Abstract: Extreme learning machine (ELM) is a new learning algorithm of single-hidden layer feed-forward neural network (SLFNs), and overcomes the disadvantages of the classical learning algorithm in neural network method's 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, we need to use 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, and by comparing 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 obvious advantages in parameter selection and learning speed.

Key words: extreme learning machine, stock price, prediction model, support vector machine, neural network