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Journals / Öneri Dergisi / 2007 / Cilt: 7 - Sayı: 28

A COMPARISION OF NEURAL NETWORK AND LINEAR REGRESSION FORECASTS OF THE ISE-100 INDEX

Murat ÇİNKO
Emin AVCI
Pages
301–307
Publication Date
2007-06-10
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Relying the findings of this study, it is concluded that the artificial neural network model is better than stepwise linear regression.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In recent years the artificial neural network models have been successfully applied to solve many the real life problems.
📊
Key Findings & Data:This study examines the comparison of artificial neural network models and stepwise linear regression forecasting the daily and sessional returns of the ISE-100 index.
💡
Conclusion & Impact:Relying the findings of this study, it is concluded that the artificial neural network model is better than stepwise linear regression.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Artificial Neural Network Models · Stock Market Forecasting

Özet

In recent years the artificial neural network models have been successfully applied to solve many the real life problems. Especially for the last decade, the artificial neural network models have been applied to solve financial problems like bankruptcy prediction, portfolio construction, credit assessments and stock market forecasting. This study examines the comparison of artificial neural network models and stepwise linear regression forecasting the daily and sessional returns of the ISE-100 index. By using stepwise regression inputs is selected tlıen the same inputs is used in the neural network. Both methods are compared on the basis of mean squared error, normalized mean squared error and trend accuracy measures. Relying the findings of this study, it is concluded that the artificial neural network model is better than stepwise linear regression.

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Cite This Article

ÇİNKO, M., AVCI, E. (2007). A COMPARISION OF NEURAL NETWORK AND LINEAR REGRESSION FORECASTS OF THE ISE-100 INDEX. Öneri Dergisi, Cilt: 7 - Sayı: 28, 301–307. https://ajindex.com/articles/35927

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References (16)

  1. Brock, P.L; Hinich, M. & Patterson, D. (1988). Bispectral- Based Tests for the Detection of Gaussianity and Linearity in Time Series. Journal of American Statisiical Association, 83(403), 657-664
  2. Abhyankar, A.; Copeland, L.S. & Wong, W. (1997). Uncovering Nonlinear Structure in Real-Time Stock Market Indexes: The S&p500, the DAX, The Nikke225, and the FTSE-100. Journal of Business and Economic Statistics, 15(1), 1-14
  3. Qi, M. (1999). Nonlinear Predicability of Stock Returns Using Financial and Economic Variables. Journal of Business and Economics Statistics, 17(4), 419-429
  4. Menezes, L.M. & Nikolaev, N.Y. (2006) Forecasting with Genetically Programmed Polynomial Neural Networks. International Journal of Forecasting, 22(2), 249-265
  5. Sewell, S.P.; Stansell, S.R.; Lee, I. & Pan, M.S. (1993). Nonlinearities in Emerging Foreign Capital Markets. Journal of Business Finance & Accounting, 20(2), 237- 248
  6. Kondak, N. (1998). The Effıcient Market Hypothesis Revisited: Some Evidence from the İstanbul Stock Exchange. Capital Market Board Publication, No: 83. Ankara
  7. Harris, R.D.F. & Kucukozmen, C.C. (2001). Linear and Nonlinear Dependence in Turkish Equity Returns and Its Consequences for Financial Risk Management European Journal of Operational Research, 134(3), 481-492
  8. Çinko, M. (2001). Nonlinearity Tests for İstanbul Stock Exchange
  9. Ulusal Ekonometri ve İstatistik Sempozyumu. 19-22 September. Adana
  10. Desai, V.S. & Bharati, R. (1998). A Comparison of Linear Regression and Neural Network Methods for Predicting Excess Returns on Large Stocks. Annals of Operations Research, 78(1-4), 127-163
  11. Hill, T.; Marquez, L.; O’Connor, M. & Remus, W. (1994). Artificial Neural Network Models for Forecasting and Decision Making. International Journal of Forecasting, 10(1), 5-15
  12. Zhang, G.P. (2001). An Investigation of Neural Networks for Linear Time-Series Forecasting. Computers & Operational Research, 28(12), 1183-1202
  13. McCulloch, W.S. & Pitts, W. (1943). A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of MathematicalBiophysics, 5(4), 115-133
  14. White, H. (1988). Economic Prediction Using Neural Networks: The Case of IBM Daily Stock Returns. Proceedings of the 2nd IEEE International Conference on NeuralNetworks, 24-27 July, San Diego, 451-458
  15. Wong, F.S.; Wang, P.Z.; Goh, T.H. & Quek, B.K. (1992). Fuzzy Neural Systems for Stock Selection. Financial Analysts Journal, 48(1), 47-52
  16. Kryzanowski, L