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Year 2017, Volume: 4 Issue: 3, 331 - 341, 30.09.2017
https://doi.org/10.17261/Pressacademia.2017.700

Abstract

References

  • Aghababaeyan, R. et al. (2011). Forecasting the Tehran Stock Market by artificial neural network. International Journal of Advanced Computer Science and Applications, Special Issue on Artificial Intelligence.
  • Yakut, E., Elmas, B., & Yavuz, S. (2014). Yapay Sinir Ağları ve Destek Vektör Makineleri. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 19(1).
  • Box, G. E. P. ve Jenkins, G. M., Time Series Analysis, Forecasting and Control, Holden Day, San Francisco, 1976.
  • Fernández, A. and S. Gómez (2007). Portfolio selection using neural networks. Computers & Operations Research, 34(4): 1177-1191.
  • Gershenfeld, N. A. (1999). The nature of mathematical modeling. Cambridge university press.
  • Gujarati, D. N. (1995). Basic Econometrics 3rd edition, New York: Me Graw Hill. Gupta KL (1970),'Personal savings in developing nations. Further evidence', the economic record, 46, 243-249.
  • Guresen, E., Kayakutlu, G., & Daim, T. U. (2011). Using artificial neural network models in stock market index prediction. Expert Systems with Applications, 38(8), 10389-10397.
  • Hamzaçebi, c. (2011). Yapay Sinir Ağları:tahmin amaçlı kullanımı MATLAB ve Neurosolutions uygulamalı. Ekin Basım Yayın Dağıtım, 2011
  • Khashei, M., Hejazi, S.R., Bijari, M., A new hybrid artificial neural networks and fuzzy regression model for time series forecasting, Fuzzy Sets and Systems 159, 769 – 786, 2008.
  • Khashei, M., Bijari, M., A novel hybridization of artificial neural networks and ARIMA models for time series forecasting, Applied Soft Computing 11, 2664–2675, 2011
  • Khashei, M., & Bijari, M. (2010). An artificial neural network (p, d, q) model for timeseries forecasting. Expert Systems with applications, 37(1), 479-489.
  • Kuncheva, L. and Whitaker, C., Measures of diversity in classifier ensembles, Machine Learning, 51, pp. 181-207, 2003
  • J. J. García Adeva, Ulises Cerviño, and R. Calvo, CLEI Journal, Vol. 8, No. 2, pp. 1 - 12, December 2005.
  • Lawrence, R. (1997). Using neural networks to forecast stock market prices. University of Manitoba.
  • Oliveira, M., & Torgo, L. (2014). Ensembles for Time Series Forecasting. In Proceedings of the Sixth Asian Conference on Machine Learning (pp. 360-370).
  • Opitz, D.; Maclin, R. (1999). "Popular ensemble methods: An empirical study". Journal of Artificial Intelligence Research 11: 169–198.
  • Pai, P. F., & Lin, C. S. (2004). A hybrid ARIMA and support vector machines model in stock price forecasting. Omega, 33(6), 497-505.
  • Rokach, L. (2010). "Ensemble-based classifiers". Artificial Intelligence Review 33 (1-2)
  • Rojas, R. (2013). Neural networks: a systematic introduction. Springer Science & Business Media.
  • Slutsky, E.,(1937) “The Summation of Random Causes As The Source of Cyclic Processes”.
  • Sutheebanjard, P., & Premchaiswadi, W. (2010). Forecastıng The Thaıland Stock Market Usıng Evolutıon Strategıes. Asian Academy of Management Journal of Accounting & Finance, 6(2).
  • Yule, G.U., “On a Method of Investigating Periodicities in Disturbed Series with Special Reference to Wölfer’s Sunspot Numbers”, Phil. Trans., A226, 267, 1927.
  • Wang, L., Zou, H., Su, J., Li, L., & Chaudhry, S. (2013). An ARIMA‐ANN hybrid model for time series forecasting. Systems Research and Behavioral Science, 30(3), 244-259.
  • Wold, H.O., A Study in The Analysis of Stationary Time Series, Almquist and Wicksell, Uppsala, 1954.
  • Yudong, Z., & Lenan, W. (2009). Stock market prediction of S& P 500 via combination of improved BCO approach and BP neural network. Expert Systems with Applications, 36, 8849–8854
  • Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175.

ANALYSIS OF FINANCIAL TIME SERIES WITH MODEL HYBRIDIZATION

Year 2017, Volume: 4 Issue: 3, 331 - 341, 30.09.2017
https://doi.org/10.17261/Pressacademia.2017.700

Abstract

Purpose- The aim of this study is to obtain better estimation results by hybridizing
models that reveal linear and nonlinear relationships used in the intended
financial time series. 

Methodology- ARIMA and Artificial Neural Networks (ANN) models were used in estimating
NASDAQ stock market index values between 03.01.2012 and 30.06.2017 comparison
of hybrid model results with different ways of error determination in literature.

Findings- ARIMA residues have been tested different models where only
residues are used with basic indications, only residues and basic. The
calculation of residues was done separately with the addition and multiplication
function. These residues were modeled with ANN, and the obtained results are
collected and established hybrid model with ARIMA forecasts. When the results
obtained at the end of the operations are compared, it is seen that the product
function of some of the addition functions gives better results in some models.







