intended sparse asset decisions. Tangency decisions alone also leave forecast scale unidentified, although it affects signal aggregation and uncertainty adjustment. In this work, we introduce Regularization-Aligned Sparse Tangent Portfolio Optimization (RA-STPO) to make the form, scale, and use of regularization explicit. It learns bounded corrections to a frozen prediction anchor by averaging losses on resampled sparse return paths, then uses calibration, measured uncertainty, and past relative performance to control their deployment. In the main comparison across five markets, RA-STPO achieves the highest average Sharpe ratio and terminal wealth. Our code is available at https://anonymous.4open.science/r/RA-STPO-A210.