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...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| التنسيق: | Final Year Project |
| اللغة: | English |
| منشور في: |
2014
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/10356/61238 |
| الوسوم: |
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| الملخص: | 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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