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    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 。
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    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。
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    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。
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    （二）英文部份
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    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.
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    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

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