white papers

    How to harness machine learning for investment strategies

    How to harness machine learning for investment strategies?
    Serge Tabachnik - Head of Research, Multi-Asset

    Serge Tabachnik

    Head of Research, Multi-Asset
    François Chareyron - Portfolio Manager

    François Chareyron

    Portfolio Manager

     

    Need to know

    • LOIM’s research partnership with data vendor Reuters/Refinitiv and French machine-learning start-up Ai for Alpha has produced some notable outcomes including two forthcoming publications in distinguished journals and the Best Paper Award at the 2021 MIDAS/ECML-PKDD conference.
    • Seeking investment applications from the two accepted papers, we have summarised the findings on ML allocation for volatility targeting portfolios.
    • It focuses on the implementation of two machine-learning-based methodologies: the first is based on deep reinforcement learning (DRL) and the second on gradient boosting decision trees (GBDT).
    • Both rely on adaptive machine learning methods to take the typical regime changes of volatility into account – but their approaches vary.

     

    Successful partnership

    Continuous investment research is a hallmark of the LOIM Multi Asset approach. It has spurred improvements in our risk-based strategies, recently enabling us to harness greater bond carry and to better integrate sustainability data in our investment process.

    Since September 2020, LOIM has collaborated with data vendor Reuters/Refinitiv and French machine-learning start-up Ai for Alpha in a research partnership made possible by a grant from the European Institute of Innovation & Technology, which is an integral part of Horizon Europe.

     

    Accomplishments and awards

    The venture has produced several joint projects, with some notable outcomes including two forthcoming publications in AAMAS ALA 2021 workshop, Machine Learning Group (Springer Neural Computing and Applications Journal) and Mining Data for Financial Applications: 6th ECML PKDD Workshop, MIDAS 2021 (Springer).

    We are pleased that the second publication won the Best Paper Award at the 2021 MIDAS/ECML-PKDD conference, and that in recent months both have ranked among the 10 most downloaded papers on SSRN for their subjects.

     

    Investment application

    Seeking investment applications from the two accepted papers, we have summarised the findings on machine learning allocation for volatility targeting portfolios in a white paper. It focuses on the implementation of two machine-learning-based methodologies to determine the optimal allocation between volatility-targeting models. The first is based on deep reinforcement learning (DRL) and the second on gradient boosting decision trees (GBDT).

     

    Differentiated approach

    We find these innovative methodologies to be intrinsically different due to their learning approaches: GBDT uses supervised learning, whereas DRL draws on unsupervised learning1. Both rely on adaptive machine learning methods to take the typical regime changes of volatility into account – but their approaches vary, marking a notable distinction between the two.

    To read the white paper, please use the download button provided.

    Learn more about our Multi Asset capabilities here.

     
     

    Sources

    1 In machine learning, a distinction can be made between supervised learning which uses labelled input and output data, and unsupervised learning which does not. Unsupervised learning works on its own to discover the inherent structure of unlabelled data.

     

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