A Study of Applying Intelligent Systems to the Warrant Pricing Model, Hedging Scheme, and Investment Strategy
碩士 === 國立雲林科技大學 === 資訊管理研究所 === 87 === Warrant is a type of call option. It provides people with multiple choice in speculate behavior contain arbitrage and hedging. In traditional option pricing model was a complex theory, and it had a lot of limitation and assumption that wait for overcome. This s...
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ndltd-TW-087YUNTE3960102015-10-13T11:50:27Z http://ndltd.ncl.edu.tw/handle/89244375695094213034 A Study of Applying Intelligent Systems to the Warrant Pricing Model, Hedging Scheme, and Investment Strategy 運用智慧型系統在認購權證評價模式、避險及投資策略之研究 Wang Shen-Juh 王勝助 碩士 國立雲林科技大學 資訊管理研究所 87 Warrant is a type of call option. It provides people with multiple choice in speculate behavior contain arbitrage and hedging. In traditional option pricing model was a complex theory, and it had a lot of limitation and assumption that wait for overcome. This study tries to use artificial neural network to build option pricing model for warrant. In Black-Scholes pricing model, there was five variables impact the option price-- use to be the input variable in artificial neural network, both Back-Propagation Network (BPN) and Radial Basis Function Network (RBFN). Base on the difference analysis, it finds out another variable that can improve learning efficiency and effectivity. The reason why using NeuroFuzzy on warrant operation strategy and hedging position is that the hedging coefficient had fuzzy characteristic. There is, however, NeuroFuzzy tech can take a turn for disadvantage in Artificial Neural Network the better. Dong-Her Shih 施東河 1999 學位論文 ; thesis 119 zh-TW |
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碩士 === 國立雲林科技大學 === 資訊管理研究所 === 87 === Warrant is a type of call option. It provides people with multiple choice in speculate behavior contain arbitrage and hedging. In traditional option pricing model was a complex theory, and it had a lot of limitation and assumption that wait for overcome. This study tries to use artificial neural network to build option pricing model for warrant. In Black-Scholes pricing model, there was five variables impact the option price-- use to be the input variable in artificial neural network, both Back-Propagation Network (BPN) and Radial Basis Function Network (RBFN). Base on the difference analysis, it finds out another variable that can improve learning efficiency and effectivity. The reason why using NeuroFuzzy on warrant operation strategy and hedging position is that the hedging coefficient had fuzzy characteristic. There is, however, NeuroFuzzy tech can take a turn for disadvantage in Artificial Neural Network the better.
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author2 |
Dong-Her Shih |
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Dong-Her Shih Wang Shen-Juh 王勝助 |
author |
Wang Shen-Juh 王勝助 |
spellingShingle |
Wang Shen-Juh 王勝助 A Study of Applying Intelligent Systems to the Warrant Pricing Model, Hedging Scheme, and Investment Strategy |
author_sort |
Wang Shen-Juh |
title |
A Study of Applying Intelligent Systems to the Warrant Pricing Model, Hedging Scheme, and Investment Strategy |
title_short |
A Study of Applying Intelligent Systems to the Warrant Pricing Model, Hedging Scheme, and Investment Strategy |
title_full |
A Study of Applying Intelligent Systems to the Warrant Pricing Model, Hedging Scheme, and Investment Strategy |
title_fullStr |
A Study of Applying Intelligent Systems to the Warrant Pricing Model, Hedging Scheme, and Investment Strategy |
title_full_unstemmed |
A Study of Applying Intelligent Systems to the Warrant Pricing Model, Hedging Scheme, and Investment Strategy |
title_sort |
study of applying intelligent systems to the warrant pricing model, hedging scheme, and investment strategy |
publishDate |
1999 |
url |
http://ndltd.ncl.edu.tw/handle/89244375695094213034 |
work_keys_str_mv |
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