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Ulaştırma ve Lojistik Kongreleri

alphanumeric journal

The Journal of Operations Research, Statistics, Econometrics and Management Information Systems

Comparison of the Global, Local and Semi-Local Chaotic Prediction Methods for Stock Markets: The Case of FTSE-100 Index


Ayşe İşi, Ph.D.

Fatih Çemrek, Ph.D.


Chaotic prediction methods are classified as global, local and semi-local methods. In this paper, unlike the studies in the literature, it is aimed to compare all these methods together for stock markets in terms of prediction performance and to determine the best prediction method for stock markets. For this purpose, Multi-Layer Perceptron (MLP) neural networks from global methods, nearest neighbour method from local methods, radial basis functions from semi-local methods are used. The FTSE-100 index is selected to represent the stock market and applied the all methods to these data. The prediction performance is measured in term of root mean square error (RMSE) and normalized mean square error (NMSE). As a result of the analysis; it has been determined that the best prediction method for the FTSE-100 index is the semi-local method. While it is possible to make a maximum of 5 days prediction with global and local methods, it has been determined that up to 20 days prediction can be made with the semi-local prediction methods. The results show that semi-local prediction methods are successful in predicting the behaviour of stock market.

Keywords: Chaotic Prediction, Chaotic Time Series, FTSE-100 Index, Nearest Neighbour Method, Radial Basis Functions Method, Stock Market

Jel Classification: C63

Suggested citation

İşi, A. & Çemrek, F. (). Comparison of the Global, Local and Semi-Local Chaotic Prediction Methods for Stock Markets: The Case of FTSE-100 Index. Alphanumeric Journal, 7(2), 289-300. http://dx.doi.org/10.17093/alphanumeric.629722


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Volume 7, Issue 2, 2019


alphanumeric journal

Volume 7, Issue 2, 2019

Pages 289-300

Received: Aug. 5, 2019

Accepted: Dec. 22, 2019

Published: Dec. 31, 2019

Full Text [557.7 KB]

2019 İşi, A., Çemrek, F.

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