Improving portfolio optimization using weighted link prediction in dynamic stock networks (2019)
- Authors:
- Autor USP: CARVALHO, ANDRÉ CARLOS PONCE DE LEON FERREIRA DE - ICMC
- Unidade: ICMC
- DOI: 10.1007/978-3-030-22744-9_27
- Subjects: APRENDIZADO COMPUTACIONAL; MÉTODOS ESTATÍSTICOS PARA APRENDIZAGEM; ALGORITMOS ÚTEIS E ESPECÍFICOS; BOLSA DE VALORES
- Keywords: Stock market; Dynamic stock networks; Portfolio optimization
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Lecture Notes in Computer Science
- ISSN: 0302-9743
- Volume/Número/Paginação/Ano: v. 11538, p. 340-353, 2019
- Conference titles: International Conference on Computational Science - ICCS
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
CASTILHO, Douglas et al. Improving portfolio optimization using weighted link prediction in dynamic stock networks. Lecture Notes in Computer Science. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-22744-9_27. Acesso em: 02 maio 2024. , 2019 -
APA
Castilho, D., Gama, J., Mundim, L. R., & Carvalho, A. C. P. de L. F. de. (2019). Improving portfolio optimization using weighted link prediction in dynamic stock networks. Lecture Notes in Computer Science. Cham: Springer. doi:10.1007/978-3-030-22744-9_27 -
NLM
Castilho D, Gama J, Mundim LR, Carvalho ACP de LF de. Improving portfolio optimization using weighted link prediction in dynamic stock networks [Internet]. Lecture Notes in Computer Science. 2019 ; 11538 340-353.[citado 2024 maio 02 ] Available from: https://doi.org/10.1007/978-3-030-22744-9_27 -
Vancouver
Castilho D, Gama J, Mundim LR, Carvalho ACP de LF de. Improving portfolio optimization using weighted link prediction in dynamic stock networks [Internet]. Lecture Notes in Computer Science. 2019 ; 11538 340-353.[citado 2024 maio 02 ] Available from: https://doi.org/10.1007/978-3-030-22744-9_27 - Reduction strategies for hierarchical multi-label classification in protein function prediction
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Informações sobre o DOI: 10.1007/978-3-030-22744-9_27 (Fonte: oaDOI API)
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