Pfizer Stock Price Prediction Based on Extreme Learning Machine

Lim Eng Aik; Tan Wee Choon; Mohd Syafarudy Abu

Lim Eng Aik Universiti Malaysia Perlis
Tan Wee Choon
Mohd Syafarudy Abu

Abstract

Nowadays, an Extreme learning machine (ELM) is known to be a fast learning algorithm of single-hidden layer feedforward neural network (SLFNs), and overcomes the disadvantages of the classical learning algorithm in neural network methods multiple iterations, huge search space and a large number of calculations, only needs to set the appropriate numbers of hidden layer nodes, assigns the weight of input and deviation of hidden layers without iteration. Research shows that the stock market is a very complex nonlinear system, which requires artificial intelligence theory, statistics theory and economic theory to study the stock price forecast. In this paper, ELM is introduced in predicting the stock price of Pfizer company, and by comparing it with SVM and BP, we analyze its feasibility and advantage in stock price prediction. The experiment results show that ELM is of high accuracy of prediction and apparent advantages in parameter selection and learning speed．

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