theisij/ml-and-the-implementable-efficient-frontier

This repository is research code for reproducing the paper’s machine-learning return forecasts, portfolio-choice results, simulations, figures, and tables. The documented workflow uses supplied financial datasets, fits return-prediction models, builds portfolios for several analyses, then analyzes generated outputs. The README documents the scripts and broad stage ordering, but the R sources were not excerpted, so detailed calls between implementation modules are not asserted; the two sampled C++ utilities are shown without guessed callers.