计算机科学
二次规划
黑色-垃圾模型
文件夹
数学优化
投资组合优化
人工神经网络
项目组合管理
MATLAB语言
对偶(语法数字)
投资策略
系列(地层学)
机器学习
复制投资组合
财务
经济
数学
项目管理
艺术
古生物学
管理
文学类
生物
市场流动性
操作系统
作者
Spyridon D. Mourtas,Vasilios N. Katsikis
标识
DOI:10.1016/j.neucom.2022.05.036
摘要
The Black-Litterman (BL) model is a particularly essential analytical tool for effective portfolio management in financial services sector since it enables investment analysts to integrate investor views into market equilibrium returns. In this research, we define and study the continuous-time BL portfolio optimization (CTBLPO) problem as a time-varying quadratic programming (TVQP) problem. The investor’s views in the CTBLPO problem are regarded as a forecasting problem, and they are generated by a novel neural network (NN) model. More precisely, employing a novel multi-function activated by a weights-and-structure-determination for time-series (MAWTS) algorithm, a 3-layer feed-forward NN model, called MAWTSNN, is proposed for handling time-series modeling and forecasting problems. Then, using real-world datasets, the CTBLPO problem is approached by two different TVQP NN solvers. These solvers are the zeroing NN (ZNN) and the linear-variational-inequality primal–dual NN (LVI-PDNN). The experiment findings illustrate and compare the performances of the ZNN and LVI-PDNN in three various portfolio configurations, as well as indicating that the MAWTSNN is an excellent alternative to the traditional approaches. To promote and contend the outcomes of this research, we created two MATLAB repositories for the interested user, that are publicly accessible on GitHub.
科研通智能强力驱动
Strongly Powered by AbleSci AI