Solving Realistic Portfolio Optimization Problems via Metaheuristics: A Survey and an Example

Doering, Jana; Juan, Ángel A.; Kizys, Renatas; Fitó, Àngels; Calvet, Laura

doi:10.1007/978-3-319-40506-3_3

Cited by 5 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e single-index model (SIM) was proposed by Sharpe in the 1960s [23]. e model assumes that the return rate of each asset only has a linear relationship with the market return rate, which can be expressed as…”

Section: Single-index Modelmentioning

confidence: 99%

“…ese articles summarize many PO-related references and have the characteristics of comprehensive investigation and clear classification. Different from references [21][22][23][24][25][26], this paper lists representative PO models in different periods and summarizes classical swarm intelligence algorithms which are applied in PO in recent years. It aims to make a reference and prospect for the further application of SI algorithm in PO.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Swarm Intelligence Algorithms for Portfolio Optimization Problems: Overview and Recent Advances

Chen

Zhao

Yuan

2022

Mobile Information Systems

View full text Add to dashboard Cite

Due to the volatility and uncertainty of the financial market, investors often use the form of portfolio to actively manage their assets. Portfolio optimization (PO) is becoming more and more important for investors. However, PO is frequently a kind of NP-hard problem in the field of modern financial optimization, which has gradually attracted the attention and interest of researchers. Some efficient mathematical models were built to describe the return and risk of portfolio. A lot of precise and approximate fast algorithms are used to solve the established PO models. The fundamental purpose is to maximize the return and to minimize the risk of portfolio under certain constraints. In recent years, researchers not only limit the goal of PO to the balance between risk and return, but also pay attention to liquidity, environmental, social, and governance (ESG) controversy level, Sortino ratio, and other indicators. The number of PO targets and constraints is further extended. In the past two decades, swarm intelligence (SI) algorithms have been widely introduced to solve PO problems. SI algorithm is mainly inspired from the daily phenomena in nature or self-organization, self-adaptation, and self-learning of biological population. The existing research results show that SI algorithm has the characteristics of high efficiency and can obtain satisfactory solutions in solving PO problems. The recent advances on the classic portfolio optimization concepts, models, and the usual SI-based solving algorithms are presented. Finally, future potential research directions are presented.

show abstract

Section: Single-index Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Swarm Intelligence Algorithms for Portfolio Optimization Problems: Overview and Recent Advances

Chen

Zhao

Yuan

2022

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…The latter sets the ceiling and/or the floor on the weights allocated to each asset in the portfolio. Doering et al [2016] analyze the role of metaheuristic algorithms in solving the POSP with cardinality and quantity constraints. Specifically, Chang et al [2000] solve the resulting problem with three different metaheuristic techniques; GA, TS and SA.…”

Section: Portfolio Optimization and Selection Problemmentioning

confidence: 99%

A Survey on Financial Applications of Metaheuristics

2017

Self Cite

View full text Add to dashboard Cite

Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, etc. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and reallife combinatorial optimization problems that arise in a number of financial and banking activities. This paper reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. The paper also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.• Applied computing➝Operational research➝Decision analysis • Applied computing➝Law, social and behavioral sciences➝Economics.

show abstract

An experimental metaheuristic approach for portfolio optimization problem with cardinality constraint

Shah¹,

Pant²

2022

AGRIVOLTAICS2021 CONFERENCE: Connecting Agrivoltaics Worldwide

View full text Add to dashboard Cite

Solving Realistic Portfolio Optimization Problems via Metaheuristics: A Survey and an Example

Cited by 5 publications

References 29 publications

Swarm Intelligence Algorithms for Portfolio Optimization Problems: Overview and Recent Advances

Swarm Intelligence Algorithms for Portfolio Optimization Problems: Overview and Recent Advances

A Survey on Financial Applications of Metaheuristics

An experimental metaheuristic approach for portfolio optimization problem with cardinality constraint

Contact Info

Product

Resources

About