Consumer upgrading and growing investment demand have brought portfolio management
to the forefront. As scholarly investigations garner pace in this field of inquiry, a critical …

Diffuse correlation spectroscopy (DCS) is a non-invasive technology for the evaluation of
blood perfusion in deep tissue. However, it requires high computational resources for data …

Can you make profits by trading on corporate disclosures using machine learning? In this
study, we aim to obtain a conservative estimate of profitability, while accounting for the …

We introduce a unified framework for rapid, large-scale portfolio optimization that
incorporates both shrinkage and regularization techniques. This framework addresses …

This paper proposes a dynamic process of portfolio risk measurement to address potential
information loss. The proposed model takes advantage of financial big data to incorporate …

We construct a momentum factor that identifies cross-sectional winners and losers based on
a weighting scheme that incorporates all the price data, over the entire lookback period, as …

This study investigates the application of orthogonal generalized auto-regressive conditional
heteroscedasticity (OGARCH) in predicting the co-movement of banking sector stocks in …

Risk parity portfolio optimization, using expected shortfall as the risk measure, is
investigated when asset returns are fat‐tailed and heteroscedastic with regime switching …

In this article, the authors explain non-normal probability distributions and the reasons it is
important to properly model the tails of one or more distributions in applications to asset …

In this paper we describe a methodology for parameter estimation of multivariate
distributions defined as normal mean-variance mixture where the mixing random variable is …