Stock Prediction Using FCMAC-BYY

Authors:
Jiacai Fu;Kok Siong Lum;Minh Nhut Nguyen;Juan Shi
Affiliations:
Research Centre of Automation, Heilongjiang Institute of Science and Technology, Harbin, China;School of Computer Engineering, Nanyang Technological University, 639798, Singapore;School of Computer Engineering, Nanyang Technological University, 639798, Singapore;Research Centre of Automation, Heilongjiang Institute of Science and Technology, Harbin, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

Citing 4
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Credit assigned CMAC and its application to online learning robust controllers

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FCMAC-BYY: Fuzzy CMAC Using Bayesian Ying–Yang Learning

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Learning convergence of CMAC technique

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Advances on BYY harmony learning: information theoretic perspective, generalized projection geometry, and independent factor autodetermination

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Abstract

The increasing reliance on Computational Intelligence applications to predict stock market positions have resulted in numerous researches in financial forecasting and trading trend identifications. Stock market price prediction applications are required to be adaptive to new incoming data as well as have fast learning capabilities due to the volatility nature of market movements. This paper analyses stock market price prediction based on a Fuzzy Cerebellar Model Articulation Controller --- Bayesian Ying Yang (FCMAC-BYY) neural network. The model is motivated from the Chinese ancient Ying-Yang philosophy which states that everything in the universe can be viewed as a product of a constant conflict between opposites, Ying and Yang. A perfect status is reached if Ying and Yang achieves harmony. The analyzed experiment on a set of real stock market data (Singapore Airlines Ltd --- SIA) in the Singapore Stock Exchange (SGX) and Ibex35 stock index shows the effectiveness of the FCMAC-BYY in the universal approximation and prediction.