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題名：類神經網路於香港跨境臺灣ETF股價預期能力之研究
書刊名：國立虎尾科技大學學報
作者：賴建成／林育俊
作者(外文)：Lai, Chien-cheng／Lin, Yu-jun
出版日期：2010
卷期：29:2
頁次：頁9-20
主題關鍵詞：跨境掛牌；類神經網路；ETF；Cross-border listed；Neural network
原始連結：連回原系統網址new window
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  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
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  • 共同引用共同引用:3
  • 點閱點閱:56
本研究利用倒傳遞演算法爲架構的前饋式類神經網路，以香港掛牌ETF及香港跨境台灣掛牌ETF爲研究標的，探討其輸入變數香港恒生指數前一日收盤價是否有「價格發現」功能，以提供給國內外投資人作爲決策輔助系統以獲取較佳投資績效。因爲香港股票市場收盤時間爲16:00，而台灣股票市場收盤時間13:30，造成香港收盤時間較晚，所以本研究依據相同的輸入變數香港恒生指數前一日收盤價，分別與輸出變數香港掛牌ETF當日開盤價、香港跨境台灣掛牌ETF當日開盤價兩者互相比較驗證，是否仍具有相同股價預期能力。實證結果顯示，香港恒生指數前一日收盤價對香港掛牌ETF當日開盤價預期能力較對香港跨境台灣掛牌ETF當日開盤價預期能力來得好，這也表示香港恒生指數前一日收盤價對香港掛牌ETF當日開盤價相較於對香港跨境台灣掛牌ETF當日開盤價有價格發現功能。
The study uses the back-propagation algorithm for the structure of feed-forward neural network, The purpose of this research focuses on the Hong Kong ETF listed in Hong Kong and the Hong Kong ETF cross-border listed in Taiwan, and explore its input (forecast) variables, whether there is "price discovery" function, to provide for domestic and foreign investors as a decision-support systems to obtain better investment performance. Because Hong Kong stock market's closing time is 16:00, while Taiwan stock market's closing time is 13:30, resulting in Hong Kong to close later. Therefore this study was based on the same input (forecast) variables, respectively, and output (target) variables Hong Kong ETF listed in Hong Kong and the Hong Kong's ETF cross-listed in Taiwan to compare the two verifications, the expected stock price still has the same capacity. The empirical results show that neural network is expected to verify the ability of Hong Kong ETF listed in Hong Kong stock price forecast capacity was better than in Hong Kong ETF cross-border Listed in Taiwan stock price forecast capacity.
期刊論文
1.陳安斌、許育嘉(200401)。整合小波轉換與神經網路於金融投資決策時間序列預測之研究。資訊管理學報，11(1)，139-165。new window  延伸查詢new window
2.歐宏杰(2003)。Financial World。國際投資月刊。  延伸查詢new window
3.Milidiu R. L.、Machado, R. J.、Rentera, R. P.(1999)。Time-series forecasting through wavelet transform and a mixture of expert models。Neurocomputing，28，145-146。  new window
4.Mizuno, H.、Kosaka, M.、Yajima, H.、Komoda, N.(1998)。Appliction of Neural Network to Technical Analysis of Stock Market Prediction。Studies in Informatics and Control，7(3)，111-120。  new window
5.Tsai D. M.、Chiang C .H.(2003)。Automatic Band Selection for Wavelet Reconstruction in The Application of Defect Detection。Image and Vision Computing，21，413-431。  new window
6.Assume, A.、Campbell, J.、Murtage, F.(1998)。Wavelet-based Feature Extraction and Decomposition Strategies for Financial Forecasting。Journal of Computational Intelligence in Finance，6(2)，5-12。  new window
7.Tang, Z.、Fishwick, P. A.(1993)。Feedforward Neural Nets as Models for Time Series Forecasting。ORSA Journal on Computing，5(4)，374-385。  new window
