Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science

Research on Prediction Model of Stock Price Based on LM-BP Neural Network

Authors
Feng Li
Corresponding Author
Feng Li
Available Online May 2014.
DOI
10.2991/lemcs-14.2014.177How to use a DOI?
Keywords
LM-BP neural network; stock price; prediction
Abstract

LM (Levenberg-Marquardt) algorithm is an optimization algorithm for the overall situation which is appropriate for neural network training. It is assumed that standard BP neural network tends to reach the local minimum instead of the optimization of the whole, demand much more training resulting in the low learning efficiency and low rate of convergence. At the same time, it seems easily to forget former sample. To solve the above disadvantages, this paper intends to form prediction model of stock price based on LM- BP neural network to forecast stock price, as well as compared with the prediction result carried out by standard BP neural network. At the end, this paper achieves the satisfactory result.

Copyright
© 2014, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science
Series
Advances in Intelligent Systems Research
Publication Date
May 2014
ISBN
978-94-6252-010-3
ISSN
1951-6851
DOI
10.2991/lemcs-14.2014.177How to use a DOI?
Copyright
© 2014, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Feng Li
PY  - 2014/05
DA  - 2014/05
TI  - Research on Prediction Model of Stock Price Based on LM-BP Neural Network
BT  - Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science
PB  - Atlantis Press
SP  - 776
EP  - 778
SN  - 1951-6851
UR  - https://doi.org/10.2991/lemcs-14.2014.177
DO  - 10.2991/lemcs-14.2014.177
ID  - Li2014/05
ER  -