Analysis of outliers treatment methods for predicting returns on stock indices traded on the stock exchange
Abstract
Defined as extreme points, outliers are observations that do not follow a standard behavior in a time series. However, this anomaly can lead to incorrect model specifications, biased parameter estimates, and low accuracy predictions. Despite being seen as a measurement error, in financial series, outliers carry relevant information about the stock market dynamics and interrelated factors. Therefore, this article proposes a comparative analysis of performing outliers treatment techniques in stock index forecasts. The results show that methods with smoothing perform better, corroborating the hypothesis that outliers have relevant informational content for predicting the dynamics of stock prices.
Keywords:
Outliers, Prediction Models, Time Series, Stock indexes, Rolling Origin
References
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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.
Published
2021-10-04
How to Cite
GEA, Cristiane; LIMA, Janio; BEZERRA, Eduardo; OGASAWARA, Eduardo.
Analysis of outliers treatment methods for predicting returns on stock indices traded on the stock exchange. In: BRAZILIAN SYMPOSIUM ON DATABASES (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.
