An Effective Approach for Obtaining a Group Trading Strategy Portfolio Using Grouping Genetic Algorithm

Chen, Chun‐Hao; Chen, Yu-Hsuan; Lin, Jerry Chun‐Wei; Wu, Mu-En

doi:10.1109/access.2018.2889737

Cited by 29 publications

(24 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The classical Markowitz model in (6) can be updated by considering the transaction cost model (11) and cardinality constraint (12), (13). The updated optimization problem, can be written in the expanded form as min t t t T…”

Section: Unified Optimization Problemmentioning

confidence: 99%

“…financial parameters are stochastic and hard to predict with a high degree of accuracy [5]- [8], it is expected to find an optimal selection of stock options to maximize the chance of gaining monetary gain by leveraging the advances in the theory of mathematical modeling and optimization algorithms. Modern approaches on portfolio optimization rely on optimization-based algorithms to find the optimal proportion of each stock in the portfolio [9]- [11]. These approaches rely on a fitness function, also called the objective function, which takes a proportion of each stock in the portfolio and outputs a fitness value.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Portfolio Management for Engineering Problems Using Nonconvex Cardinality Constraint: A Computing Perspective

Khan¹,

Cao²,

Katsikis³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

The problem of portfolio management relates to the selection of optimal stocks, which results in a maximum return to the investor while minimizing the loss. Traditional approaches usually model the portfolio selection as a convex optimization problem and require the calculation of gradient. Note that gradient-based methods can stuck at local optimum for complex problems and the simplification of portfolio optimization to convex, and further solved using gradient-based methods, is at a high cost of solution accuracy. In this paper, we formulate a nonconvex model for the portfolio selection problem, which considers the transaction cost and cardinality constraint, thus better reflecting the decisive factor affecting the selection of portfolio in the real-world. Additionally, constraints are put into the objective function as penalty terms to enforce the restriction. Note that this reformulated problem cannot be readily solved by traditional methods based on gradient search due to its nonconvexity. Then, we apply the Beetle Antennae Search (BAS), a nature-inspired metaheuristic optimization algorithm capable of efficient global optimization, to solve the problem. We used a large real-world dataset containing historical stock prices to demonstrate the efficiency of the proposed algorithm in practical scenarios. Extensive experimental results are presented to further demonstrate the efficacy and scalability of the BAS algorithm. The comparative results are also performed using Particle Swarm Optimizer (PSO), Genetic Algorithm (GA), Pattern Search (PS), and gradient-based fmincon (interior-point search) as benchmarks. The comparison results show that the BAS algorithm is six times faster in the worst case (25 times in the best case) as compared to the rival algorithms while achieving the same level of performance. INDEX TERMS Portfolio management, constrained optimization, nature-inspired algorithms, beetle search optimization.

show abstract

Section: Unified Optimization Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Portfolio Management for Engineering Problems Using Nonconvex Cardinality Constraint: A Computing Perspective

Khan¹,

Cao²,

Katsikis³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Operator crossover digunakan sebagai penentu frekuensi dalam pertukaran antar kromosom untuk menghasilkan kromosom baru (Shih, Gunarathne, Ochirbat, & Su, 2018). Sebagai contoh Chen menggunakan two point crossover untuk menciptakan kromosom baru dari pertukaran kromosom basis dengan kromosom input (Chen, Chen, Lin, & Wu, 2019). Adanya perbedaan tingkat intensitas pertukaran pada tahap crossover mempengaruhi hasil kerja algoritma genetika.…”

Section: Hasil Dan Pembahasanunclassified

Formasi kelompok dinamis untuk mendukung kolaborasi pembelajaran proyek perangkat lunak

