HKML S5E3 - Nonlinearities in a multi-factor model framework using Machine Learning by CrunchDAO

Abstract:
CrunchDAO's Crowdsourced Investment Framework makes use of supervised learning to predict returns, which are residualized against, i.e., uncorrelated with linear econometric risk models.
In order to obtain estimates with the desired properties, in this work we investigate the effect of design choices associated with feature engineering and model training. In particular, the orthogonality condition between factors and estimations can be imposed indirectly training on an orthogonal feature space. Alternatively, (non-linear) machine learning models can be trained, imposing orthogonalization in the definition of the fitness function. We show the consequences, in terms of out-of-sample accuracy, of these design choices, both for a feature space orthogonal and not orthogonal to risk factors. In the context of crowdsourced investment research through tournaments, we explore the potential of confidential computing to align the interests of tournament players with that of portfolio managers; from a game theory, perspective, we discuss the need to be able to define a Nash equilibrium in the CrunchDAO tournament.
Speaker:
Matteo is the lead quant researcher of CrunchDAO. With a background in machine learning and dynamical systems theory. He graduated in Space Flight with a Talent Scholarship from TU Delft, worked as a researcher in the Horizon2020 program by the European Commission and worked as a flight dynamics software engineer for the European Space Agency. Together with working in quantitative finance, he is one of the developers of CrunchDeSci, a Decentralized Science platform used to perform research in a reproducible manner.