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Diagonal Log-Normal Generalized RBF Neural Network for Stock Price Prediction

  • Wenli Zheng
  • Jinwen MaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8866)

Abstract

Stock price prediction is one of the most important topics in financial engineering. In this paper, for stock closing price prediction, we propose a diagonal log-normal generalized RBF neural network in which the diagonal log-normal density functions serve as the RBFs. Specifically, it utilizes the dynamic split-and-merge EM algorithm to select the number of hidden units (or RBFs) as well as the initial values of the parameters, and implements a synchronous LMS learning algorithm for parameter learning. It is demonstrated by the experiments that the diagonal log-normal generalized RBF neural network has a competitive performance on stock closing price prediction.

Keywords

RBF neural network Log-normal distributions EM algorithm Stock price prediction 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Information Science, School of Mathematical Sciences and LMAMPeking UniversityBeijingChina

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