Comparison of Performance of Different Functions in Functional Link Artificial Neural Network: A Case Study on Stock Index Forecasting

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 31)

Abstract

The rapid growth of world economy and globalization has been attracting researchers to develop intelligent forecasting models for stock market prediction. In order to forecasting the stock market trend efficiently, this paper developed four single layer low complex forecasting models known as functional link artificial neural network (FLANN). Different basis functions such as Trigonometric, Chebysheb, Legendre and Lagurre polynomials are used for functional expansion of input signals to achieve higher dimensionality. The models are termed as TFLANN, CFLANN, LeFLANN and LFLANN respectively. The weight and bias vectors are optimized by genetic algorithm (GA). The number of functional expansion for each models are optimized by GA during the training process instead of fixing it earlier, which is the novelty of this research work. The models are employed to forecast the one-day-ahead prediction of three fast growing global stock markets. Different types of FLANN are considered and their comparative performance is investigated.

Keywords

Stock market forecasting Functional link artificial neural network Genetic algorithm Chebysheb polynomial Legendre polynomial 

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Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Veer Surendra Sai University of TechnologyBurlaIndia
  2. 2.Silicon Institute of TechnologyBhubaneswarIndia

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