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Title: Stock trading and prediction using back-propagation neural network
Authors: Wang, Xiaogang
Keywords: DRNTU::Engineering
Issue Date: 2014
Abstract: 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.
Schools: School of Electrical and Electronic Engineering 
Research Centres: Centre for Computational Intelligence 
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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