Machine Learning Risk Models
Journal of Risk & Control 6(1) (2019) 37-64
26 Pages Posted: 8 Jan 2019 Last revised: 10 Apr 2019
Date Written: January 1, 2019
We give an explicit algorithm and source code for constructing risk models based on machine learning techniques. The resultant covariance matrices are not factor models. Based on empirical backtests, we compare the performance of these machine learning risk models to other constructions, including statistical risk models, risk models based on fundamental industry classifications, and also those utilizing multilevel clustering based industry classifications.
Keywords: machine learning, risk model, clustering, k-means, statistical risk models, covariance, correlation, variance, cluster number, risk factor, optimization, regression, mean-reversion, factor loadings, principal component, industry classification, quant, trading, dollar-neutral, alpha, signal, backtest
JEL Classification: G00, G10, G11, G12, G23
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