We investigate a new family of distributionally robust optimization problem under marginal
and copula ambiguity with applications to portfolio optimization problems. The proposed …

This paper explores the use of machine learning algorithms and narrative sentiments when
applied to the task of forecasting and trading Bitcoin. The forecasting framework starts from …

To comprehensively reflect the heteroscedasticity, nonlinear dependence and heavy-tailed
distributions of stock returns while reducing the huge cost of parameter estimation, we use …

This study aims to test the validity of the Fama-French Five-Factor Model (FF5F) for Turkey.
Within the scope of the study, throughout 468 weeks between September 2009 and August …

Forming quantitative portfolios using statistical risk models presents a significant challenge
for hedge funds and portfolio managers. This research investigates three distinct statistical …

The fixed income market (ie bonds) is a massive asset class with an overall size of USD 100
trillion that remains relatively under-investigated using machine learning. The yield curve is …

The valuation and planning of complex projects are becoming increasingly challenging with
rising market uncertainty and the deregulation of many industries, which have also raised …

This paper proposes a multilayer network risk factor pricing model to depict the impact of
interactions between stocks on excess stock returns by constructing the network risk factor …

This article extends the socially responsible multiobjective problem to (i) estimating optimal
portfolios via reward/risk maximization,(ii) including dependence structure between asset …

University Dissertations No 416/2021, ISBN: ISBN: 978-91-89283-79-4 (print), 978-91-
89283-80-0 (pdf). This thesis studies and develops copula-based portfolio optimization. The …