Stock trading using RBF neural networks

Stock trading using RBF neural networks

Stock market comprises of complex sample of data in time series. It has unique characteristics like non-linearity, high noise and uncertainties. In order to gain profit, prediction of stock price becomes a hot topic all the time. According to the characteristics of financial time series, BP neura...

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Main Author: Hu, Donglin
Other Authors: Wang Lipo
Format: Final Year Project
Language:English
Published: 2016
Subjects:
DRNTU::Engineering
Online Access:http://hdl.handle.net/10356/68014
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-680142023-07-07T16:32:27Z Stock trading using RBF neural networks Hu, Donglin Wang Lipo School of Electrical and Electronic Engineering DRNTU::Engineering Stock market comprises of complex sample of data in time series. It has unique characteristics like non-linearity, high noise and uncertainties. In order to gain profit, prediction of stock price becomes a hot topic all the time. According to the characteristics of financial time series, BP neural network prediction model with the minimum standard of empirical risk has poor generalization ability, which easy to fall into the optimal and disadvantages of local presence, we come up with RBF neural network. Bachelor of Engineering 2016-05-24T03:07:15Z 2016-05-24T03:07:15Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/68014 en Nanyang Technological University 63 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering
spellingShingle DRNTU::Engineering
Hu, Donglin
Stock trading using RBF neural networks
description Stock market comprises of complex sample of data in time series. It has unique characteristics like non-linearity, high noise and uncertainties. In order to gain profit, prediction of stock price becomes a hot topic all the time. According to the characteristics of financial time series, BP neural network prediction model with the minimum standard of empirical risk has poor generalization ability, which easy to fall into the optimal and disadvantages of local presence, we come up with RBF neural network.
author2 Wang Lipo
author_facet Wang Lipo
Hu, Donglin
format Final Year Project
author Hu, Donglin
author_sort Hu, Donglin
title Stock trading using RBF neural networks
title_short Stock trading using RBF neural networks
title_full Stock trading using RBF neural networks
title_fullStr Stock trading using RBF neural networks
title_full_unstemmed Stock trading using RBF neural networks
title_sort stock trading using rbf neural networks
publishDate 2016
url http://hdl.handle.net/10356/68014
_version_ 1772826209210597376

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