Ensaios sobre o uso de redes neurais na previsão de taxa de câmbio

Imagem de Miniatura
Tipo
Tese
Data
2020-04-24
Autores
Costa, Marisa Gomes da
Orientador
Basso, Leonardo Fernando Cruz
Título da Revista
ISSN da Revista
Título de Volume
Membros da banca
Hadad Junior, Eli
Mendonça, Diogo de Prince
Jucá, Michele Nascimento
Kimura, Herbert
Programa
Administração de Empresas
Resumo
This study aims to compare and evaluate the predictive power of artificial neural network models on exchange rates. Initially, a bibliometric study and literature review is carried out in order to identify the current research status in the area. Then, an empirical study is propesed to forecast various Exchange rates using data of opening, closing,high and low in daily frequency. The data sample includes exchange rates (BRL / USD, EUR / USD and GBP / USD) from January 2014 to December 2019. Forecasts are made for a period ahead. Different architectures of the LSTM recurrent neural network model were tested. To rank the models in terms of predictive power, the results of the predictions are compared to the prediction of the random walk model, using it as a benchmark, as well as ARIMA. The selection of models is made by the model confidence set (MCS). Lunde and Nason. The results indicated that the LSTM model is superior to the random walk and ARIMA for all analyzed currencies.
Descrição
Palavras-chave
confidence set model , aprendizagem de máquina , LSTM , redes neurais recorrentes , temas emergentes
Citação
COSTA, Marisa Gomes da. Ensaios sobre o uso de redes neurais na previsão de taxa de câmbio. 2020. 92 f. Tese (Doutorado em Administração de Empresas) - Universidade Presbiteriana Mackenzie, São Paulo, 2020.