English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113303/144284 (79%)
Visitors : 50803141      Online Users : 760
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/87282


    Title: BPN暨RN神經網路與向量誤差修正模型對國內債券價格之預測績效
    Exploring the Relative Abilities of Neural Networks and VECM in Forecasting Taiwan`s Bond Price
    Authors: 紀如龍
    Jih, Ru-Long
    Contributors: 林修葳
    蔡瑞煌
    Lin, Hsiou-Wei
    Tsaih, Rru--Huan
    紀如龍
    Jih, Ru-Long
    Keywords: 公債
    殖利率預測
    神經網路
    RN模型
    BPN模型
    向量誤差修正模型
    Government bond
    Yield to maturity
    Neural network
    RN
    BPN
    VECM
    Date: 1996
    Issue Date: 2016-04-28 11:34:08 (UTC+8)
    Abstract: 本研究計畫探討以RN神經網路模型預測國內債券價格的效度。目前一般用於財務預測的神經網路論著主要為BPN模型，惟BPN模型有其限制，所以本研究計畫將（1）分析比較統計計量模型，BPN神經網路，RN神經網路系統對國內公債價格之預測績效。（2）分析不同時期的預測能力，找出景氣和預測變數的關係，同時將比較各個時期統計計量模型和神經網路模型是否同時有效， 抑或有些有效， 有些無效，以探討各工具是否具有互補性或替代性。並探討預測績效是否受到背後經濟環境的影響。
    This research project empirically investigates the accuracy of Reasoning Neural Networks (RN) in forecasting Taiwan`s bond prices. We explore (1) the relative predictive abilities of Vector Error Correction Model (VECM), which serve as a representative econometric model, Back Propagation Neural Networks (BPN), which is adopted by most current studies in the application of neural networks in finance, and RN, and (2) th3 potential variations in the three models` predictive power in different phases of economic cycle. Specifically, we aim to study if the three models substitute or complementone another. In addition, we explore the extent to which the relativepredictive abilities of the three models varies with underlying macroecomonic factors. The explanatory variables adopted in this study include all potential drives to (real) risk-free rate, expected inflation rate, and riskspremiums.
    Reference: "（一）中文部份
    1、梁志氏、汪義育（1995），我國總體數列因果關係之非恆定計量研究完全修正向量自迴歸實證方法。
    2、婁天威（1993），我國債券市場結構分析與問題探討，臺灣銀行季刊第四十六志第一期，pp.151-202 。
    3、黃國忠（1994），台灣利率期間結構之殖利率曲線計量模型，台灣大學財金所碩士論文。
    4、黃振明，債券報酬預測模式預測績效之實證比較，台灣工業技術學院管理技術所碩士論文。
    5、張維哲（1992），人工神經網路，全欣資訊圖書去司，10月出版。
    6、焦李成（1991），神經網路系統理論，儒林圖書去司，10月出版。
    7、葉怡成 （1993），類神經網路模式應用與實作，儒林圖書公司，1月出版。
    8、蔡瑞煌（1994），The Softening Learning Procedure for The Networks with Multiple Output Nodes，資管評論，第四期，pp.89-93。
    9、蔡瑞煌，（1995），類神經網路概論，三民書局，1月出版。
    10、蔣廷方，（1994），類神經網路股價預測系統，企銀季刊，4月，pp .40-49。
    11、蘇仁，（1994），可轉換公司債評價模式與類神經網路臺灣地區的實證研究，台灣大學財金所碩士論文。
    12、蘇家興，（1993），類神經網路在預測臺灣貨幣市場利率上的應用，交通大學資管所碩士論文。

