The purpose of this paper is to consider the potential in the projection of
Fuzzy logic and Neural networks, also to make some combination between
models to address implementation issues in the prediction of index and prices
for Amman stock exchange in different models, where the previous researchers
have to demonstrate the differences between these measures. We have
used in this research Amman stock Exchange index prices data as a sample set
to compare the different application models, where predicting the stock market
was very difficult since it depends on nonstationary financial data, in addition
to the most of the models are nonlinear systems. These papers draw an
existing academic and practitioner in literature review as a combination of
these models and compare them, the facilities of the development of conceptual
methods and the research proposition are the basis for serving this combination.
Hence, the present and recent papers can serve the further researchers
into addressing contemporary barriers in the direction of these researchers.
The authors show in this paper the Fuzzy logic and Neural networks, in
addition to time series analysis through these models, utilized of RSI, OS,
MACD, and OBV, then using MSE, MAPE, and RMSE. The research implication
represents of too much data for the period of study, also this paper is
conceptual in its nature, the paper highlights in finding that the implementation
challenges, and how these challenges can facilitate the trader decision in
the stock market. The results of the analysis show that the ANFIS is the better
model to achieve prediction of stock market more than others. When are
MAPE and RMSE the best more than simulating the errors in other methods?
Also the fuzzy-neural models as the results of table show that more prominent
in fuzzy-neural models ,while it appears that in MSE as medium, MAD posses less amount than other models in all table testing fuzzy-neural models, therefore,
it becomes superior in stock prediction.
References
[1]
Partha, R., Sanjay, S. and Kowar, M.K. (2012) Fuzzy Candlestick Approach to Trade S & P CNX, NIFTY 50 Index Using Engulfing Patterns. International Journal of Hybrid Information Technology, 5, 57-66.
[2]
Hiemstra, Y. (1994) A Stock Market Forecasting Support System Based on Fuzzy Logic. Proceedings of the IEEE 27th Annual Hawaii International Conference on System, Sciences, Wailea, HI, 4-7 January 1994, 281-287.
https://doi.org/10.1109/HICSS.1994.323343
[3]
Nicholis, S.C. and Sumpter, D.J.T. (2011) A Dynamical Approach to Stock Market Fluctuation. International Journal of Bifurcation and Chaos, 21, 3557-564.
https://doi.org/10.1142/S0218127411030726
[4]
Preethi, G. and Santhi, B. (2012) Stock Market Forecasting Techniques: A Survey. Journal of Theoretical and Applied Information Technology, 46, 24-30.
[5]
Chong, T. (2009) Financial Time Series Forecasting Using Improved Wavelet ANN. Master Thesis Report. (Not Published)
[6]
Dhamija, A.K. (2010) Financial Time Series Forecasting: Comparison of ANN and ARCH Model. International Research Journal of Finance, No. 49, 194-212.
[7]
Zadeh, L.A. (1965) Fuzzy Sets. Information and Control, 8, 338-353.
https://doi.org/10.1016/S0019-9958(65)90241-X
[8]
Zadeh, L.A. (1975). The Concept of a Linguistic Variable and Its Application to Approximate Reasoning I. Information Science, 8, 199-249.
https://doi.org/10.1016/0020-0255(75)90036-5
[9]
Abraham, A. and Nath, B. (2001) Hybrid Intelligent Systems Design—A Review of a Decade of Research. Technical Report (5/2000), Gippsland School of Computing and Information Technology, Monash University, Australia.
[10]
Abraham, A. (2001) Neuro-Fuzzy Systems: State-Of-The-Art Modeling Techniques, In: Mira, J. and Prieto, A., Eds., Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence, Springer-Verlag Germany, Granada, Spain, 269-276. https://doi.org/10.1007/3-540-45720-8_30
[11]
Thammano, A. (1999) Neuro-Fuzzy Model for Stock Market Prediction. In: Dagli, C.H., Buczak, A.L., Ghosh, J., Embrechts, M.J. and Ersoy, O., Eds., Smart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Data Mining, and Complex Systems, Proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE ‘99). ASME Press, New York, 587-591.
[12]
Kim, K.-J. and Han, I. (2000) Genetic Algorithms Approach to Feature Discretization in Artificial Neural Networks for the Prediction of Stock Price Index. Expert Systems with Applications, 19, 125-132.
https://doi.org/10.1016/S0957-4174(00)00027-0
[13]
Egeli, B., Ozturan, M. and Badur, B. (2003) Stock Market Prediction Using Artificial Neural Networks. Proceedings of the 3rd International Conference on Business, Hawaii, 18-21 June 2003, 1-8.
[14]
Singh, K.K., Dimri, P. and Singh, J.N. (2014) Green Data Base Management System for Intermediaries of the Indian Stock Market. IEEE Xplore Digital Library, 1-5.
[15]
White, H. (2010) Economic Prediction Using Neural Networks: The Case of IBM Daily Stock Returns. Department of Economics University of California, San Diego.
[16]
Dashore, P. and Jain, S. (2010) Fuzzy Rule Based Expert System to Represent Uncertain Knowledge of E-Commerce. International Journal of Computer Theory and Engineering, 2, 882-886.
[17]
Huang, K.Y. and Jane, C.-J. (2009) A Hybrid Model for Stock Market Forecasting and Portfolio Selection Based on ARX, Grey System and RS Theories. Expert Systems with Applications, 36, 5387-5392. https://doi.org/10.1016/j.eswa.2008.06.103
[18]
Feng, L., Baowen, L., Boris, P., Tobias, P. and Eugene, H. (2012) Linking Agent-Based Models and Stochastic Models of a Financial Market. Proceeding of the National Academy of Sciences of the United State of America, 109, 8388-8393.
https://doi.org/10.1073/pnas.1205013109
[19]
Kim, M.J., Min, S.H. and Han, I.G. (2006) An Evolutionary Approach to the Combination of Multiple Classifiers to Predict a Stock Price Index. Expert Systems with Applications, 31, 241-247. https://doi.org/10.1016/j.eswa.2005.09.020
[20]
Mate, A.A. (2014) Scheduling By Using Fuzzy Logic in Manufacturing. International Journal of Engineering Research and Applications, 4, 104-111.
[21]
Alejandro, E., Juliano, M. and Sebastian, M. (2013) A Technical Analysis Indicator Based on Fuzzy Logic. Electronic Notes on Theoretical Computer Science, 292, 27-37. https://doi.org/10.1016/j.entcs.2013.02.003
