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Sugeno-Type Fuzzy Inference Model for Stock Price Prediction

by Uduak A. Umoh, Alfred A. Udosen
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 103 - Number 3
Year of Publication: 2014
Authors: Uduak A. Umoh, Alfred A. Udosen
10.5120/18051-8957

Uduak A. Umoh, Alfred A. Udosen . Sugeno-Type Fuzzy Inference Model for Stock Price Prediction. International Journal of Computer Applications. 103, 3 ( October 2014), 1-12. DOI=10.5120/18051-8957

@article{ 10.5120/18051-8957,
author = { Uduak A. Umoh, Alfred A. Udosen },
title = { Sugeno-Type Fuzzy Inference Model for Stock Price Prediction },
journal = { International Journal of Computer Applications },
issue_date = { October 2014 },
volume = { 103 },
number = { 3 },
month = { October },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume103/number3/18051-8957/ },
doi = { 10.5120/18051-8957 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:34:53.557853+05:30
%A Uduak A. Umoh
%A Alfred A. Udosen
%T Sugeno-Type Fuzzy Inference Model for Stock Price Prediction
%J International Journal of Computer Applications
%@ 0975-8887
%V 103
%N 3
%P 1-12
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The operations of the prediction of stock price are complex and risky due to fluctuation in the stock market because of the vagueness, incompleteness, and uncertainty of the information used. However, it is therefore as a matter of necessity to seek to foresee stock prices because traders need to know when to invest in order to get the maximum return of the investment. This paper proposes a Sugeno-type fuzzy inference system for stock price prediction using technical indicators as its input values. Knowledge Base, Fuzzification, Inference Engine and Defuzzification are the essential components of our model. We explore Sugeno-type fuzzy inference engine to optimize the estimated result. We evaluate the degree of participation of each input parameter with Trapezoidal membership function. Center of Gravity technique is employed for defuzzification. We employ object oriented design tool to model our database. MATLAB and fuzzy relational database are used in the implementation of our study. The development of this system is based on the selection of stock data history which are studied and used for training the system. This system provides vital support to stock traders, researchers and other financial experts in making decisions as regards stock trading.

