We examine the diversification benefits of cryptocurrency asset categories. To mitigate the effects of estimation risk, we employ the Bayes-Stein model with no short-selling and variance-based constraints. We estimate the inputs using lasso regression and elastic net regression, employing the...

In the realm of investment decision-making, it is widely recognized that effective decision-making necessitates the integration of various knowledge domains. This paper presents a novel approach to estimating investors’ views in the Black-Litterman model by accumulating evidence from various...

Sub-Saharan African (SSA) countries are struggling as they have a lower level of human capital than developed countries. These economies are also trying to develop their financial sector with improved products and services. The main cause of their slow development in this region is income...

At the heart of the portfolio selection problem lies the challenge of accurately estimating asset expected returns and covariance matrices. The classical Black-Litterman model addresses this challenge by combining market equilibrium and investors' views within the Markowitz mean-variance...

We present a structured portfolio optimization framework with sparse inverse covariance estimation and an attention-based LSTM network that exploits machine learning (deep learning) techniques. We shrink Wishart volatility towards a Graphical Lasso initial covariance estimator and solve the...