 Single-stage portfolio optimization with automated machine learning for M6Xinyu Huang, David P. Newton, Emmanouil Platanakis and Charles Sutcliffe International Journal of Forecasting, 2025, vol. 41, issue 4, 1450-1460 Abstract: The goal of the M6 forecasting competition was to shed light on the efficient market hypothesis by evaluating the forecasting abilities of participants and performance of their investment strategies. In this paper, we challenge the ‘estimate-then-optimize’ approach with one that directly optimizes portfolio weights from data. We frame portfolio selection as a constrained penalized regression problem. We present a data-driven approach that automatically performs model selection and hyperparameter tuning to maximize the objective without noisy or potentially misspecified intermediate steps. Finally, we show how the portfolio weights can be optimized using the Method of Moving Asymptotes. Testing on the M6 competition data, our approach achieves a global rate of return of 9.5% and an information ratio of 5.045, which is in stark contrast to the mean IR of the M6 competition teams of −3.421 and the IR of 0.453 for the M6 benchmark. Keywords: Automated machine learning; Portfolio selection; Investment analysis; Estimation risk; Parameter uncertainty (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:41:y:2025:i:4:p:1450-1460 DOI: 10.1016/j.ijforecast.2024.08.004 Access Statistics for this article International Journal of Forecasting is currently edited by R. J. Hyndman More articles in International Journal of Forecasting from Elsevier |
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