Replication Data for: Machine Learning and the Implementable Efficient Frontier

Harvard Dataverse 2026 Dataset Open access

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Authors: Theis Ingerslev Jensen, Bryan Kelly, Semyon Malamud, Lasse Heje Pedersen
DOI: 10.7910/dvn/gklu6g
Cited by: 0

Abstract

The repo contains the replication package for the paper "Machine Learning and the Implementable Efficient Frontier" by Jensen, Kelly, Malamud, and Pedersen. For the most recent version of the code, visit https://github.com/theisij/ml-and-the-implementable-efficient-frontier.

Fields of Study & Subjects

Financial Markets and Investment Strategies Risk and Portfolio Optimization Stock Market Forecasting Methods

Publication details

Journal / source
Harvard Dataverse
Publication date
8 January 2026
Document type
Dataset