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EBITDA Index Prediction Using Exponential Smoothing and ARIMA Model

Author

Listed:
  • Lihki Rubio

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

  • Alejandro J. Gutiérrez-Rodríguez

    (Faculty of Economic Sciences and Management, Universidad de Ibagué, Ibagué 730002, Colombia)

  • Manuel G. Forero

    (Semillero Lún, Faculty of Engineering, Universidad de Ibagué, Ibagué 730002, Colombia)

Abstract

Forecasting has become essential in different economic sectors for decision making in local and regional policies. Therefore, the aim of this paper is to use and compare performance of two linear models to predict future values of a measure of real profit for a group of companies in the fashion sector, as a financial strategy to determine the economic behavior of this industry. With forecasting purposes, Exponential Smoothing (ES) and autoregressive integrated moving averages (ARIMA) models were used for yearly data. ES and ARIMA models are widely used in statistical methods for time series forecasting. Accuracy metrics were used to select the model with best performance and ES parameters. For the real profit measure of the financial performance of the fashion sector in Colombia EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) was used and was calculated using multiple SQL queries.

Suggested Citation

  • Lihki Rubio & Alejandro J. Gutiérrez-Rodríguez & Manuel G. Forero, 2021. "EBITDA Index Prediction Using Exponential Smoothing and ARIMA Model," Mathematics, MDPI, vol. 9(20), pages 1-14, October.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:20:p:2538-:d:652628
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    References listed on IDEAS

    as
    1. Jan Bouwens & Ties de Kok & Arnt Verriest, 2019. "The Prevalence and Validity of EBITDA as a Performance Measure," ACCRA, Association francophone de comptabilité, vol. 25(1), pages 55-105.
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    3. Chlomou, Grigoria & Demirakos, Efthimios, 2020. "How do financial analysts implement the Sum-of-the-Parts (SOTP) valuation framework?," International Review of Financial Analysis, Elsevier, vol. 70(C).
    4. K. O. Cogger, 1974. "The Optimality of General-Order Exponential Smoothing," Operations Research, INFORMS, vol. 22(4), pages 858-867, August.
    5. Jan Bouwens & Ties de Kok & Arnt Verriest, 2019. "The Prevalence and Validity of EBITDA as a Performance Measure," Comptabilité - Contrôle - Audit, Association francophone de comptabilité, vol. 25(1), pages 55-105.
    6. Emmanuel Sirimal Silva & Hossein Hassani & Dag Øivind Madsen & Liz Gee, 2019. "Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends," Social Sciences, MDPI, vol. 8(4), pages 1-23, April.
    7. Tsan-Ming Choi & Chi-Leung Hui & Yong Yu, 2014. "Introduction: Intelligent Fashion Forecasting," Springer Books, in: Tsan-Ming Choi & Chi-Leung Hui & Yong Yu (ed.), Intelligent Fashion Forecasting Systems: Models and Applications, edition 127, chapter 0, pages 3-8, Springer.
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    Cited by:

    1. Lihki Rubio & Keyla Alba, 2022. "Forecasting Selected Colombian Shares Using a Hybrid ARIMA-SVR Model," Mathematics, MDPI, vol. 10(13), pages 1-21, June.
    2. Ji Feng & Jiashuang Huang & Chang Guo & Zhenquan Shi, 2024. "MSA-GCN: Multistage Spatio-Temporal Aggregation Graph Convolutional Networks for Traffic Flow Prediction," Mathematics, MDPI, vol. 12(21), pages 1-29, October.
    3. Ivan Borisov Todorov & Fernando Sánchez Lasheras, 2023. "Stock Price Forecasting of IBEX35 Companies in the Petroleum, Electricity, and Gas Industries," Energies, MDPI, vol. 16(9), pages 1-21, April.
    4. 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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