• ISSN: 2148-2225 (online)

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.


Abstract

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

bibtex

References

  • Abarbanel, H. (1996). Analysis Of Observed Chaotic Data. New York: Spinger-Verlag.
  • Abarbanel, H. D., Brown, R., Kadtke, J. B. (1990). “Prediction in chaotic nonlinear systems: methods for time series with broadband fourier spectra”. Physical Review A, 41(4), 1782-1807.
  • Abhyankar, A., Copeland, L. S., Wong, W. (1995). “Nonlinear dynamics in real-time equity market indices: evidence from the United Kingdom”. The Economic Journal, 864-880.
  • Brock, W. A., Hsieh, D. A., Lebaron, B. D. (1991). Nonlinear Dynamics, Chaos and Instability: Statistical Theory and Economic Evidence. MIT Press.
  • Casdagli, M. (1989). “Nonlinear prediction of chaotic time series”. Physica D: Nonlinear Phenomena, 35(3), 335-356.
  • Chan, K.S., Tong, H. (2001). Chaos: A Statistical Perspective, New York: Springer-Verlag.
  • Eckmann, J. P., Ruelle, D. (1985). “Ergodic theory of chaos and strange attractors”. Reviews of Modern Physics, 57(3), 617-656.
  • Eldridge, R. M., Coleman, M. P. (1993). “The British FTSE-100 Index: Chaotically Deterministic or Random”. Working Paper, School of Business, Fairfield University.
  • Elshorbagy, A., Simonovic, S. P., Panu, U. S. (2002). “Estimation of missing streamflow data using principles of chaos theory”. Journal of Hydrology, 255(1), 123-133.
  • Elsner, J.B. (1992). “Predicting time series using a neural network as a method of distinguishing chaos from noise”. Journal of Physics A: Mathematical and General, 25(4) 843–850.
  • Farmer, J.D., Sidorowich, J.J. (1987). “Predicting chaotic time series”. Physical Review Letters, 59(8), 845-848.
  • Fraedrich, K. (1986). “Estimating the dimensions of weather and climate attractors”. Journal of The Atmospheric Sciences, 43(5), 419-432.
  • Fraser, A. M., Swinney, H. L. (1986). “Independent coordinates for strange attractors from mutual information”. Physical Reviews A, 33, 1134-1140.
  • Grassberger, P., Procaccia, I. (1983a). “Characterization of strange attractors”. Physical Review Letters, 50(5), 346-349.
  • Grassberger, P., Procaccia, I. (1983b). “Measuring the strangeness of strange attractors”. Physica D: Nonlinear Phenomena, 9(1-2), 189-208.
  • Guegan, D., Mercier, L. (2005). “Prediction in chaotic time series: methods and comparisons with an application to financial intra-day data”. The European Journal Of Finance, 11(2), 137-150.
  • Hanias M., Magafas L., Konstantaki P. (2013). “Nonlinear analysis of S&P index”. Equilibrium Quarterly Journal of Economics and Economic Policy, 8(4), 125-135.
  • Hassan M.R., Nath, B. (2005). “Stock market forecasting using Hidden Markov Model: A New Approach”. Proceedings of the 5th International Conference on Intelligent Systems Design and Applications, 192–196.
  • Hsieh, D.A. (1991). “Chaos and nonlinear dynamics: Application to financial markets”. Journal of Finance, 46(5), 1839–1877.
  • Huang, S. C., Chuang, P. J., Wu, C. F., Lai, H. J. (2010). “Chaos-based support vector regressions for exchange rate forecasting”. Expert Systems with Applications, 37(12), 8590-8598.
  • Kantz, H. (1994). “A robust method to estimate the maximal Lyapunov exponent of a time series”. Physics Letters A, 185(1), 77-87.
  • Kantz, H., Schreiber, T. (2004). Nonlinear Time Series Analysis. UK: Cambridge University Press.
  • Karunasinghe, D.S., Liong, S.Y. (2006). “Chaotic time series prediction with a global model: artificial neural network”. Journal of Hydrology, 323(1), 92-105.
