ARIMA: An Applied Time Series Forecasting Model for the Bovespa Stock Index

Paulo Rotela Junior, Fernando Luiz Riêra Salomon, Edson de Oliveira Pamplona

Institute of Production Engineering and Management, Federal University of Itajuba, Itajuba, Brazil.

DOI: 10.4236/am.2014.521315                

Abstract

Due to the relative uncertainty involved with the variables which affect financial market behavior, forecasting future variations in a time series of the Brazilian stock market Index (Ibovespa) can be considered a difficult task. This article aims to evaluate the performance of the model ARIMA for time series forecasting of Ibovespa. The research method utilized was mathematical modeling and followed the Box-Jenkins method. In order to compare results with other smoothing models, the parameter of evaluation MAPE (Mean Absolute Percentage Error) was used. The results showed that the model utilized obtained lower MAPE values, thus indicating greater suitability. This therefore demonstrates that the ARIMA model can be used for time-series indices related to stock market index forecasting.

Keywords

Forecasting, ARIMA, Time Series, MAPE, Ibovespa

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Rotela, J.P., Pamplona, E.O. and Salomon, F.R. (2014) Otimização de Portfólios: Análise de Eficiência. RAE—Revista de Administração de Empresas, 54, 405-413. http://dx.doi.org/10.1590/S0034-759020140406
[2]	Markowitz, H. (1952) Portfolio Selection. Journal of Finance, 7, 77-91.
[3]	Sharpe, W.F. (1963) A Simplified Model for Portfolio Analysis. Management Science, 9, 277-293.
[4]	Fama, E. (1970) Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance, 25, 383-417. http://dx.doi.org/10.2307/2325486
[5]	Slack, M., Chambers, S. and Johnston, R. (2007) Operations Management. Prentice Hall/Financial Times, Upper Saddle River.
[6]	Linstone, H.A. and Turoof, M. (1975) The Delphi Method: Techniques and Applications. Addison-Wesley, Boston.
[7]	Gaither, N. and Frazier, G. (2001) Operations Management. South-Western, Ohio.
[8]	Bacci, L.A. (2007) Combinação de métodos de séries temporais para a previsão da demanda de café no Brasil. Thesis, UNIFEI, Itajuba.
[9]	Morettin, P.A and Toloi, C.M. (2006) Análise de Séries Temporais—2a Edição Revista e Ampliada. 2nd Edition, Editora Edgar Blucher, São Paulo.
[10]	Morettin, P.A. and Toloi, C.M. (1987) Previsão de séries temporais. 2nd Edition, Atual Editora, São Paulo.
[11]	Pindyck, R.S. and Rubinfeld, D.L. (2008) Microeconomics. 7th Edition, Prentice Hall, Upper Saddle River.
[12]	Makridakis, S., Wheelwrigt, S.C. and Hyndman, R.J. (1998) Forecasting: Methods and Applications. 3rd Edition, John Wiley & Sons, New York.
[13]	Fava, V.L. (2000) Metodologia de Box-Jenkins para Modelos Univariados. In: Vasconcellos, M.A.S. and Alves, D., Eds., Manual de econometria: nível intermediário, Atlas, São Paulo, 205-232.
[14]	Bertrand, J. and Fransoo, J. (2002) Operations Management Research Methodologies Using Quantitative Modeling. International Journal of Operations & Production Management, 22, 241-264. http://dx.doi.org/10.1108/01443570210414338
[15]	Makridakis, S., Wheelwrigt, S.C. and Mcgee, V. (1983) Forecasting: Methods and Applications. 2nd Edition, John Wiley & Sons, New York.