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Maximizing Portfolio Predictability with Machine Learning
Solving for the optimal constrained weights in the multi-asset MPP gives portfolios with a high monthly coefficient of determination, given the sample covariance matrix of predicted return errors from a machine learning model.
Bootstrap inference for quantile-based modal regression
Specifically, we propose to estimate the conditional mode by minimizing the derivative of the estimated conditional quantile function defined by smoothing the linear quantile regression estimator, and develop two bootstrap methods, a novel pivotal bootstrap and the nonparametric bootstrap, for our conditional mode estimator.
Statistics Theory Methodology Statistics Theory
Machine Learning Portfolio Allocation
We find economically and statistically significant gains when using machine learning for portfolio allocation between the market index and risk-free asset.
