Mean-risk portfolio optimization with prior pca-based Stock selection

2012 ◽  
Author(s):  
Cristinca Fulga ◽  
Silvia Dedu
Keyword(s):  
Portfolio Optimization ◽  
Stock Selection ◽  
Mean Risk

Robust mean-risk portfolio optimization using machine learning-based trade-off parameter

2021 ◽  
pp. 107948
Author(s):  
Liangyu Min ◽  
Jiawei Dong ◽  
Jiangwei Liu ◽  
Xiaomin Gong
Keyword(s):  
Machine Learning ◽  
Portfolio Optimization ◽  
Trade Off ◽  
Mean Risk

A Long Short-Term Memory Approach Towards Stock Selection and Portfolio Optimization

2020 ◽  
Author(s):  
Sumit Mahlawat ◽  
Utkarsh Prabhakar ◽  
Nishank Goyal ◽  
Praket Parth ◽  
Varun Ramamohan
Keyword(s):  
Portfolio Optimization ◽  
Short Term Memory ◽  
Stock Selection ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Portfolio Optimization with Metaheuristics

2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Georgios Mamanis
Keyword(s):  
Portfolio Optimization ◽  
Portfolio Selection ◽  
Value At Risk ◽  
Optimal Allocation ◽  
Optimization Problems ◽  
Objective Functions ◽  
Cardinality Constraints ◽  
Risk Optimization ◽  
Measures Of Risk ◽  
Mean Risk

<p>Portfolio optimization is the problem ofsearching foran optimal allocation of wealth to put in the available assets. Since the seminalworkdoneby Markowitz, the problem is codifiedas a two-objective mean-risk optimization problem where the best trade-off solutions (portfolios) between risk (measured by variance) and mean are hunted. Complex measures of risk (e.g., value-at-risk, expected shortfall, semivariance), addedobjective functions (e.g., maximization of skewness, liquidity, dividends) and pragmatic, real-worldconstraints (e.g., cardinality constraints, quantity constraints, minimum transaction lots, class constraints) that are included in recent portfolio selection models, provide many optimization challenges. The resulting portfolio optimizationproblem becomes very hard to be tackledwith exact techniquesas it displaysnonlinearities, discontinuities and high dimensional efficient frontiers. These characteristics prompteda lot ofresearchers to explorethe use of metaheuristics, which are powerful techniquesfor discoveringnear optimal solutions (sometimes the real optimum) for hard optimization problems in acceptable computationaltime. This report provides a briefnoteon the field of portfolio optimization with metaheuristics and concludes that especially Multiobjectivemetaheuristics (MOMHs) provide a natural background for dealing with portfolio selection problems with complex measures of risk (which define non-convex, non-differential objective functions), discrete constraints and multiple objectives.</p>

Mean-risk-skewness models for portfolio optimization based on uncertain measure

Optimization ◽  
2018 ◽  
Vol 67 (5) ◽  
pp. 701-714 ◽  
Author(s):  
Jia Zhai ◽  
Manying Bai ◽  
Hongru Wu
Keyword(s):  
Portfolio Optimization ◽  
Uncertain Measure ◽  
Mean Risk

scholarly journals Numerical methods for mean-risk portfolio optimization

2020 ◽  
Author(s):  
Pieter Marais Van Staden
Keyword(s):  
Numerical Methods ◽  
Portfolio Optimization ◽  
Mean Risk

Portfolio Optimization with Metaheuristics

2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Georgios Mamanis
Keyword(s):  
Portfolio Optimization ◽  
Portfolio Selection ◽  
Value At Risk ◽  
Optimal Allocation ◽  
Optimization Problems ◽  
Objective Functions ◽  
Cardinality Constraints ◽  
Risk Optimization ◽  
Measures Of Risk ◽  
Mean Risk

<p>Portfolio optimization is the problem ofsearching foran optimal allocation of wealth to put in the available assets. Since the seminalworkdoneby Markowitz, the problem is codifiedas a two-objective mean-risk optimization problem where the best trade-off solutions (portfolios) between risk (measured by variance) and mean are hunted. Complex measures of risk (e.g., value-at-risk, expected shortfall, semivariance), addedobjective functions (e.g., maximization of skewness, liquidity, dividends) and pragmatic, real-worldconstraints (e.g., cardinality constraints, quantity constraints, minimum transaction lots, class constraints) that are included in recent portfolio selection models, provide many optimization challenges. The resulting portfolio optimizationproblem becomes very hard to be tackledwith exact techniquesas it displaysnonlinearities, discontinuities and high dimensional efficient frontiers. These characteristics prompteda lot ofresearchers to explorethe use of metaheuristics, which are powerful techniquesfor discoveringnear optimal solutions (sometimes the real optimum) for hard optimization problems in acceptable computationaltime. This report provides a briefnoteon the field of portfolio optimization with metaheuristics and concludes that especially Multiobjectivemetaheuristics (MOMHs) provide a natural background for dealing with portfolio selection problems with complex measures of risk (which define non-convex, non-differential objective functions), discrete constraints and multiple objectives.</p>

Quantitative Techniques in Stock Selection and Portfolio Management

2006 ◽  
Vol 23 (3) ◽  
pp. 71-78
Author(s):  
Oliver Buckley
Keyword(s):  
Portfolio Management ◽  
Stock Selection

Recognizing Investor “Comfort” Assets in Portfolio Optimization

CFA Digest ◽  
2013 ◽  
Vol 43 (3) ◽  
Author(s):  
Vipul K. Bansal
Keyword(s):  
Portfolio Optimization
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