References
  1. Ching, L. S. , Jyh C. , and Shih, M. Y. (2010). International Journal of Education and Information Technologies, 4(3).
  2. Nassim, H. and Ali, A. (2011). Stock price prediction using a fusion model of wavelet, fuzzy logic and ANN. International Conference on E-business, Management and Economics. IPEDR, 25. IACSIT Press, Singapore.
  3. Hemanth, K. P, Prashanth, K. B. , Nirmala, T. V. , Basavaraj S. P. (2012). Neuro Fuzzy based Techniques for Predicting Stock Trends. IJCSI International Journal of Computer Science 9, 4(3), 1694-0814.
  4. 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(5), 8849–8854.
  5. Boyacioglu, M. A. & Avci, D. (2010). An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange. Expert Systems with Applications 37. 7908–7912.
  6. Zadeh, L. A. (1965). Fuzzy Sets. Information and Control 8, 338–353.
  7. Chang, P. C. , & Liu, C. H. (2008). A TSK type fuzzy rule based system for stock price prediction. Expert Systems with Applications, 34(1), 135–144.
  8. Mohammadian, M. and Kingham, M. (2004). An adaptive hierarchical fuzzy logic system for modelling of financial systems. International Journal of Intelligent Systems in Accounting, Finance and Management, 12(1), 61–82.
  9. Hiemstra, Y. (1994). A stock market forecasting support system based on fuzzy logic. In Proceedings of the Twenty-Seventh Hawaii International Conference on System Sciences, 3, 281–287.
  10. Cheung, W. M. and Kaymak, U. (2012). A Fuzzy Logic Based Trading System. Ching, L. S. , Jyh C. , and Shih, M. Y. (2010). International Journal of Education and Information Technologies. 3, 4.
  11. Abbasi, E. & Abouec, A. (2008). Stock price forecast by using neuro-fuzzy inference system. Proceedings of World Academy of Science, Engineering and Technology, 36, 320–323.
  12. Sureshkumar, K. K. & Elango, N. M. (2012). Performance Analysis of Stock Price Prediction using Artificial Neural Network, Global Journal of Computer Science and Technology (GJCST), 12(1), 2-9.
  13. Ahmad, S. M, Gayar, N, Elazim, H. A (2006). A Fuzzy Engine Model for Efficient Stock Market Prediction Proceedings of the 5th WSEAS Int. Conf. on COMPUTATIONAL INTELLIGENCE, MAN-MACHINE SYSTEMS AND CYBERNETICS, Venice, Italy, November 20-22, 2006 217
  14. Saxena, N. Hotchandani, Y. Kulshrestha, M. Arora, P. (2010). Stock Market Expert: Analyzing Stock Prices Proceedings of the 4th National Conference; INDIACom-2010 Computing For Nation Development, February 25 – 26, 2010 Bharati Vidyapeeth's Institute of Computer Applications and Management, New Delhi
  15. Kasemsan, M. L. and Radeerom, M. (2011). Intelligence Trading System for Thai Stock Index Based on Fuzzy Logic and Neuro-fuzzy System. Proceedings of the World Congress on Engineering and Computer Science 2011, I. San Francisco, USA.
  16. Feng, H. M. and Chou, H. C. (2011). Evolutional RBFNs prediction systems generation in the applications of financial time series data, Expert Systems with Applications, 38, 8285-8292.
  17. Abiyev, R. H. Abiyev, V. H. (2012). Differential Evaluation Learning of Fuzzy Wavelet Neural Networks for Stock Price Prediction, Journal of Information and Computing Science, 7(2), 121-130.
  18. ElAaL, M. M. A. , Selim, G. and Fakhr, W. (2012). Stock Market Trend Prediction Model for the Egyptian Stock Market Using Neural Networks and Fuzzy Logic. D. -S. Huang et al. (Eds. ): ICIC 2011, LNBI 6840, 85–90.
  19. Jandaghi, G. , Reza Tehrani, R. Hosseinpour, D. Gholipour, R. and Shadkam, S. A. S. , (2012)Application of Fuzzy-neural networks in multi-ahead forecast of stock price, African Journal of Business Management 4(6), 903-914.
  20. Prasanna, S. and Ezhilmaran, D. (2013), An analysis on Stock Market Prediction using Data Mining Techniques. International Journal of Computer Science & Engineering Technology (IJCSET), 4(3), 49-51.
  21. Umoh, U. A, Nwachukwu, E. O. and Obot, O. U. (2010). Fuzzy Rule-based Framework for Effective Control of Profitability in a Paper Recycling Plant. Global Journal of Computer Science and Technology (GJCST), 10, 56-67.
  22. Umoh, U. A, Nwachukwu, E. O. and Okure, O. U. (2011). "Fuzzy-Neural Network Model for Effective Control of Profitability in a Paper Recycling Plant". American Journal of Scientific and Industrial Research (AJSIR). 2(4): 552-558.
  23. Pantazopoulos, K. N. , Tsoukalas, L. H. , Bourbakis, N. G. , Brun, M. J. and Houstis, E. N. (1998). Financial prediction and trading strategies using neurofuzzy approaches. IEEE Transactions on Systems, Man and Cybernetics, Part B, 28(4), 520–531. Publishing Company, Boston.
  24. Takagi, T. and M. Sugeno, "Fuzzy identification of systems and its applications to modeling and control," IEEE Trans. Systems, Man, and Cybernetics, 15, 116–132, 1985.
  25. Guney, K. (2009). Comparison of Mamdani And Sugeno Fuzzy Inference System Models For Resonant Frequency Calculation Of Rectangular Microstrip Antennas. Progress In Electromagnetics Research B, 12, 81–104.
  26. Achelis, S. B. (2000). Technical Analysis from A to Z, Library of Congress Cataloging- in-Publication Data, ISBN 0-07-136348-3. McGraw Hill.
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy Logic Stock Price Technical indicators Trapezoidal membership function Object Oriented Tool.

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