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Forecasting Selected Colombian Shares Using a Hybrid ARIMA-SVR Model

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  • Lihki Rubio

    (Department of Mathematics and Statistics, Universidad del Norte, Barranquilla 080001, Colombia)

  • Keyla Alba

    (Department of Mathematics and Statistics, Universidad del Norte, Barranquilla 080001, Colombia)

Abstract

Forecasting future values of Colombian companies traded on the New York Stock Exchange is a daily challenge for investors, due to these stocks’ high volatility. There are several forecasting models for forecasting time series data, such as the autoregressive integrated moving average (ARIMA) model, which has been considered the most-used regression model in time series prediction for the last four decades, although the ARIMA model cannot estimate non-linear regression behavior caused by high volatility in the time series. In addition, the support vector regression (SVR) model is a pioneering machine learning approach for solving nonlinear regression estimation procedures. For this reason, this paper proposes using a hybrid model benefiting from ARIMA and support vector regression (SVR) models to forecast daily and cumulative returns of selected Colombian companies. For testing purposes, close prices of Bancolombia, Ecopetrol, Tecnoglass, and Grupo Aval were used; these are relevant Colombian organizations quoted on the New York Stock Exchange (NYSE).

Suggested Citation

  • Lihki Rubio & Keyla Alba, 2022. "Forecasting Selected Colombian Shares Using a Hybrid ARIMA-SVR Model," Mathematics, MDPI, vol. 10(13), pages 1-21, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2181-:d:845473
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    References listed on IDEAS

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    2. Tiago E. Pratas & Filipe R. Ramos & Lihki Rubio, 2023. "Forecasting bitcoin volatility: exploring the potential of deep learning," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(2), pages 285-305, June.
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    5. Luzia, Ruan & Rubio, Lihki & Velasquez, Carlos E., 2023. "Sensitivity analysis for forecasting Brazilian electricity demand using artificial neural networks and hybrid models based on Autoregressive Integrated Moving Average," Energy, Elsevier, vol. 274(C).

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