臺灣博碩士論文加值系統

雖然許多文獻已經證實類神經網路對於財管領域的預測績效良好，但是將之應用在債券殖利率預測的相關研究仍然缺乏，再者，這些研究大多也僅採用倒傳遞類神經網路作為預測工具。倒傳遞類神經網路有學習速度慢、執行時間過長、以及容易陷入局部最小值的困擾，而這些問題的存在，恐怕將會影響預測的準確性，所以，本研究嘗試運用彈性倒傳遞演算法、輻狀基底函數類神經網路、調適性網路模糊推論系統、與倒傳遞類神經網路，針對臺灣十年期指標公債殖利率作一預測，希望藉由比較不同類神經網路模型，推估出預測臺灣公債殖利率上相對較佳之預測模型。
我們的研究結果顯示：(1)四種類神經網路的隱藏層參數與平均絕對誤差百分比之間並無一規律可遵循。(2)對本研究所使用之類神經網路模型與臺灣十年期指標公債資料而言，輸入層節點數以四或五個較佳。(3)較長的訓練資料時間確實有助於提升類神經網路模型的預測準確度。(4)整體預測表現上，以輻狀基底函數類神經網路模型預測結果相對最佳，其次為調適性網路模糊推論系統、彈性倒傳遞演算法，使用最陡坡降法的倒傳遞類神經網路則較差。(5)於臺灣十年期指標公債的預測上，我們建議使用輻狀基底函數類神經網路，其模型參數設定為五個輸入層節點，六個隱藏層中心點個數及一個輸出節點。

Numerous studies have shown that artificial neural networks which have good performance can be one of the very useful tools in financial prediction. However, the field is still short of government bond yield forecasting, and even there are a few published papers in this area, these conventional forecasting models only rely on BPN. Unfortunately, BPN have some potential problems which includes slow training speed, long processing time, and possible local minimum. In practice, it may lead to poor forecasting ability and have incorrect result. The purpose of this research is to provide an in-depth study of effects of four selected models on the performance of neural networks in government bond yields forecasting. Specifically, we examine and compare four neural network models(RPROP, RBFN, ANFIS and BPN) on the forecasting performance of government bond yield. The results indicate that (1)MAPE is shown to be insensitive to the number of hidden layer. (2)The suitable number of input nodes are four or five. (3)Obviously, large training samples do enhance forecasting performance in our study. (4)The predictable performance of RBFN is the best, followed by ANFIS and RPROP, and then the gradient steepest descent method of BPN. (5)Our result reveals that RBFN is a useful predictable approach in government bond yield better than other three neural network models. Recommended parameters for in RBFN are five input nodes, six internal nodes of hidden layer, and the output layer contained a single node with an acceptable degree of accuracy.

目錄
論文指導教授推薦書 i
論文口試委員審定書 ii
明志科技大學學位論文授權書 iii
博碩士論文電子檔案上網授權書 iv
致謝 v
中文摘要 vi
英文摘要 vii
目錄 viii
圖目錄 x
表目錄 xi
第一章 緒論 1
第一節 研究動機與目的 1
第二節 本文架構 5
第二章 文獻回顧 6
第一節 與股票相關文獻 7
第二節 與期貨相關文獻 9
第三節 與匯率相關文獻 10
第四節 與債券相關文獻 12
第三章 研究方法 17
第一節 類神經網路模型 17
第二節 研究流程 23
第三節 資料來源與應用軟體 25
第四節 網路預測績效之評量標準 25
第四章 實證結果 27
第一節 樣本資料 27
第二節 類神經網路之預測結果 29
第三節 各類神經網路模型之比較與分析 37
第五章 結論與建議 41
參考文獻 43
附錄一 48
附錄二 50
圖目錄
圖2.1 神經元模型 6
圖3.1 倒傳遞類神經網路架構圖 17
圖3.2 輻狀基底函數類神經網路架構圖 20
圖3.3 調適性網路模糊推論系統架構圖 22
圖3.4 研究流程圖 24
圖4.1 臺灣十年期指標公債殖利率走勢圖 27
圖4.2 倒傳遞類神經網路在985筆訓練樣本數之預測模型 31
圖4.3 彈性倒傳遞演算法在985筆訓練樣本數之預測模型 33
圖4.4 輻狀基底函數類神經網路在985筆訓練樣本數之預測模型 35
圖4.5 調適性網路模糊推論系統在985筆訓練樣本數之預測模型 37
表目錄
表4.1 類神經網路設定條件 28
表4.2 倒傳遞類神經網路在不同輸入層節點數之預測結果 29
表4.3 倒傳遞類神經網路在不同隱藏層節點數之預測結果 30
表4.4 彈性倒傳遞演算法在不同輸入層節點數之預測結果 31
表4.5 彈性倒傳遞演算法在不同隱藏層節點數之預測結果 32
表4.6 輻狀基底函數類神經網路在不同輸入層節點數之預測結果 33
表4.7 輻狀基底函數類神經網路在不同隱藏層中心點個數之預測結果 34
表4.8 調適性網路模糊推論系統在不同輸入層節點數之預測結果 36
表4.9 調適性網路模糊推論系統在不同隸屬函數之預測結果 36
表4.10 四種類神經網路在不同訓練筆數之預測結果 38
表4.11 倒傳遞類神經網路在新增樣本數與不同輸入層節點數之預測結果 39
表4.12 臺灣十年期指標公債-類神經網路模型預測結果之比較 39
表4.13 臺灣十年期指標公債-類神經網路模型最佳參數設定組合 40

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