Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30172
Adaptive Neuro-Fuzzy Inference System for Financial Trading using Intraday Seasonality Observation Model

Authors: A. Kablan


The prediction of financial time series is a very complicated process. If the efficient market hypothesis holds, then the predictability of most financial time series would be a rather controversial issue, due to the fact that the current price contains already all available information in the market. This paper extends the Adaptive Neuro Fuzzy Inference System for High Frequency Trading which is an expert system that is capable of using fuzzy reasoning combined with the pattern recognition capability of neural networks to be used in financial forecasting and trading in high frequency. However, in order to eliminate unnecessary input in the training phase a new event based volatility model was proposed. Taking volatility and the scaling laws of financial time series into consideration has brought about the development of the Intraday Seasonality Observation Model. This new model allows the observation of specific events and seasonalities in data and subsequently removes any unnecessary data. This new event based volatility model provides the ANFIS system with more accurate input and has increased the overall performance of the system.

Keywords: Adaptive Neuro-fuzzy Inference system, High Frequency Trading, Intraday Seasonality Observation Model.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2984


[1] A. Kablan. Adaptive Neuro Fuzzy Inference Systems for High Frequency Financial Trading and Forecasting. Proceedings of The Third International Conference on Advanced Engineering Computing and Applications in Sciences. 2009.
[2] E. F. Fama. Efficient capital markets: A review of theory and empirical work. Journal of Finance, pages 383-417, May 1970.
[3] T. Hellström and K. Holmstrom. Predicting the Stock Market. Technical Report Ima-TOM-1997-07, Center of Mathematical Modeling, Department of Mathematics and Physis, Malardalen University, Vasteras, Sweden, August 1998.
[4] L.C. Ying and M.C. Pan. Using adaptive network based fuzzy inference system to forecast regional electricity loads", Energy Conversion and. Management, Vol. 49, pp. 205-211, 2008.
[5] T. Takagi and M. Sugeno. Fuzzy identification of systems and its application to modeling and control, IEEE Transactions on Systems, Man and Cybernetics, Vol. 15, pp 116-132, 1985.
[6] R. Wilson and R. Sharda. Bankruptcy prediction using neural networks, Decision Support Systems, Vol. 11, pp 545-557, 1994.
[7] Y. Yoon, T. Guimaraes, and G. Swales. Integrating neural networks with rule-based expert systems, Decision Support Systems, Vol. 11 pp 497-507, 1994.
[8] R. Jang. ANFIS: Adaptive network-based fuzzy inference system, IEEE Transactions on Systems, Man and Cybernetics, Vol. 23 (3) pp 665-685,1993.
[9] O. J. Grabbe (1996): International Financial Markets, Englewood Hills, Prentice Hall Inc.
[10] A. V. Dormale, (1997): The Power of Money, Macmillan Press, London.
[11] S. Banik. Modeling chaotic behavior of Dhaka Stock Market Index values using the neuro-fuzzy model, 10th international conference on Computer and information technology ICCIT. Issue , 27-29 Dec. pp 1-6, 2007.
[12] S. Chabaa and A. Zeroual. Predicting Packet Transmission Data over IP Networks Using Adaptive Neuro-Fuzzy Inference Systems. Journal of Computer Science Vol. 5(2), pp 123-130, 2009.
[13] Y. Becerikli, "Optimal Estimation Theory" Lecture Notes, Kocaeli University, Turkey. 2005.
[14] R. Cont. Empirical properties of asset returns: stylized facts and statistical issues. Quantitative Finance pp 223-236. 2001.
[15] M. Dracogna, R. Gencay, U. M├╝ller, and R. Olsen. 2001, An Introduction to High-Frequency Finance, Academic Press.
[16] Murphy J: Technical Analysis of Futures Markets. New York, N.Y.: New York Inst. of Finance, 1986
[17] J. Hull, Incorporating Volatility Updating into the Historical Simulation Method for VaR," Journal of Risk (Fall), Vol. 1, No. 1, 5-19, 1998.
[18] R. Olsen. CCFEA Expert Seminar Series. University of Essex. United Kingdom. 6,13 and 20 March 2008.
[19] Bauwens, L, Ben Omrane, W. Giot, P News Announcements, Market Activity and Volatility in the Euro/Dollar Foreign Exchange Market. Journal of International Money and Finance 24, 1108ÔÇö1125. 2005.
[20] J.B. Glattfelder, A. Dupuis, R.B. Olsen. An extensive set of scaling laws and the FX coastline. Unpublished manuscript. 2008.