8.Hiraki, T.、Maberly, E. D.、Akezawa, N. T.(1995)。The Information Content of End-of-the-Day Index Futures Returns: International Evidence form the Osaka Nikkei 225 Futures Contract。Journal of Banking and Finance，19(5)，921-936。  new window
9.Amihud, Y.、Mendelson, H.(1990)。Volatility, Efficiency and Trading: Evidence From the Japanese Stock Market。The Journal of Finance，46(5)，1765-1789。  new window
10.游淑禎(19980000)。類神經網路應用於臺灣股市預測：統合基本面與技術面資訊。證券市場發展季刊，10(3)=39，97-134。new window  延伸查詢new window
11.Cao, Liangyue、Hong, Yiguang、Zhao, Hanzhang、Deng, Shuhui(1996)。Predicting economic time series using a nonlinear deterministic technique。Computational Economics，9(2)，149-178。  new window
12.Pan, Zuohong、Wang, Xiaodi(1998)。A Stochastic Nonlinear Regression Estimator Using Wavelets。Computational Economics，11，89-102。  new window
13.Zhang, Bai-Ling、Coggins, R.、Jabri, M. A.、Dersch, D.、Flower, B.(2001)。Multiresolution Forecasting for Futures Trading Using Wavelet Decompositions。IEEE Transactions on Neural Networks，12(4)。  new window
14.Zheng, Gonghui、Starck, Jean-Luc、Campbell, Jonathan、Murtagh, Fionn(1999)。The Wavelet Transform for Filtering Financial Data Streams。Journal of Computational Intelligence in Finance，7(3)，18-35。  new window
會議論文
1.Bjorn, V.(1995)。Multiresolution methods for financial time series prediction97。  new window
2.Kozaki, M.、Baba, N.(1992)。An intelligent forecasting system of stock price using neural networks。The International Joint Conference on Neural Networks，371-377。  new window
3.Kimoto, T.、Asakawa, K.(1990)。Stock Market Prediction System With Modular Networks。International Joint Conference on Neural Networks, (IJCNN)-90-Wash。Washington, DC。1-6。  new window
4.Yim, J.(2002)。A Comparison of Neural Networks with Time Series Models for Forecasting Returns on a Stock Market Index25-35。  new window
研究報告
1.Fryzlewicz, P.、Van Bellegen, S.、Von Sachs, R.(2002)。Forecasting non-stationary time series by wavelet process modelling Technical report。Université catholique de Louvain, Institut de Statistique。  new window
2.Ramsey, James B.。Wavelets in Economics and Finance: Past and Future, Economic research report。  new window
學位論文
1.林其鴻(2005)。資料探勘於財務時間序列預測模式之建構--以日經225期貨與現貨指數為例(碩士論文)。輔仁大學。  延伸查詢new window
2.黃綺年(2004)。統計方法與類神經網路應用於國內開放式股票型基金投資績效分類及投資報酬率預測之研究(碩士論文)。國立成功大學，臺南。  延伸查詢new window
3.劉昭賢(1998)。臺灣股市報酬率的實證研究--高階動差與遺傳演算法的應用(碩士論文)。國立中山大學。  延伸查詢new window
4.陳國玄(2004)。人工神經網路與統計方法應用於臺灣上市電子類股價指數預測與分類之研究(碩士論文)。國立成功大學，臺南。  延伸查詢new window
圖書
1.Anderson, J. A.、Rosenfeld, E.(1998)。Neurocomputing: Foundation of research。Cambridge, MA：MIT Press。  new window
2.Rao, R. M.(1998)。Wavelet transforms, introduction to theory and applications。Addison-Wesley。  new window
其他
1.李存修，邱顯比。台灣共同基金績效評比，http://140.112.111.12/。  延伸查詢new window
圖書論文
1.Rumelhart, D. E.、Hinton, G. E.、Williams, R. J.(1986)。Learning Internal Representations by Error Propagation。Parallel Distributed Processing。Cambridge：MIT Press。  new window
 
 
 
 
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