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Modelo de previsão de Séries Temporais para previsão do preço das ações da Netflix

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  • De Oliveira Santos, Thalita
  • da Silva, Thaylon Gomes

Abstract

O mercado de ações é uma parte importante de qualquer economia e, por isso, compreendê-lo é objetivo de vários estudos, pois permite que o investidor tome decisões mais firmes e certeiras. Entretanto, realizar previsões de séries financeiras é uma tarefa difícil, uma vez que são compostas de ruídos e apresentam um comportamento bastante errático. Este trabalho faz o uso dos modelos de média móvel integrada autorregressiva e do modelo de média móvel integrada autorregressiva sazonal, para prever o preço de abertura das ações da Netflix na bolsa de valores norte-americana NASDAQ. O Critério de Informação de Akaike foi usado para selecionar o melhor modelo, e o desempenho dos modelos foi analisado através do erro quadrático médio. Depois de selecionar o modelo mais preciso, realizou-se uma comparação das médias dos períodos antes e durante a pandemia. Os resultados obtidos revelam que o modelo ARIMA (0,1,1) foi o que conseguiu realizar previsões mais precisas, e que a pandemia teve um impacto positivo no preço das ações.

Suggested Citation

  • De Oliveira Santos, Thalita & da Silva, Thaylon Gomes, 2022. "Modelo de previsão de Séries Temporais para previsão do preço das ações da Netflix," SocArXiv mc5h2, Center for Open Science.
    • Handle: RePEc:osf:socarx:mc5h2
    DOI: 10.31219/osf.io/mc5h2
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    References listed on IDEAS

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    1. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    2. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
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