Solving Portfolio Optimization Problems Using AMPL

Karakalidis, Alexis; Sifaleras, Angelo

doi:10.1007/978-3-319-33003-7_8

Search citation statements

Order By: Relevance

Paper Sections

Select...

Which Realistic Constraints Are Used?1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2020

2021

Publication Types

Select...

Article1

Book1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to Karakalidis & Sifaleras (2017), AMPL is an easy-to-learn language where the researcher implements the mathematical model simply using a natural language and notations very close to those used in the writing of the model, and allows the call optimization software. Among these solvers, we can highlight CPLEX  , GUROBI Optimizer  , and KNITRO  (used for nonlinear problems).…”

Section: Which Realistic Constraints Are Used?mentioning

confidence: 99%

Analysis of new approaches used in portfolio optimization: a systematic literature review

Milhomem

Dantas

2020

Prod.

View full text Add to dashboard Cite

Section: Which Realistic Constraints Are Used?mentioning

confidence: 99%

Analysis of new approaches used in portfolio optimization: a systematic literature review

Milhomem

Dantas

2020

Prod.

View full text Add to dashboard Cite

Analysis of New Approaches Used in Portfolio Optimization: A Systematic Literature Review

Milhomem

Dantas

2021

Adaptation, Learning, and Optimization

View full text Add to dashboard Cite

Paper aims: To do a comprehensive review of the exact and heuristic methods, software/programming languages, constraints, and types of analysis (technical and fundamental) used to solve the portfolio optimization problem. Originality:The paper presents a useful discussion on aspects of portfolio optimization, both for researchers and investors and for finance professionals.Research method: A systematic literature review was performed, and the articles were compiled according to pre-established criteria/filters. Main findings:A point of attention should be given to the input data of optimization models. Depending on the degree of the estimation error of these input parameters, the optimization results may be lower than the results of the 1/N trading strategy.Implications for theory and practice: Robust optimization, Fuzzy logic, and prediction are examples of techniques used to reduce estimation errors. At the end of the article are pointed out trends and some gaps for future work.

show abstract

Solving Portfolio Optimization Problems Using AMPL

Cited by 2 publications

References 27 publications

Analysis of new approaches used in portfolio optimization: a systematic literature review

Analysis of new approaches used in portfolio optimization: a systematic literature review

Analysis of New Approaches Used in Portfolio Optimization: A Systematic Literature Review

Contact Info

Product

Resources

About