Conclusion- The
hybridization of the ANN and NASDAQ index estimates with the ARIMA method
resulted in processing for both addition and multiplication functions. Residues
calculated with the addition model showed better results in ANN hybrid. What
variables are used to calculate residuals is that the hybrid model gives better
estimation results than single models.

References

  • Aghababaeyan, R. et al. (2011). Forecasting the Tehran Stock Market by artificial neural network. International Journal of Advanced Computer Science and Applications, Special Issue on Artificial Intelligence.
  • Yakut, E., Elmas, B., & Yavuz, S. (2014). Yapay Sinir Ağları ve Destek Vektör Makineleri. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 19(1).
  • Box, G. E. P. ve Jenkins, G. M., Time Series Analysis, Forecasting and Control, Holden Day, San Francisco, 1976.
  • Fernández, A. and S. Gómez (2007). Portfolio selection using neural networks. Computers & Operations Research, 34(4): 1177-1191.
  • Gershenfeld, N. A. (1999). The nature of mathematical modeling. Cambridge university press.
  • Gujarati, D. N. (1995). Basic Econometrics 3rd edition, New York: Me Graw Hill. Gupta KL (1970),'Personal savings in developing nations. Further evidence', the economic record, 46, 243-249.
  • Guresen, E., Kayakutlu, G., & Daim, T. U. (2011). Using artificial neural network models in stock market index prediction. Expert Systems with Applications, 38(8), 10389-10397.
  • Hamzaçebi, c. (2011). Yapay Sinir Ağları:tahmin amaçlı kullanımı MATLAB ve Neurosolutions uygulamalı. Ekin Basım Yayın Dağıtım, 2011
  • Khashei, M., Hejazi, S.R., Bijari, M., A new hybrid artificial neural networks and fuzzy regression model for time series forecasting, Fuzzy Sets and Systems 159, 769 – 786, 2008.
  • Khashei, M., Bijari, M., A novel hybridization of artificial neural networks and ARIMA models for time series forecasting, Applied Soft Computing 11, 2664–2675, 2011
  • Khashei, M., & Bijari, M. (2010). An artificial neural network (p, d, q) model for timeseries forecasting. Expert Systems with applications, 37(1), 479-489.
  • Kuncheva, L. and Whitaker, C., Measures of diversity in classifier ensembles, Machine Learning, 51, pp. 181-207, 2003
  • J. J. García Adeva, Ulises Cerviño, and R. Calvo, CLEI Journal, Vol. 8, No. 2, pp. 1 - 12, December 2005.
  • Lawrence, R. (1997). Using neural networks to forecast stock market prices. University of Manitoba.
  • Oliveira, M., & Torgo, L. (2014). Ensembles for Time Series Forecasting. In Proceedings of the Sixth Asian Conference on Machine Learning (pp. 360-370).
  • Opitz, D.; Maclin, R. (1999). "Popular ensemble methods: An empirical study". Journal of Artificial Intelligence Research 11: 169–198.
  • Pai, P. F., & Lin, C. S. (2004). A hybrid ARIMA and support vector machines model in stock price forecasting. Omega, 33(6), 497-505.
  • Rokach, L. (2010). "Ensemble-based classifiers". Artificial Intelligence Review 33 (1-2)
  • Rojas, R. (2013). Neural networks: a systematic introduction. Springer Science & Business Media.
  • Slutsky, E.,(1937) “The Summation of Random Causes As The Source of Cyclic Processes”.
  • Sutheebanjard, P., & Premchaiswadi, W. (2010). Forecastıng The Thaıland Stock Market Usıng Evolutıon Strategıes. Asian Academy of Management Journal of Accounting & Finance, 6(2).
  • Yule, G.U., “On a Method of Investigating Periodicities in Disturbed Series with Special Reference to Wölfer’s Sunspot Numbers”, Phil. Trans., A226, 267, 1927.
  • Wang, L., Zou, H., Su, J., Li, L., & Chaudhry, S. (2013). An ARIMA‐ANN hybrid model for time series forecasting. Systems Research and Behavioral Science, 30(3), 244-259.
  • Wold, H.O., A Study in The Analysis of Stationary Time Series, Almquist and Wicksell, Uppsala, 1954.
  • Yudong, Z., & Lenan, W. (2009). Stock market prediction of S& P 500 via combination of improved BCO approach and BP neural network. Expert Systems with Applications, 36, 8849–8854
  • Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175.
There are 26 citations in total.

Details

Journal Section Articles
Authors

Huseyin Ince This is me

Fatma Sonmez Cakir

Publication Date September 30, 2017
Published in Issue Year 2017 Volume: 4 Issue: 3

Cite

APA Ince, H., & Sonmez Cakir, F. (2017). ANALYSIS OF FINANCIAL TIME SERIES WITH MODEL HYBRIDIZATION. Journal of Economics Finance and Accounting, 4(3), 331-341. https://doi.org/10.17261/Pressacademia.2017.700

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