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政大典藏 > College of Commerce > Department of MIS > Theses > Item 140.119/86217

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/86217

Title:	應用神經網路於金融交換與Black-Scholes定價模式之探討與其意義分析 A study and analysis of applying neural networks to the financial swapa and the Black-Scholes pricing model
Authors:	林義評 Lin, Yi-Ping
Contributors:	蔡瑞煌 Tsai, Rai-Hwan 林義評 Lin, Yi-Ping
Keywords:	倒傳遞網路裡解神經網路 Black-Scholes 定價模式金融交換敏感度分析滯留區分析 BP RN Black-Scholes pricing model Financial swaps Sensitivity analysis Dead region analysis
Date:	1997
Issue Date:	2016-04-27 11:13:04 (UTC+8)
Abstract:	本篇論文旨在分析神經網路學習績效，並提出一套學習演算法，結合倒傳遞網路(BP)與理解神經網路(RN)，命名為RNBP，這套學習演算法將與傳統的BP做比較，以兩個不同的財務金融領域的應用，一個是選擇權上Black-Scholes訂價模式的模擬，一個是金融交換上利率的預測。主要績效的評估準則是以學習的效率與模擬、預測的準確度為依據。 The study attempts to analyze the learning performance of neural networks in applications, and propose a new learning procedure for the layered feedforward neural network systems, named KNBP, which binds RN and BP learning algorithms. Two artificial neural networks, BP and KNBP, here are both applied to two financial fields, the simulation of Black-Scholes pricing model for the call options and the midrates forecasting in financial swaps. The explicit performance comparison between the two artificial neural network systems is mainly based on two criteria, which are learning efficiency and forecasting effectiveness.
Description:	碩士國立政治大學資訊管理學系 85356002
Source URI:	http://thesis.lib.nccu.edu.tw/record/#B2002001945
Data Type:	thesis
Appears in Collections:	[Department of MIS] Theses

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