This paper describes a portfolio optimization system by using Neuro-Fuzzy framework in order to manage stock portfolio. It is great importance to stock investors and applied researchers. The proposed portfolio optimization approach Neuro-Fuzzy System reasoning in order to make a more yields from the stock portfolio, and hence maximize return and minimize risk of a stock portfolio through divers...

Constrained optimization problems have a wide range of applications in science, economics, and engineering. In this paper, a neural network model is proposed to solve a class of nonsmooth constrained optimization problems with a nonsmooth convex objective function subject to nonlinear inequality and affine equality constraints. It is a one-layer non-penalty recurrent neural network based on the...

Abstract The purpose of this article is to evaluate optimal expected utility risk measures (OEU) in a risk-constrained portfolio optimization context where the return maximized. We compare with OEU constraint selection model using value at as constraint. former coherent measure for functions constant relative aversion and allows individual specifications investor’s attitude time preference. In ...

This study investigates the performance of Bitcoin as a diversifier under different constraining portfolio optimization frameworks. The employs frameworks that seek to maximize risk-adjusted returns (Sharpe ratio) by optimizing allocations each asset class (asset allocation). attributes are evaluated comparing portfolios both with and without ranging from equal-weighted, risk-parity, semi-const...

In this paper, we propose a predictor-corrector type Consensus Based Optimization(CBO) algorithm on convex feasible set. Our proposed generalizes the CBO in [11] to tackle constrained optimization problem for global minima of non-convex function defined domain. As practical application algorithm, study portfolio finance. application, introduce an objective choose optimal weight each asset asset...

this study focuses on the optimization of the plane structure. sequential quadratic programming (sqp) will be utilized, which is one of the most efficient methods for solving nonlinearly constrained optimization problems. a new formulation for the second order sensitivity analysis of the two-dimensional finite element will be developed. all the second order required derivatives will be calculat...

We propose solution methods for previouslyunsolved constrained MDPs in which actions can continuously modify the transition probabilities within some acceptable sets. While many methods have been proposed to solve regular MDPs with large state sets, there are few practical approaches for solving constrained MDPs with large action sets. In particular, we show that the continuous action sets can ...