The viscosity structure of Earth's deep mantle affects the thermal evolution of Earth, the
ascent of mantle plumes, settling of subducted oceanic lithosphere, and the mixing of …

Heart disease prediction is a critical task regarding human health. It is based on deriving an
Machine Learning model from medical parameters to predict risk levels. In this work, we …

In many practical applications, correlation matrices might be affected by the “curse of
dimensionality” and by an excessive sensitiveness to outliers and remote observations …

The mean–variance model is widely acknowledged as the foundation of portfolio allocation
because it provides a framework for analyzing the trade-off between risk and return for …

Asset allocation constitutes one of the most crucial and most challenging tasks in financial
engineering, which often requires the estimation of large covariance or precision matrices …

A detailed, multi-disciplinary approach to investment analytics Portfolio Construction and
Analytics provides an up-to-date understanding of the analytic investment process for …

We propose a portfolio rebalance framework that integrates machine learning models into
the mean-risk portfolios in multi-period settings with risk-aversion adjustment. In each …

It is documented in the literature that due to estimation errors, mean-variance efficient
portfolios deliver no higher out-of-sample Sharpe ratios than does the naïve equally …

Within the new Basel regulatory framework for market risks, non-securitization credit
positions in the trading book are subject to a separate default risk charge (formally …

Minimum-variance portfolios, which ignore the mean and focus on the (co) variances of
asset returns, outperform mean–variance approaches in out-of-sample tests. Despite these …