Stock trading and prediction using back-propagation neural network
In this paper, Back-Propagation neural network is used to make prediction on stock price. Theories of neural network, back propagation, and Levenberg-Marquardt algorithm are discussed to obtain a deeper understanding into the paper. Then, variable input information including basic information, techn...
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sg-ntu-dr.10356-612382023-07-07T16:53:36Z Stock trading and prediction using back-propagation neural network Wang, Xiaogang Wang Lipo School of Electrical and Electronic Engineering Centre for Computational Intelligence DRNTU::Engineering In this paper, Back-Propagation neural network is used to make prediction on stock price. Theories of neural network, back propagation, and Levenberg-Marquardt algorithm are discussed to obtain a deeper understanding into the paper. Then, variable input information including basic information, technical indicators and index indicators are investigated to find the most robust input combinations. The impact of neural network architecture is also covered. The neural network with best performance is later tested on 8 other companies to evaluate its profit ability. In the end, the experiment obtained a promising result and proved Back-Propagation neural network’s capacity in stock prediction. Bachelor of Engineering 2014-06-06T06:27:48Z 2014-06-06T06:27:48Z 2014 2014 Final Year Project (FYP) http://hdl.handle.net/10356/61238 en Nanyang Technological University 61 p. application/pdf |
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DRNTU::Engineering Wang, Xiaogang Stock trading and prediction using back-propagation neural network |
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In this paper, Back-Propagation neural network is used to make prediction on stock price. Theories of neural network, back propagation, and Levenberg-Marquardt algorithm are discussed to obtain a deeper understanding into the paper. Then, variable input information including basic information, technical indicators and index indicators are investigated to find the most robust input combinations. The impact of neural network architecture is also covered. The neural network with best performance is later tested on 8 other companies to evaluate its profit ability. In the end, the experiment obtained a promising result and proved Back-Propagation neural network’s capacity in stock prediction. |
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Wang Lipo |
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Wang Lipo Wang, Xiaogang |
| format |
Final Year Project |
| author |
Wang, Xiaogang |
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Wang, Xiaogang |
| title |
Stock trading and prediction using back-propagation neural network |
| title_short |
Stock trading and prediction using back-propagation neural network |
| title_full |
Stock trading and prediction using back-propagation neural network |
| title_fullStr |
Stock trading and prediction using back-propagation neural network |
| title_full_unstemmed |
Stock trading and prediction using back-propagation neural network |
| title_sort |
stock trading and prediction using back-propagation neural network |
| publishDate |
2014 |
| url |
http://hdl.handle.net/10356/61238 |
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1772828878164721664 |