Using back propagation network, multivariate adaptive regression splines, and autoregressive integrated moving average to design forecasting models for stock price.

碩士 === 輔仁大學 === 管理學研究所 === 96 === Investing in stock market is the most popular and easiest way for investors. Everybody knows trading, but not all can make profit. As Taiwanese stock market doesn’t lie in the scope of efficient markets hypothesis, so investors can use reliable forecasting tools in...

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Main Authors:	Lu Yueh-Hsia, 盧月霞
Other Authors:	Lee Tian-Shyug
Format:	Others
Language:	zh-TW
Published:	2010
Online Access:	http://ndltd.ncl.edu.tw/handle/06346670509726255886

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spelling	ndltd-TW-096FJU004570292015-10-13T18:25:53Z http://ndltd.ncl.edu.tw/handle/06346670509726255886 Using back propagation network, multivariate adaptive regression splines, and autoregressive integrated moving average to design forecasting models for stock price. 運用倒傳遞類神經網路、多元適應性雲形迴歸模型及自我相關整合移動平均建構個股股價預測模式－以台積電、日月光為例 Lu Yueh-Hsia 盧月霞碩士輔仁大學管理學研究所 96 Investing in stock market is the most popular and easiest way for investors. Everybody knows trading, but not all can make profit. As Taiwanese stock market doesn’t lie in the scope of efficient markets hypothesis, so investors can use reliable forecasting tools in predicting the trend and variation of stock prices. The purpose of this paper is to investigate the stock market forecasting capability of backpropagation neural network (BPN), multivariate adaptive regression splines (MARS), ARIMA, and hybrid MARS+BPN forecasting techniques. In order to evaluate the performance of the four proposed forecasting models, two public companies listed in Taiwan Stock Market are adopted as illustrative examples. The empirical results indicate that MARS and BPN provide better forecasting results in terms of several performance criteria. Besides, the obtained basis functions of MARS forecasting method can provide useful information for better investment decisions. Lee Tian-Shyug 李天行 2010 學位論文 ; thesis 71 zh-TW
collection	NDLTD
language	zh-TW
format	Others
sources	NDLTD
description	碩士 === 輔仁大學 === 管理學研究所 === 96 === Investing in stock market is the most popular and easiest way for investors. Everybody knows trading, but not all can make profit. As Taiwanese stock market doesn’t lie in the scope of efficient markets hypothesis, so investors can use reliable forecasting tools in predicting the trend and variation of stock prices. The purpose of this paper is to investigate the stock market forecasting capability of backpropagation neural network (BPN), multivariate adaptive regression splines (MARS), ARIMA, and hybrid MARS+BPN forecasting techniques. In order to evaluate the performance of the four proposed forecasting models, two public companies listed in Taiwan Stock Market are adopted as illustrative examples. The empirical results indicate that MARS and BPN provide better forecasting results in terms of several performance criteria. Besides, the obtained basis functions of MARS forecasting method can provide useful information for better investment decisions.
author2	Lee Tian-Shyug
author_facet	Lee Tian-Shyug Lu Yueh-Hsia 盧月霞
author	Lu Yueh-Hsia 盧月霞
spellingShingle	Lu Yueh-Hsia 盧月霞 Using back propagation network, multivariate adaptive regression splines, and autoregressive integrated moving average to design forecasting models for stock price.
author_sort	Lu Yueh-Hsia
title	Using back propagation network, multivariate adaptive regression splines, and autoregressive integrated moving average to design forecasting models for stock price.
title_short	Using back propagation network, multivariate adaptive regression splines, and autoregressive integrated moving average to design forecasting models for stock price.
title_full	Using back propagation network, multivariate adaptive regression splines, and autoregressive integrated moving average to design forecasting models for stock price.
title_fullStr	Using back propagation network, multivariate adaptive regression splines, and autoregressive integrated moving average to design forecasting models for stock price.
title_full_unstemmed	Using back propagation network, multivariate adaptive regression splines, and autoregressive integrated moving average to design forecasting models for stock price.
title_sort	using back propagation network, multivariate adaptive regression splines, and autoregressive integrated moving average to design forecasting models for stock price.
publishDate	2010
url	http://ndltd.ncl.edu.tw/handle/06346670509726255886
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Using back propagation network, multivariate adaptive regression splines, and autoregressive integrated moving average to design forecasting models for stock price.

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