Análise de métodos de tratamento de outliers para predição dos retornos de índices de ações negociados em bolsa
Resumo
Definidos como pontos extremos, os outliers são observações que não seguem um comportamento padrão em uma série temporal. Contudo, esta anomalia pode levar a especificação incorreta do modelo, estimativas viesadas dos parâmetros e previsões de baixa acurácia. Apesar de ser visto como um erro de medida, nas séries financeiras os outliers carregam informações relevantes sobre a dinâmica do mercado acionário e de fatores interrelacionados. Diante disso, este artigo propõe uma análise comparativa do desempenho de técnicas de tratamento de outliers nas previsões dos índices de ações. Os resultados apontam que métodos com suavização apresentam melhor desempenho, corroborando com a hipótese de que os outliers possuem conteúdo informacional relevante para a previsão da dinâmica dos índices de ações.
Palavras-chave:
Outliers, Modelo de predição, Séries Temporais, Índice de preço de ações, Rolling Origin
Referências
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R.J. Hyndman and Y. Khandakar. Automatic time series forecasting: The forecast package for r. Journal of Statistical Software, 27(3):1–22, 2008.
Rob J. Hyndman and George Athanasopoulos. Forecasting: principles and practice. OTexts, May 2018. ISBN 978-0-9875071-1-2.
Wolfgang Härdle. Applied Nonparametric Regression. Cambridge University Press, 1990. ISBN 978-0-521-42950-4.
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E.H. Lee, C.Wickham, P.A. Beedlow, R.S.Waschmann, and D.T. Tingey. A likelihood-based time series modeling approach for application in dendrochronology to examine the growth-climate relations and forest disturbance history. Dendrochronologia, 45:132–144, 2017.
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Robert H. Shumway and David S. Stoffer. Time Series Analysis and Its Applications: With R Examples. Springer, April 2017. ISBN 978-3-319-52452-8.
Turan G. Bali, Robert F. Engle, and Scott Murray. Empirical Asset Pricing: The Cross Section of Stock Returns. John Wiley & Sons, April 2016. ISBN 978-1-118-09504-1.
Les Clewlow and Chris Strickland. Energy Derivatives: Pricing and Risk Management. Lacima Publications, 2000. ISBN 978-0-9538896-0-0.
A. Davydenko and R. Fildes. Measuring Forecasting Accuracy: The Case Of Judgmental Adjustments To Sku-Level Demand Forecasts. International Journal of Forecasting, 29(3):510–522, 2013.
R.J. Hodrick and E.C. Prescott. Postwar U.S business cycles: An empirical investigation. Journal of Money, Credit and Banking, 29(1):1–16, 1997.
R.J. Hyndman and Y. Khandakar. Automatic time series forecasting: The forecast package for r. Journal of Statistical Software, 27(3):1–22, 2008.
Rob J. Hyndman and George Athanasopoulos. Forecasting: principles and practice. OTexts, May 2018. ISBN 978-0-9875071-1-2.
Wolfgang Härdle. Applied Nonparametric Regression. Cambridge University Press, 1990. ISBN 978-0-521-42950-4.
Hemanth P. Kumar and Basavaraj S. Patil. Forecasting volatility trend of INR USD currency pair with deep learning LSTM techniques. In Proceedings 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018, pages 91–97, 2018.
E.H. Lee, C.Wickham, P.A. Beedlow, R.S.Waschmann, and D.T. Tingey. A likelihood-based time series modeling approach for application in dendrochronology to examine the growth-climate relations and forest disturbance history. Dendrochronologia, 45:132–144, 2017.
J. Liu, Q. Li, W. Chen, Y. Yan, Y. Qiu, and T. Cao. Remaining useful life prediction of PEMFC based on long short-term memory recurrent neural networks. International Journal of Hydrogen Energy, pages 5470–5480, 2019.
A. Pimenta, C.A.L. Nametala, F.G. Guimarães, and E.G. Carrano. An Automated Investing Method for Stock Market Based on Multiobjective Genetic Programming. Computational Economics, 52(1):125–144, 2018.
Robert H. Shumway and David S. Stoffer. Time Series Analysis and Its Applications: With R Examples. Springer, April 2017. ISBN 978-3-319-52452-8.
Publicado
04/10/2021
Como Citar
GEA, Cristiane; LIMA, Janio; BEZERRA, Eduardo; OGASAWARA, Eduardo.
Análise de métodos de tratamento de outliers para predição dos retornos de índices de ações negociados em bolsa. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 36. , 2021, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 277-282.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2021.17885.