    （二）英文部份
    1、Baneljee，A. ; Dolado，J. J. ; Galbraith，J.W. ; Hendry，D. F.，（1993），Cointeration，Error Correction and the Econometric Analysis of Nonstationry Data，Published by Oxford University Press Inc.
    2、Bergerson，K. ; Wunsch，D.C.，（1991），A Commodity Trading Model Based on a Neural Networks-Expert System Hybird，Proceedings of the International Joint Conference on Neural Network 1991，pp.289-293.
    3、Elton，E. J. ; Gruber，M. J. ; Bl8ke，C. R.，（1995），Fundamental Economic Variables，Expected Returns，and Bond Fund Performance，Journal of Finance，Sep，pp.1229-1256.
    4、Engle，R. F. ; Granger，C. W. J.，（1987），Co-intergration and Error Correction ： Representation，Estimation and Testing，Econometrica，Vo1.55，pp.251-276.
    5，Engle，R. F. ; Yoo，B. S.，（1987），Forecasting and Testing in Cointegrated Systems，Journal of Econometrics，Vo1.35，May，pp.143-159.
    6，Gmdnitski，G. ; Osburn，L.，（1993），Forecasting S &P500 and Gold Futures Prices： An Application of Neural Networks，Journal of Futures Markets，Vol. 13，NO.6，pp.631-643.
    7，Harvey，A. C.，（1990）The Econometric Analysis of Time Series，Second Edition，Published by Philip Allan.
    8，Jeffrey，E. S. ; Venkatachalam，A. R.，（1995），A Neural Network Approach to Forecasting Model Selection，Information & Management，Vo1.29，Dec，pp.297-303.
    9，Johansen，S. ; Juselius，K.，（1990），Maximun Likelihood Estimation and Inference on Cointegration-With Applications to the Demand for Money，Oxford Bulletin of Economics & Statistics，Vo1.52，May，pp.169-210.
    10，Judge，G. G. ; Hill，R. C. ; Griffiths，W. E. ; Lutkepohl，H. ; Lee，T. C.，（1988）Introduction to The Theoy and Practice of Econometrics，Second Edition，Published by John Wiley & Sons，Inc.
    11，Kimoto，T. ; Asakawa，K.，（1990），Stock Market Prediction System with Modular Network，IJCNN-90-Wash，Vol. 1，pp.1-6.
    12，Krugman，P. R. ; Obstfeld，M.，（1994）International Economics：Theoy and Policy， Third Edition，Published by R.R. Donnelley & Sons Company.
    13，Kryzanowski，L. ; Galler，M. ; Wright，D.W.，（1993），Using Artificial Neural Networks to Pick Stocks，Financial Analysis Journal，Jul-Aug.
    14，Lapedes，A. ; Farber，R.，（1987），Nonelinear Signal Processing using Neural Networks：Prediction and System Modeling，Los Alamos National Laboratory Report，LA-UR-87-2662.
    15，Lee，T. ; White，H.，（1993），Testing for Neglected Nonlinearity in Time Series 1vfodels，Journal of Econometrics，April，pp.269-290.
    16，Michael，G. B. ; Stephen，A. L.，（1992），The Treasury Yield Curve as a Cointegrated System，Journal of Financial and Quantitative Analysis，Vol.27，NO.3，Sep，449-464.
    17，Phillips，P. C. B. ; Perron，P.，（1988），Testing for a Unit Root in Time Series Regression，Biometrica，Vo1.75，pp.335-346.
    18，Rumelhatt，D. E. ; McClelland，1. L.，（1986），Parallel Distributed Processing，Vol. 1，Published by The Massachusetts Institute of Technology.
    19，Schoneburg，E.，（1990），Stock Price Prediction using Neural Networks：A Project Report，Neuralcomputing 2，pp.17-27.
    20，Swanson N. ; White H.，（1995），A Model-Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks，Joul11al of Business and Economic Statistics，July，pp.265-275.
    21，Tsaih，R.，（1995），The Reasoning Neural Network，Annals of Mathematics and Artificial Intelligence，accepted.
    22，Tsaih，R.，（1993），The Softening Learning Procedure，Mathematical and Computer Modelling，Vol.18，No.8，pp.61-64."
    Description: 碩士
    國立政治大學
    國際經營與貿易學系
    83351022
    Source URI: http://thesis.lib.nccu.edu.tw/record/#B2002002748
    Data Type: thesis
    Appears in Collections:[國際經營與貿易學系 ] 學位論文

    Files in This Item:

    File SizeFormat
    index.html0KbHTML2791View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback