Exploiting Mean-Variance Portfolio Optimization Problems through Zeroing Neural Networks

Portfolio-optimization by the mean-variance-approach

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Mean - Variance Portfolio Optimisation

QP is the optimization of a quadratic function subject to linear equality and inequality constraints. It arises in multiple objective decision making where the departure of the actual decisions from their corresponding ideal, or bliss, value can be evaluated using a weighted quadratic norm as a measure of deviation. The formulation of mean-variance optimization of uncertain systems also leads t...

Convex Duality in Constrained Mean-variance Portfolio Optimization

Neural network-based mean-variance-skewness model for portfolio selection

In this study, a novel neural network-based mean–variance–skewness model for optimal portfolio selection is proposed integrating different forecasts and trading strategies, as well as investors’ risk preference. Based on the Lagrange multiplier theory in optimization and the radial basis function (RBF) neural network, the model seeks to provide solutions satisfying the trade-off conditions of m...

Application of Clayton Copula in Portfolio Optimization and its Comparison with Markowitz Mean-Variance Analysis

With the aim of portfolio optimization and management, this article utilizes the Clayton-copula along with copula theory measures. Portfolio-Optimization is one of the activities in investment funds. Thus, it is essential to select an appropriate optimization method. In modern financial analyses, there is growing evidence indicating the distribution of proceeds of financial properties is not cu...