A time-varying stock portfolio selection model based on optimized PSO-BiLSTM and multi-objective mathematical programming under budget constraints

Choosing the optimal portfolio is an ongoing challenging research area and a complex process involving selecting the best investment plan according to various factors, such as investor preferences for expected return, risk, and duration of investments. Although various methods have been presented so...

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Veröffentlicht in:	Neural computing & applications Jg. 35; H. 25; S. 18445 - 18470
Hauptverfasser:	Vaziri, Jalil, Farid, Dariush, Nazemi Ardakani, Mehdi, Hosseini Bamakan, Seyed Mojtaba, Shahlaei, MohammadAli
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.09.2023 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Budgets Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Image Processing and Computer Vision Indicators Investments Machine learning Mathematical analysis Mathematical models Mathematical programming Methodology Multiple objective analysis Original Article Particle swarm optimization Pricing Probability and Statistics in Computer Science Recurrent neural networks Time series Stock selection Portfolio optimization Multi-objective mathematical planning model Budget constraints Artificial intelligence
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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