Utomo

Kurniawan

2020

jitp

View full text Add to dashboard Cite

Matakuliah proyek perangkat lunak digunakan untuk melatih mahasiswa dalam penguasaan materi pengembangan perangkat lunak yang terdiri dari analisis, desain, implementasi, dan evaluasi. Mahasiswa diajarkan bagaimana cara mengerjakan perangkat lunak dari tahap awal hingga tahap akhir. Selain itu, mahasiswa juga dilatih untuk bekerja secara tim. Permasalahan yang terdapat pada Universitas Dian Nuswantoro adalah pembentukan kelompok masih dilakukan secara random-select. Pembentukan kelompok yang dilakukan mahasiswa berdasarkan unsur pertemanan, satu komunitas, atau grup di social media seperti WhatsApp. Hasilnya, terjadi ketidakseimbangan di dalam kelompok tim proyek. Ketidakseimbangan tim proyek dapat menyebabkan gagalnya proyek pengembangan perangkat lunak. Penelitian ini mengusulkan eksperimen pendekatan dynamic group formation dengan algoritma genetika. Hasil dari eksperimen menunjukkan bahwa algoritma genetika mampu membantu pembentukan kelompok tim proyek dengan tingkat keberhasilan 87.5% dengan pengaturan inisial populasi adalah 100 populasi dan probabilitas crossover adalah 0.6. Tujuan dari penelitian ini adalah memberikan alternatif pembentukan kelompok mahasiswa secara dinamis guna mendukung kolaborasi tim proyek mahasiswa. Pada proyek perangkat lunak kedepan, tidak ada pembentukan tim proyek secara homogen atau pemilihan anggota tim proyek secara self-select atau random-select. AbstractSoftware project courses are used to train students in mastering software development materials consisting of analysis, design, implementation, and evaluation. Students are taught how to work on software from the initial stage to the final stage. In addition, students are also trained to work in teams. The problem in Universitas Dian Nuswantoro is that group formation is still done randomly-selectively. The formation of groups by students is based on the friendship level, a community, or a group on social media such as WhatsApp. As a result, there is an imbalance in the project team. Imbalance of the project team can cause the failure of software development projects. This study proposes an experiment using a dynamic group formation approach with genetic algorithms. The results of the experiment show that the genetic algorithm is able to help the formation of project team groups with a success rate of 87.5% with the initial population is 100 population and the probability of crossover (pc) is 0.6. The objective of this study is to provide an alternative dynamic formation of student groups to support the collaboration of student project teams. In the future, there is no homogeneous project team formation or selection of member teams using a self-select or random-select method.

show abstract

“…Coello et al introduced the multi-objective particle swarm optimization framework [24] that applies the adaptive grid method to maintain an external archive, change the direction of the particles to keep them from flying out of the search space, and keep the particles within the boundary. Several algorithms based on the evolutionary computations are extensively presented in different domains and applications [35][36][37].…”

Section: Evolutionary Computationmentioning

confidence: 99%

A Grid-Based Swarm Intelligence Algorithm for Privacy-Preserving Data Mining

2019

Self Cite

View full text Add to dashboard Cite

Privacy-preserving data mining (PPDM) has become an interesting and emerging topic in recent years because it helps hide confidential information, while allowing useful knowledge to be discovered at the same time. Data sanitization is a common way to perturb a database, and thus sensitive or confidential information can be hidden. PPDM is not a trivial task and can be concerned an Non-deterministic Polynomial-time (NP)-hard problem. Many algorithms have been studied to derive optimal solutions using the evolutionary process, although most are based on straightforward or single-objective methods used to discover the candidate transactions/items for sanitization. In this paper, we present a multi-objective algorithm using a grid-based method (called GMPSO) to find optimal solutions as candidates for sanitization. The designed GMPSO uses two strategies for updating gbest and pbest during the evolutionary process. Moreover, the pre-large concept is adapted herein to speed up the evolutionary process, and thus multiple database scans during each evolutionary process can be reduced. From the designed GMPSO, multiple Pareto solutions rather than single-objective algorithms can be derived based on Pareto dominance. In addition, the side effects of the sanitization process can be significantly reduced. Experiments have shown that the designed GMPSO achieves better side effects than the previous single-objective algorithm and the NSGA-II-based approach, and the pre-large concept can also help with speeding up the computational cost compared to the NSGA-II-based algorithm.

show abstract

An Effective Approach for Obtaining a Group Trading Strategy Portfolio Using Grouping Genetic Algorithm

Cited by 29 publications

References 30 publications

Optimal Portfolio Management for Engineering Problems Using Nonconvex Cardinality Constraint: A Computing Perspective

Optimal Portfolio Management for Engineering Problems Using Nonconvex Cardinality Constraint: A Computing Perspective

Formasi kelompok dinamis untuk mendukung kolaborasi pembelajaran proyek perangkat lunak

A Grid-Based Swarm Intelligence Algorithm for Privacy-Preserving Data Mining

Contact Info

Product

Resources

About