  • Kazem, A., Sharifi, E., Hussain, F. K., Saberi, M., Hussain, O. K. (2013). “Support vector regression with chaos-based firefly algorithm for stock market price forecasting”. Applied Soft Computing, 13(2), 947-958.
  • Kennel, M.B., Brown, R., Abarbanel, H.D.I. (1992). “Determining embedding dimension for phase-space reconstruction using a geometrical construction”. Physical Review A, 45(6), 3403-3411.
  • Lapedes, A., Farber, R., (1987). Nonlinear Signal Processing Using Neural Networks: Prediction and System Modelling. Technical Report, Los Alamos National Laboratory, Los Alamos, NM.
  • Lillekjendlie, B., Kugiumtzis, D., Christophersen, N. (1994). “Chaotic time series part II: System identification and prediction”. Modeling, Identification And Control, 15, 225-243.
  • Mayfield, E. S., Mizrach, B. (1992). “On determining the dimension of real-time stock-price data”. Journal of Business and Economic Statistics, 10(3), 367-374.
  • Özdemir, S. D., Akgül, I. (2014). “Hisse senedi piyasalarının kaotik yapısı ve yapay sinir ağları ile öngörüsü: İMKB-100 örneği”. İktisat İşletme ve Finans, 29 (336), 31-58.
  • Shang, P., Li, X., Kamae, S. (2005). “Chaotic analysis of traffic time series”. Chaos, Solitons and Fractals, 25(1), 121-128.
  • Sivakumar, B., Jayawardena, A. W., Fernando, T. M. K. G. (2002). “River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches”. Journal of Hydrology, 265(1), 225-245.
  • Smaoui, N. (1999). “An artificial neural network noise reduction method for chaotic attractors”. International Journal of Computer Mathematics, 73(4), 417-431.
  • Sprott, J. C. (2003). Chaos and Time Series Analysis. Oxford Press.
  • Sprott, J. C. (2010). Elegant Chaos: Algebraically Simple Chaotic Flows. World Scientific.
  • Takens, F., (1981). in Dynamical Systems and Turbulence, Warwick Vol. 898 of Lecture Notes in Mathematics, edited by D.A. Rand, L. S. Young ,Springer, Berlin.
  • Vaidyanathan, R., Krehbiel, T. (1992). “Does the S&P 500 futures mispricing series exhibit nonlinear dependence across time?”. Journal of Futures Markets, 12(6), 659-677.
  • Webel, K. (2012). “Chaos in German stock returns-new evidence from the 0–1 test”. Economics Letters, 115(3), 487-489.
  • Wolf, A., Swift, J. B., Swinney, H. L., Vastano, J. A. (1985). “Determining Lyapunov exponents from a time series”. Physica D: Nonlinear Phenomena,16(3), 285-317.
  • Xiaofeng, G., Lai, C. H. (1999). “Improvement of the local prediction of chaotic time series”. Physical Review E, 60(5), 5463-5468.

Volume 7, Issue 2, 2019

2019.07.02.STAT.01

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.

This is an Open Access article, licensed under Creative Commons Attribution-NonCommercial 4.0 International License.

Creative Commons Attribution licence

scan QR code to access this article from your mobile device

Contact Us

Faculty of Transportation and Logistics, Istanbul University
Beyazit Campus 34452 Fatih/Istanbul/TURKEY

Bahadır Fatih Yıldırım, Ph.D.
editor@alphanumericjournal.com
+ 90 (212) 440 00 00 - 13219

alphanumeric journal

alphanumeric journal has been publishing as "International Peer-Reviewed Journal" every six months since 2013. alphanumeric serves as a vehicle for researchers and practitioners in the field of quantitative methods, and is enabling a process of sharing in all fields related to the operations research, statistics, econometrics and management informations systems in order to enhance the quality on a globe scale.