Redes neurais aplicadas na previsão de índice Sharpe: evidência em componentes do Ibovespa

LIMA, Renan Delgado Camurça

dc.creator	LIMA, Renan Delgado Camurça
dc.date.issued	2023-07-14
dc.identifier.citation	LIMA, Renan Delgado Camurça. Redes neurais aplicadas na previsão de índice Sharpe: evidência em componentes do Ibovespa. 2023. 93 f. Dissertação (Mestrado Profissional em Engenharia de Produção), Instituto de Engenharias Integradas, Universidade Federal de Itajubá (UNIFEI), Campus de Itabira, Minas Gerais, 2023.	pt_BR
dc.identifier.uri	https://repositorio.unifei.edu.br/jspui/handle/123456789/3890
dc.description.abstract	The application of neural network models for selecting assets for investment portfolios using the Sharpe ratio has gained the attention of researchers. This study aims to apply neural networks in predicting the Sharpe ratio for investment portfolio selection. Through a literature review, the methods used in portfolio construction, optimization techniques, and neural network structures employed in this context are identified. The construction of prediction models requires the collection and preprocessing of historical data on financial assets and economic indicators. The portfolio selection process is divided into two stages: optimization based on the Sharpe ratio and portfolio selection using neural networks to predict the portfolio with the best performance in the next period. The performance evaluation is compared to the Bovespa index. The results show that the unconstrained model performs better in terms of optimization time and Sharpe ratio. Neural networks, although not surpassing the portfolios, demonstrate superior performance compared to the Ibovespa index. The LSTM+Bahdanau Attention neural network achieved the best performance. These findings contribute to the advancement of knowledge in the field of finance and highlight the potential of neural networks in investment portfolio selection.	pt_BR
dc.description.sponsorship	Agência 1	pt_BR
dc.language	por	pt_BR
dc.publisher	Universidade Federal de Itajubá	pt_BR
dc.rights	Acesso Aberto	pt_BR
dc.subject	Redes Neurais	pt_BR
dc.subject	Neural Networks	pt_BR
dc.subject	Índice Sharpe	pt_BR
dc.subject	Sharpe Ratio	pt_BR
dc.subject	Otimização de Carteiras	pt_BR
dc.subject	Portfolio Optimization	pt_BR
dc.subject	Mecanismo de Atenção	pt_BR
dc.subject	Attention Mechanism	pt_BR
dc.title	Redes neurais aplicadas na previsão de índice Sharpe: evidência em componentes do Ibovespa	pt_BR
dc.type	Dissertação	pt_BR
dc.date.available	2023-07-14
dc.date.available	2023-09-15T14:12:54Z
dc.date.accessioned	2023-09-15T14:12:54Z
dc.creator.Lattes	http://lattes.cnpq.br/5067476685715711	pt_BR
dc.contributor.advisor1	CARVALHO, Henrique Duarte
dc.contributor.advisor1Lattes	http://lattes.cnpq.br/7552625336212384	pt_BR
dc.contributor.referee1	OLIVEIRA, Leonardo Albergaria de
dc.contributor.referee1Lattes	http://lattes.cnpq.br/1369895191082587	pt_BR
dc.contributor.referee2	LELES, Michel Carlo Rodrigues
dc.contributor.referee2Lattes	http://lattes.cnpq.br/5512914843540140	pt_BR
dc.description.resumo	A aplicação de modelos de redes neurais com intuito de selecionar ativos para carteiras de investimentos com o uso do índice Sharpe é uma área que tem atraído a atenção de pesquisadores. Este estudo tem como objetivo aplicar redes neurais na previsão do índice Sharpe para seleção de carteiras de investimento. Por meio de uma revisão bibliográfica, são identificados os métodos utilizados na construção de carteiras, assim como as técnicas de otimização e as estruturas de redes neurais empregadas nesse contexto. A construção dos modelos de previsão requer a coleta e o tratamento de dados históricos de ativos financeiros e dados econômicos. O processo de seleção de carteiras é dividido em duas etapas: a otimização baseada no índice Sharpe e a seleção da carteira utilizando redes neurais para previsão da carteira com melhor desempenho no período seguinte. A avaliação do desempenho é realizada em comparação com o índice Bovespa. Os resultados mostram que o modelo sem restrições apresenta melhor desempenho em termos de tempo de otimização e índice Sharpe. As redes neurais, embora não superem as carteiras, demonstram desempenho superior ao Ibovespa. A rede neural LSTM+Atenção de Bahdanau obteve o melhor desempenho. Essas descobertas contribuem para o avanço do conhecimento na área de finanças e destacam o potencial das redes neurais na seleção de carteiras de investimento.	pt_BR
dc.publisher.country	Brasil	pt_BR
dc.publisher.department	PPG - Programas de Pós Graduação - Itabira	pt_BR
dc.publisher.program	PPG - Programas de Pós Graduação - Itabira	pt_BR
dc.publisher.initials	UNIFEI	pt_BR
dc.subject.cnpq	CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO	pt_BR
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dc.title.alternative	Neural Networks Applied in Sharpe Ratio Forecasting: Evidence in Ibovespa Components	pt_BR