Portfolio Optimisation: Models and Solution Approaches

2013 ◽  
pp. 201-221 ◽  
Author(s):  
J. E. Beasley
Keyword(s):  
Portfolio Optimisation

Multi-Asset Class Portfolio Optimisation Using a Belief Rule-Based System

2010 ◽  
Author(s):  
Ser-Huang Poon ◽  
Yu-Wang Chen ◽  
Jian-Bo Yang ◽  
Dong-Ling Xu ◽  
Dongxu Zhang ◽  
...  
Keyword(s):  
Portfolio Optimisation ◽  
Rule Based ◽  
Rule Based System ◽  
Asset Class

Deep Learning for Portfolio Optimisation

2020 ◽  
Author(s):  
Zihao Zhang ◽  
Stefan Zohren ◽  
Stephen Roberts
Keyword(s):  
Deep Learning ◽  
Portfolio Optimisation

scholarly journals COSMO: A Conic Operator Splitting Method for Convex Conic Problems

2021 ◽  
Vol 190 (3) ◽  
pp. 779-810
Author(s):  
Michael Garstka ◽  
Mark Cannon ◽  
Paul Goulart
Keyword(s):  
Convex Sets ◽  
Operator Splitting ◽  
Splitting Method ◽  
Computational Cost ◽  
Coefficient Matrix ◽  
Benchmark Problems ◽  
Portfolio Optimisation ◽  
Splitting Algorithm ◽  
Semidefinite Programs ◽  
Operator Splitting Method

AbstractThis paper describes the conic operator splitting method (COSMO) solver, an operator splitting algorithm and associated software package for convex optimisation problems with quadratic objective function and conic constraints. At each step, the algorithm alternates between solving a quasi-definite linear system with a constant coefficient matrix and a projection onto convex sets. The low per-iteration computational cost makes the method particularly efficient for large problems, e.g. semidefinite programs that arise in portfolio optimisation, graph theory, and robust control. Moreover, the solver uses chordal decomposition techniques and a new clique merging algorithm to effectively exploit sparsity in large, structured semidefinite programs. Numerical comparisons with other state-of-the-art solvers for a variety of benchmark problems show the effectiveness of our approach. Our Julia implementation is open source, designed to be extended and customised by the user, and is integrated into the Julia optimisation ecosystem.

Mixture design of experiments on portfolio optimisation of power generation

2017 ◽  
Vol 11 (2) ◽  
pp. 322-329 ◽  
Author(s):  
André Rodrigues Monticeli ◽  
Pedro Paulo Balestrassi ◽  
Antonio C. Zambroni de Souza ◽  
Rafael Coradi Leme ◽  
Anderson Paulo de Paiva
Keyword(s):  
Power Generation ◽  
Design Of Experiments ◽  
Mixture Design ◽  
Portfolio Optimisation

Petroleum Exploration Risk in Prospect Portfolio Selection

2017 ◽  
pp. 40-65
Keyword(s):  
Standard Deviation ◽  
Portfolio Selection ◽  
Six Sigma ◽  
Efficient Frontier ◽  
Sharpe Ratio ◽  
Petroleum Exploration ◽  
Management Approach ◽  
Portfolio Optimisation ◽  
Exploration Risk ◽  
Return Variance

Presented method is applied to petroleum exploration for prospect portfolio selection to achieve investment objectives controlling risk. DMAIC framework applies stochastic techniques to risk management. Optimisation resolves Efficient Frontier of portfolios for desired range of expected return with initially defined increment. Simulation measures Efficient Frontier portfolios calculating mean return, variance, standard deviation, Sharpe Ratio, and Six Sigma metrics versus pre-specified target limits. Analysis considers mean return, Six Sigma metrics and Sharpe Ratio and selects the portfolio with maximal Sharpe Ratio as initially the best portfolio. Optimisation resolves Efficient Frontier in a narrow interval with smaller increments. Simulation measures Efficient Frontier performance including mean return, variance, standard deviation, Sharpe Ratio, and Six Sigma metrics versus pre-specified target. Analysis identifies the maximal Sharpe Ratio portfolio, i.e. the best portfolio for implementation. Selected prospects in the portfolio are individual projects. So, Project Management approach is used for control.

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